WorldWideScience

Sample records for video event detection

  1. Detection of goal events in soccer videos

    Science.gov (United States)

    Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

    2005-01-01

    In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

  2. Online Detection of Abnormal Events in Video Streams

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2013-01-01

    an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique.

  3. Complex Event Detection via Multi Source Video Attributes (Open Access)

    Science.gov (United States)

    2013-10-03

    Complex Event Detection via Multi-Source Video Attributes Zhigang Ma† Yi Yang‡ Zhongwen Xu‡§ Shuicheng Yan Nicu Sebe† Alexander G. Hauptmann...under its International Research Centre @ Singapore Fund- ing Initiative and administered by the IDM Programme Of- fice, and the Intelligence Advanced

  4. TACKLING EVENT DETECTION IN THE CONTEXT OF VIDEO SURVEILLANCE

    Directory of Open Access Journals (Sweden)

    Raducu DUMITRESCU

    2011-11-01

    Full Text Available In this paper we address the problem of event detection in the context of video surveillance systems. First we deal with background extraction. Three methods are being tested, namely: frame differencing, running average and an estimate of median filtering technique. This provides information about changing contents. Further, we use this information to address human presence detection in the scene. This is carried out thought a contour-based approach. Contours are extracted from moving regions and parameterized. Human silhouettes show particular signatures of these parameters. Experimental results prove the potential of this approach to event detection. However, these are our first preliminary results to this application.

  5. Event detection for car park entries by video-surveillance

    Science.gov (United States)

    Coquin, Didier; Tailland, Johan; Cintract, Michel

    2007-10-01

    Intelligent surveillance has become an important research issue due to the high cost and low efficiency of human supervisors, and machine intelligence is required to provide a solution for automated event detection. In this paper we describe a real-time system that has been used for detecting car park entries, using an adaptive background learning algorithm and two indicators representing activity and identity to overcome the difficulty of tracking objects.

  6. Detection of Visual Events in Underwater Video Using a Neuromorphic Saliency-based Attention System

    Science.gov (United States)

    Edgington, D. R.; Walther, D.; Cline, D. E.; Sherlock, R.; Salamy, K. A.; Wilson, A.; Koch, C.

    2003-12-01

    The Monterey Bay Aquarium Research Institute (MBARI) uses high-resolution video equipment on remotely operated vehicles (ROV) to obtain quantitative data on the distribution and abundance of oceanic animals. High-quality video data supplants the traditional approach of assessing the kinds and numbers of animals in the oceanic water column through towing collection nets behind ships. Tow nets are limited in spatial resolution, and often destroy abundant gelatinous animals resulting in species undersampling. Video camera-based quantitative video transects (QVT) are taken through the ocean midwater, from 50m to 4000m, and provide high-resolution data at the scale of the individual animals and their natural aggregation patterns. However, the current manual method of analyzing QVT video by trained scientists is labor intensive and poses a serious limitation to the amount of information that can be analyzed from ROV dives. Presented here is an automated system for detecting marine animals (events) visible in the videos. Automated detection is difficult due to the low contrast of many translucent animals and due to debris ("marine snow") cluttering the scene. Video frames are processed with an artificial intelligence attention selection algorithm that has proven a robust means of target detection in a variety of natural terrestrial scenes. The candidate locations identified by the attention selection module are tracked across video frames using linear Kalman filters. Typically, the occurrence of visible animals in the video footage is sparse in space and time. A notion of "boring" video frames is developed by detecting whether or not there is an interesting candidate object for an animal present in a particular sequence of underwater video -- video frames that do not contain any "interesting" events. If objects can be tracked successfully over several frames, they are stored as potentially "interesting" events. Based on low-level properties, interesting events are

  7. A Macro-Observation Scheme for Abnormal Event Detection in Daily-Life Video Sequences

    Directory of Open Access Journals (Sweden)

    Chiu Wei-Yao

    2010-01-01

    Full Text Available Abstract We propose a macro-observation scheme for abnormal event detection in daily life. The proposed macro-observation representation records the time-space energy of motions of all moving objects in a scene without segmenting individual object parts. The energy history of each pixel in the scene is instantly updated with exponential weights without explicitly specifying the duration of each activity. Since possible activities in daily life are numerous and distinct from each other and not all abnormal events can be foreseen, images from a video sequence that spans sufficient repetition of normal day-to-day activities are first randomly sampled. A constrained clustering model is proposed to partition the sampled images into groups. The new observed event that has distinct distance from any of the cluster centroids is then classified as an anomaly. The proposed method has been evaluated in daily work of a laboratory and BEHAVE benchmark dataset. The experimental results reveal that it can well detect abnormal events such as burglary and fighting as long as they last for a sufficient duration of time. The proposed method can be used as a support system for the scene that requires full time monitoring personnel.

  8. Deception Detection in Videos

    OpenAIRE

    Wu, Zhe; Singh, Bharat; Davis, Larry S.; Subrahmanian, V. S.

    2017-01-01

    We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely ...

  9. Hierarchical Context Modeling for Video Event Recognition.

    Science.gov (United States)

    Wang, Xiaoyang; Ji, Qiang

    2016-10-11

    Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

  10. Robust Adaptable Video Copy Detection

    DEFF Research Database (Denmark)

    Assent, Ira; Kremer, Hardy

    2009-01-01

    in contrast). Our query processing combines filtering and indexing structures for efficient multistep computation of video copies under this model. We show that our model successfully identifies altered video copies and does so more reliably than existing models.......Video copy detection should be capable of identifying video copies subject to alterations e.g. in video contrast or frame rates. We propose a video copy detection scheme that allows for adaptable detection of videos that are altered temporally (e.g. frame rate change) and/or visually (e.g. change...

  11. Video2vec Embeddings Recognize Events When Examples Are Scarce.

    Science.gov (United States)

    Habibian, Amirhossein; Mensink, Thomas; Snoek, Cees G M

    2017-10-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call Video2vec, the correlations between the words are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We show how learning the Video2vec embedding using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose an event specific variant of Video2vec to learn a more accurate representation for the words, which are indicative of the event, by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of Video2vec over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition. By its ability to improve predictability of present day audio-visual video features, while at the same time maximizing their semantic descriptiveness, Video2vec leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.

  12. Hierarchical event selection for video storyboards with a case study on snooker video visualization.

    Science.gov (United States)

    Parry, Matthew L; Legg, Philip A; Chung, David H S; Griffiths, Iwan W; Chen, Min

    2011-12-01

    Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas. © 2010 IEEE

  13. Joint Attributes and Event Analysis for Multimedia Event Detection.

    Science.gov (United States)

    Ma, Zhigang; Chang, Xiaojun; Xu, Zhongwen; Sebe, Nicu; Hauptmann, Alexander G

    2017-06-15

    Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED.

  14. Features for detecting smoke in laparoscopic videos

    Directory of Open Access Journals (Sweden)

    Jalal Nour Aldeen

    2017-09-01

    Full Text Available Video-based smoke detection in laparoscopic surgery has different potential applications, such as the automatic addressing of surgical events associated with the electrocauterization task and the development of automatic smoke removal. In the literature, video-based smoke detection has been studied widely for fire surveillance systems. Nevertheless, the proposed methods are insufficient for smoke detection in laparoscopic videos because they often depend on assumptions which rarely hold in laparoscopic surgery such as static camera. In this paper, ten visual features based on motion, texture and colour of smoke are proposed and evaluated for smoke detection in laparoscopic videos. These features are RGB channels, energy-based feature, texture features based on gray level co-occurrence matrix (GLCM, HSV colour space feature, features based on the detection of moving regions using optical flow and the smoke colour in HSV colour space. These features were tested on four laparoscopic cholecystectomy videos. Experimental observations show that each feature can provide valuable information in performing the smoke detection task. However, each feature has weaknesses to detect the presence of smoke in some cases. By combining all proposed features smoke with high and even low density can be identified robustly and the classification accuracy increases significantly.

  15. Conceptlets: Selective Semantics for Classifying Video Events

    NARCIS (Netherlands)

    Mazloom, M.; Gavves, E.; Snoek, C.G.M.

    2014-01-01

    An emerging trend in video event classification is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available detectors, we propose in this paper an algorithm that learns from examples what concepts in a bank are

  16. Learning and Parsing Video Events with Goal and Intent Prediction

    Science.gov (United States)

    2012-03-19

    including office, lab, hallway, cor- ridor and near vending machines . Figure 14 shows some screen-shots of the videos. The training video total lasts...most of the ambiguities can be removed by the event context in the top-down bottom-up inference, we will show this in the experiment section. 5 Figure 5...events, and remove the ambiguities in the detection of atomic actions by the event context. The energy of PG is E(PG | I∧) = p(K) K∑ k=1 (ε(pgk | I

  17. Video library for video imaging detection at intersection stop lines.

    Science.gov (United States)

    2010-04-01

    The objective of this activity was to record video that could be used for controlled : evaluation of video image vehicle detection system (VIVDS) products and software upgrades to : existing products based on a list of conditions that might be diffic...

  18. VideoStory Embeddings Recognize Events when Examples are Scarce

    OpenAIRE

    Habibian, Amirhossein; Mensink, Thomas; Snoek, Cees G. M.

    2015-01-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call VideoStory, the correlati...

  19. Video2vec Embeddings Recognize Events when Examples are Scarce

    OpenAIRE

    Habibian, A.; Mensink, T.; Snoek, C.G.M.

    2017-01-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call Video2vec, the correlatio...

  20. Detection of anomalous events

    Science.gov (United States)

    Ferragut, Erik M.; Laska, Jason A.; Bridges, Robert A.

    2016-06-07

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

  1. Indexing Motion Detection Data for Surveillance Video

    DEFF Research Database (Denmark)

    Vind, Søren Juhl; Bille, Philip; Gørtz, Inge Li

    2014-01-01

    We show how to compactly index video data to support fast motion detection queries. A query specifies a time interval T, a area A in the video and two thresholds v and p. The answer to a query is a list of timestamps in T where ≥ p% of A has changed by ≥ v values. Our results show that by building...... a small index, we can support queries with a speedup of two to three orders of magnitude compared to motion detection without an index. For high resolution video, the index size is about 20% of the compressed video size....

  2. Recommendations for Recognizing Video Events by Concept Vocabularies

    NARCIS (Netherlands)

    Habibian, A.; Snoek, C.G.M.

    2014-01-01

    Representing videos using vocabularies composed of concept detectors appears promising for generic event recognition. While many have recently shown the benefits of concept vocabularies for recognition, studying the characteristics of a universal concept vocabulary suited for representing events is

  3. Semantic reasoning in zero example video event retrieval

    NARCIS (Netherlands)

    Boer, M.H.T. de; Lu, Y.J.; Zhang, H.; Schutte, K.; Ngo, C.W.; Kraaij, W.

    2017-01-01

    Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneficial for the retrieval of high-level events using visual examples,

  4. A video event trigger for high frame rate, high resolution video technology

    Science.gov (United States)

    Williams, Glenn L.

    1991-12-01

    When video replaces film the digitized video data accumulates very rapidly, leading to a difficult and costly data storage problem. One solution exists for cases when the video images represent continuously repetitive 'static scenes' containing negligible activity, occasionally interrupted by short events of interest. Minutes or hours of redundant video frames can be ignored, and not stored, until activity begins. A new, highly parallel digital state machine generates a digital trigger signal at the onset of a video event. High capacity random access memory storage coupled with newly available fuzzy logic devices permits the monitoring of a video image stream for long term or short term changes caused by spatial translation, dilation, appearance, disappearance, or color change in a video object. Pretrigger and post-trigger storage techniques are then adaptable for archiving the digital stream from only the significant video images.

  5. Event detection intelligent camera development

    International Nuclear Information System (INIS)

    Szappanos, A.; Kocsis, G.; Molnar, A.; Sarkozi, J.; Zoletnik, S.

    2008-01-01

    A new camera system 'event detection intelligent camera' (EDICAM) is being developed for the video diagnostics of W-7X stellarator, which consists of 10 distinct and standalone measurement channels each holding a camera. Different operation modes will be implemented for continuous and for triggered readout as well. Hardware level trigger signals will be generated from real time image processing algorithms optimized for digital signal processor (DSP) and field programmable gate array (FPGA) architectures. At full resolution a camera sends 12 bit sampled 1280 x 1024 pixels with 444 fps which means 1.43 Terabyte over half an hour. To analyse such a huge amount of data is time consuming and has a high computational complexity. We plan to overcome this problem by EDICAM's preprocessing concepts. EDICAM camera system integrates all the advantages of CMOS sensor chip technology and fast network connections. EDICAM is built up from three different modules with two interfaces. A sensor module (SM) with reduced hardware and functional elements to reach a small and compact size and robust action in harmful environment as well. An image processing and control unit (IPCU) module handles the entire user predefined events and runs image processing algorithms to generate trigger signals. Finally a 10 Gigabit Ethernet compatible image readout card functions as the network interface for the PC. In this contribution all the concepts of EDICAM and the functions of the distinct modules are described

  6. Quantitative method for the detection and localization of quantum-limited events from radionuclides in cells and tissue sections by computer-enhanced video microscopy

    International Nuclear Information System (INIS)

    Pressman, N.J.; Frost, J.K.; Gupta, P.K.; Showers, R.L.; Gill, G.W.; Cook, D.L.; Frost, J.K. Jr.; Traub, R.K.

    1987-01-01

    Cellular dynamics often involve extremely low concentrations of biologically active substances, which can be radiolabeled and detected, localized and quantitated by autoradiography. The latter may require exposures from a few days to many months. The objective of this research was to demonstrate the feasibility of reducing this long period of data collection by one to two orders of magnitude, while maintaining or improving the spatial resolution and localization in tissues and the quantitative characteristics inherent in autoradiography. A mathematical model describing the complete system was generated using energy partition calculations to estimate photon production via scintillant per H3 beta particle emission and to estimate the subsequent photon capture based upon imaging system parameters and microscope geometry. Calculations showed that, typically, a single tritium beta particle produces a maximum of 5.8 X 10(3) photons. A photon-limited camera and microscope imaging system were selected and optimized in conjunction with a specially developed physical scintillation model. Results showed that the number of detected photoevents increases monotonically with both signal integration time and, independently, with the concentration of the radionuclide. Consequently, this work demonstrates that video microscopy imaging methods can spatially and temporally quantify very low concentrations of radiolabeled substances and can reduce data acquisition times

  7. VLSI-based video event triggering for image data compression

    Science.gov (United States)

    Williams, Glenn L.

    1994-02-01

    Long-duration, on-orbit microgravity experiments require a combination of high resolution and high frame rate video data acquisition. The digitized high-rate video stream presents a difficult data storage problem. Data produced at rates of several hundred million bytes per second may require a total mission video data storage requirement exceeding one terabyte. A NASA-designed, VLSI-based, highly parallel digital state machine generates a digital trigger signal at the onset of a video event. High capacity random access memory storage coupled with newly available fuzzy logic devices permits the monitoring of a video image stream for long term (DC-like) or short term (AC-like) changes caused by spatial translation, dilation, appearance, disappearance, or color change in a video object. Pre-trigger and post-trigger storage techniques are then adaptable to archiving only the significant video images.

  8. Recommendations for recognizing video events by concept vocabularies

    Science.gov (United States)

    2014-06-01

    represents a video in terms of low-level audiovisual features [16,38,50,35,15,19,37]. In general, these methods first extract from the video various types of...interpretable, but is also reported to outperform the state-of-the-art low-level audiovisual features in recognizing events [31,33]. Rather than training...concept detector accuracy. As a consequence, the vocabulary concepts do not necessarily have a semantic interpreta- tion needed to explain the video content

  9. Video2vec Embeddings Recognize Events when Examples are Scarce

    NARCIS (Netherlands)

    Habibian, A.; Mensink, T.; Snoek, C.G.M.

    2017-01-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire

  10. Video motion detection for physical security applications

    International Nuclear Information System (INIS)

    Matter, J.C.

    1990-01-01

    Physical security specialists have been attracted to the concept of video motion detection for several years. Claimed potential advantages included additional benefit from existing video surveillance systems, automatic detection, improved performance compared to human observers, and cost-effectiveness. In recent years, significant advances in image-processing dedicated hardware and image analysis algorithms and software have accelerated the successful application of video motion detection systems to a variety of physical security applications. Early video motion detectors (VMDs) were useful for interior applications of volumetric sensing. Success depended on having a relatively well-controlled environment. Attempts to use these systems outdoors frequently resulted in an unacceptable number of nuisance alarms. Currently, Sandia National Laboratories (SNL) is developing several advanced systems that employ image-processing techniques for a broader set of safeguards and security applications. The Target Cueing and Tracking System (TCATS), the Video Imaging System for Detection, Tracking, and Assessment (VISDTA), the Linear Infrared Scanning Array (LISA); the Mobile Intrusion Detection and Assessment System (MIDAS), and the Visual Artificially Intelligent Surveillance (VAIS) systems are described briefly

  11. Video change detection for fixed wing UAVs

    Science.gov (United States)

    Bartelsen, Jan; Müller, Thomas; Ring, Jochen; Mück, Klaus; Brüstle, Stefan; Erdnüß, Bastian; Lutz, Bastian; Herbst, Theresa

    2017-10-01

    In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the

  12. Moving Shadow Detection in Video Using Cepstrum

    Directory of Open Access Journals (Sweden)

    Fuat Cogun

    2013-01-01

    Full Text Available Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum-based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made.

  13. GIF Video Sentiment Detection Using Semantic Sequence

    Directory of Open Access Journals (Sweden)

    Dazhen Lin

    2017-01-01

    Full Text Available With the development of social media, an increasing number of people use short videos in social media applications to express their opinions and sentiments. However, sentiment detection of short videos is a very challenging task because of the semantic gap problem and sequence based sentiment understanding problem. In this context, we propose a SentiPair Sequence based GIF video sentiment detection approach with two contributions. First, we propose a Synset Forest method to extract sentiment related semantic concepts from WordNet to build a robust SentiPair label set. This approach considers the semantic gap between label words and selects a robust label subset which is related to sentiment. Secondly, we propose a SentiPair Sequence based GIF video sentiment detection approach that learns the semantic sequence to understand the sentiment from GIF videos. Our experiment results on GSO-2016 (GIF Sentiment Ontology data show that our approach not only outperforms four state-of-the-art classification methods but also shows better performance than the state-of-the-art middle level sentiment ontology features, Adjective Noun Pairs (ANPs.

  14. Defect detection on videos using neural network

    Directory of Open Access Journals (Sweden)

    Sizyakin Roman

    2017-01-01

    Full Text Available In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.

  15. Rocchio-based relevance feedback in video event retrieval

    NARCIS (Netherlands)

    Pingen, G.L.J.; de Boer, M.H.T.; Aly, Robin; Amsaleg, Laurent; Guðmundsson, Gylfi Þór; Gurrin, Cathal; Jónsson, Björn Þór; Satoh, Shin’ichi

    This paper investigates methods for user and pseudo relevance feedback in video event retrieval. Existing feedback methods achieve strong performance but adjust the ranking based on few individual examples. We propose a relevance feedback algorithm (ARF) derived from the Rocchio method, which is a

  16. Automatic blood detection in capsule endoscopy video

    Czech Academy of Sciences Publication Activity Database

    Novozámský, Adam; Flusser, Jan; Tachecí, I.; Sulík, L.; Bureš, J.; Krejcar, O.

    2016-01-01

    Roč. 21, č. 12 (2016), s. 1-8, č. článku 126007. ISSN 1083-3668 R&D Projects: GA ČR GA15-16928S Institutional support: RVO:67985556 Keywords : Automatic blood detection * capsule endoscopy video Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.530, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/flusser-0466936.pdf

  17. Violent Interaction Detection in Video Based on Deep Learning

    Science.gov (United States)

    Zhou, Peipei; Ding, Qinghai; Luo, Haibo; Hou, Xinglin

    2017-06-01

    Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features such as statistic features between motion regions, leading to a poor adaptability to another dataset. En lightened by the development of convolutional networks on common activity recognition, we construct a FightNet to represent the complicated visual violence interaction. In this paper, a new input modality, image acceleration field is proposed to better extract the motion attributes. Firstly, each video is framed as RGB images. Secondly, optical flow field is computed using the consecutive frames and acceleration field is obtained according to the optical flow field. Thirdly, the FightNet is trained with three kinds of input modalities, i.e., RGB images for spatial networks, optical flow images and acceleration images for temporal networks. By fusing results from different inputs, we conclude whether a video tells a violent event or not. To provide researchers a common ground for comparison, we have collected a violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. By comparison with other algorithms, experimental results demonstrate that the proposed model for violent interaction detection shows higher accuracy and better robustness.

  18. User-based key frame detection in social web video

    OpenAIRE

    Chorianopoulos, Konstantinos

    2012-01-01

    Video search results and suggested videos on web sites are represented with a video thumbnail, which is manually selected by the video up-loader among three randomly generated ones (e.g., YouTube). In contrast, we present a grounded user-based approach for automatically detecting interesting key-frames within a video through aggregated users' replay interactions with the video player. Previous research has focused on content-based systems that have the benefit of analyzing a video without use...

  19. Fast compressed domain motion detection in H.264 video streams for video surveillance applications

    DEFF Research Database (Denmark)

    Szczerba, Krzysztof; Forchhammer, Søren; Støttrup-Andersen, Jesper

    2009-01-01

    This paper presents a novel approach to fast motion detection in H.264/MPEG-4 advanced video coding (AVC) compressed video streams for IP video surveillance systems. The goal is to develop algorithms which may be useful in a real-life industrial perspective by facilitating the processing of large...... on motion vectors embedded in the video stream without requiring a full decoding and reconstruction of video frames. To improve the robustness to noise, a confidence measure based on temporal and spatial clues is introduced to increase the probability of correct detection. The algorithm was tested on indoor...

  20. Shadow Detection Based on Regions of Light Sources for Object Extraction in Nighttime Video

    Directory of Open Access Journals (Sweden)

    Gil-beom Lee

    2017-03-01

    Full Text Available Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used intensity, color, texture, and geometric information to perform shadow detection in daytime video, but these methods lack the capability of removing shadows in nighttime video. In this paper, a novel shadow detection algorithm for nighttime video is proposed; this algorithm partitions each foreground object based on the object’s vertical histogram and screens out shadow objects by validating their orientations heading toward regions of light sources. From the experimental results, it can be seen that the proposed algorithm shows more than 93.8% shadow removal and 89.9% object extraction rates for nighttime video sequences, and the algorithm outperforms conventional shadow removal algorithms designed for daytime videos.

  1. Performance evaluation software moving object detection and tracking in videos

    CERN Document Server

    Karasulu, Bahadir

    2013-01-01

    Performance Evaluation Software: Moving Object Detection and Tracking in Videos introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS),

  2. EDICAM (Event Detection Intelligent Camera)

    Energy Technology Data Exchange (ETDEWEB)

    Zoletnik, S. [Wigner RCP RMI, EURATOM Association, Budapest (Hungary); Szabolics, T., E-mail: szabolics.tamas@wigner.mta.hu [Wigner RCP RMI, EURATOM Association, Budapest (Hungary); Kocsis, G.; Szepesi, T.; Dunai, D. [Wigner RCP RMI, EURATOM Association, Budapest (Hungary)

    2013-10-15

    Highlights: ► We present EDICAM's hardware modules. ► We present EDICAM's main design concepts. ► This paper will describe EDICAM firmware architecture. ► Operation principles description. ► Further developments. -- Abstract: A new type of fast framing camera has been developed for fusion applications by the Wigner Research Centre for Physics during the last few years. A new concept was designed for intelligent event driven imaging which is capable of focusing image readout to Regions of Interests (ROIs) where and when predefined events occur. At present these events mean intensity changes and external triggers but in the future more sophisticated methods might also be defined. The camera provides 444 Hz frame rate at full resolution of 1280 × 1024 pixels, but monitoring of smaller ROIs can be done in the 1–116 kHz range even during exposure of the full image. Keeping space limitations and the harsh environment in mind the camera is divided into a small Sensor Module and a processing card interconnected by a fast 10 Gbit optical link. This camera hardware has been used for passive monitoring of the plasma in different devices for example at ASDEX Upgrade and COMPASS with the first version of its firmware. The new firmware and software package is now available and ready for testing the new event processing features. This paper will present the operation principle and features of the Event Detection Intelligent Camera (EDICAM). The device is intended to be the central element in the 10-camera monitoring system of the Wendelstein 7-X stellarator.

  3. Automatic polyp detection in colonoscopy videos

    Science.gov (United States)

    Yuan, Zijie; IzadyYazdanabadi, Mohammadhassan; Mokkapati, Divya; Panvalkar, Rujuta; Shin, Jae Y.; Tajbakhsh, Nima; Gurudu, Suryakanth; Liang, Jianming

    2017-02-01

    Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

  4. Detection of Abnormal Events via Optical Flow Feature Analysis

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2015-03-01

    Full Text Available In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm.

  5. Detection of Abnormal Events via Optical Flow Feature Analysis

    Science.gov (United States)

    Wang, Tian; Snoussi, Hichem

    2015-01-01

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

  6. Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

    Directory of Open Access Journals (Sweden)

    Shaonian Huang

    2018-01-01

    Full Text Available Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.

  7. Methods and Algorithms for Detecting Objects in Video Files

    Directory of Open Access Journals (Sweden)

    Nguyen The Cuong

    2018-01-01

    Full Text Available Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.

  8. Abnormal global and local event detection in compressive sensing domain

    Science.gov (United States)

    Wang, Tian; Qiao, Meina; Chen, Jie; Wang, Chuanyun; Zhang, Wenjia; Snoussi, Hichem

    2018-05-01

    Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.

  9. Abnormal global and local event detection in compressive sensing domain

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2018-05-01

    Full Text Available Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.

  10. Real-time Multiple Abnormality Detection in Video Data

    DEFF Research Database (Denmark)

    Have, Simon Hartmann; Ren, Huamin; Moeslund, Thomas B.

    2013-01-01

    Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which...... are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets...... show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods....

  11. How does structured sparsity work in abnormal event detection?

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

    the training, which is the due to the fact that abnormal videos are limited or even unavailable in advance in most video surveillance applications. As a result, there could be only one label in the training data which hampers supervised learning; 2) Even though there are multiple types of normal behaviors, how...... many normal patterns lie in the whole surveillance data is still unknown. This is because there is huge amount of video surveillance data and only a small proportion is used in algorithm learning, consequently, the normal patterns in the training data could be incomplete. As a result, any sparse...... structure learned from the training data could have a high bias and ruin the precision of abnormal event detection. Therefore, we in the paper propose an algorithm to solve the abnormality detection problem by sparse representation, in which local structured sparsity is preserved in coefficients. To better...

  12. Human features detection in video surveillance

    OpenAIRE

    Barbosa, Patrícia Margarida Silva de Castro Neves

    2016-01-01

    Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores Human activity recognition algorithms have been studied actively from decades using a sequence of 2D and 3D images from a video surveillance. This new surveillance solutions and the areas of image processing and analysis have been receiving special attention and interest from the scientific community. Thus, it became possible to witness the appearance of new video compression techniques, the tr...

  13. Automatic video surveillance of outdoor scenes using track before detect

    DEFF Research Database (Denmark)

    Hansen, Morten; Sørensen, Helge Bjarup Dissing; Birkemark, Christian M.

    2005-01-01

    This paper concerns automatic video surveillance of outdoor scenes using a single camera. The first step in automatic interpretation of the video stream is activity detection based on background subtraction. Usually, this process will generate a large number of false alarms in outdoor scenes due...

  14. Extended image differencing for change detection in UAV video mosaics

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang; Schumann, Arne

    2014-03-01

    Change detection is one of the most important tasks when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. We address changes of short time scale, i.e. the observations are taken in time distances from several minutes up to a few hours. Each observation is a short video sequence acquired by the UAV in near-nadir view and the relevant changes are, e.g., recently parked or moved vehicles. In this paper we extend our previous approach of image differencing for single video frames to video mosaics. A precise image-to-image registration combined with a robust matching approach is needed to stitch the video frames to a mosaic. Additionally, this matching algorithm is applied to mosaic pairs in order to align them to a common geometry. The resulting registered video mosaic pairs are the input of the change detection procedure based on extended image differencing. A change mask is generated by an adaptive threshold applied to a linear combination of difference images of intensity and gradient magnitude. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed size of shadows, and compression or transmission artifacts. The special effects of video mosaicking such as geometric distortions and artifacts at moving objects have to be considered, too. In our experiments we analyze the influence of these effects on the change detection results by considering several scenes. The results show that for video mosaics this task is more difficult than for single video frames. Therefore, we extended the image registration by estimating an elastic transformation using a thin plate spline approach. The results for mosaics are comparable to that of single video frames and are useful for interactive image exploitation due to a larger scene coverage.

  15. A simple strategy for fall events detection

    KAUST Repository

    Harrou, Fouzi

    2017-01-20

    The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.

  16. Evaluation of video detection systems, volume 1 : effects of configuration changes in the performance of video detection systems.

    Science.gov (United States)

    2009-10-01

    The effects of modifying the configuration of three video detection (VD) systems (Iteris, Autoscope, and Peek) : are evaluated in daytime and nighttime conditions. Four types of errors were used: false, missed, stuck-on, and : dropped calls. The thre...

  17. Identification of canonical neural events during continuous gameplay of an 8-bit style video game.

    Science.gov (United States)

    Cavanagh, James F; Castellanos, Joel

    2016-06-01

    Cognitive neuroscience suffers from a unique and pervasive problem of generalizability. Since neural findings are often interpreted in the context of a specific manipulation during a carefully controlled task, it is hard to transfer knowledge from one task to another. In this report we address problems of generalizability with two methodological advancements. First, we aimed to transcend status quo experimental procedures with a continuous, engaging task environment. To this end, we created a novel 8-bit style continuous space shooter video game that elicits a multitude of goal-oriented events, such as crashing into a wall or blowing up an enemy with a missile. Second, we aimed to objectively define the psychological significance of these events. To achieve this aim, we used pattern classification of EEG data to derive predictive weights from carefully controlled pre-game exemplar events (oddball target detection and gambling wins and losses) and transferred those weights to EEG activities during video game events. All major goal-oriented events (crashes into the wall, crashes into an enemy, missile hit on an enemy) had a significant between-task transfer bias towards oddball target weights in the time range of the canonical P3, indicating the presence of similar salience detection processes. Missile hits on an enemy were specifically identified as gambling wins, confirming the hypothesis that this goal-oriented event was appetitive. These findings suggest that it is possible to identify the contribution of canonical neural activities during otherwise ambiguous and uncontrolled task performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Semblance for microseismic event detection

    Czech Academy of Sciences Publication Activity Database

    Staněk, František; Anikiev, D.; Valenta, Jan; Eisner, Leo

    2015-01-01

    Roč. 201, č. 3 (2015), s. 1362-1369 ISSN 0956-540X R&D Projects: GA ČR GAP210/12/2451 Institutional support: RVO:67985891 Keywords : microseismic event * microseismic monitoring * source mechanisms Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 2.484, year: 2015

  19. Filtering the Unknown: Speech Activity Detection in Heterogeneous Video Collections

    NARCIS (Netherlands)

    Huijbregts, M.A.H.; Wooters, Chuck; Ordelman, Roeland J.F.

    2007-01-01

    In this paper we discuss the speech activity detection system that we used for detecting speech regions in the Dutch TRECVID video collection. The system is designed to filter non-speech like music or sound effects out of the signal without the use of predefined non-speech models. Because the system

  20. A novel visual saliency detection method for infrared video sequences

    Science.gov (United States)

    Wang, Xin; Zhang, Yuzhen; Ning, Chen

    2017-12-01

    Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

  1. Detection of neutral current events

    International Nuclear Information System (INIS)

    Innocenti, P.G.

    1979-01-01

    The topics investigated in the course of the study can be broadly divided into three classes: (i) Inclusive measurements of the scattered electron for the determination of structure functions, scaling violations, deltaL/deltaT and weak interaction effects. (ii) Exclusive measurements of the current jet (momentum, energy, particle composition) for the study of fragmentation functions, for search of new particles, new quarks and QCD effects in the jet. (iii) Search for heavy leptons by detection and identification of their decay products. (orig.)

  2. Generalized Detectability for Discrete Event Systems

    Science.gov (United States)

    Shu, Shaolong; Lin, Feng

    2011-01-01

    In our previous work, we investigated detectability of discrete event systems, which is defined as the ability to determine the current and subsequent states of a system based on observation. For different applications, we defined four types of detectabilities: (weak) detectability, strong detectability, (weak) periodic detectability, and strong periodic detectability. In this paper, we extend our results in three aspects. (1) We extend detectability from deterministic systems to nondeterministic systems. Such a generalization is necessary because there are many systems that need to be modeled as nondeterministic discrete event systems. (2) We develop polynomial algorithms to check strong detectability. The previous algorithms are based on observer whose construction is of exponential complexity, while the new algorithms are based on a new automaton called detector. (3) We extend detectability to D-detectability. While detectability requires determining the exact state of a system, D-detectability relaxes this requirement by asking only to distinguish certain pairs of states. With these extensions, the theory on detectability of discrete event systems becomes more applicable in solving many practical problems. PMID:21691432

  3. Heterogeneous CPU-GPU moving targets detection for UAV video

    Science.gov (United States)

    Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan

    2017-07-01

    Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.

  4. Exterior field evaluation of new generation video motion detection systems

    International Nuclear Information System (INIS)

    Malone, T.P.

    1988-01-01

    Recent advancements in video motion detection (VMD) system design and technology have resulted in several new commercial VMD systems. Considerable interest in the new VMD systems has been generated because the systems are advertised to work effectively in exterior applications. Previous VMD systems, when used in an exterior environment, tended to have very high nuisance alarm rates due to weather conditions, wildlife activity and lighting variations. The new VMD systems advertise more advanced processing of the incoming video signal which is aimed at rejecting exterior environmental nuisance alarm sources while maintaining a high detection capability. This paper discusses the results of field testing, in an exterior environment, of two new VMD systems

  5. Dangerous gas detection based on infrared video

    Science.gov (United States)

    Ding, Kang; Hong, Hanyu; Huang, Likun

    2018-03-01

    As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

  6. Detecting fire in video stream using statistical analysis

    Directory of Open Access Journals (Sweden)

    Koplík Karel

    2017-01-01

    Full Text Available The real time fire detection in video stream is one of the most interesting problems in computer vision. In fact, in most cases it would be nice to have fire detection algorithm implemented in usual industrial cameras and/or to have possibility to replace standard industrial cameras with one implementing the fire detection algorithm. In this paper, we present new algorithm for detecting fire in video. The algorithm is based on tracking suspicious regions in time with statistical analysis of their trajectory. False alarms are minimized by combining multiple detection criteria: pixel brightness, trajectories of suspicious regions for evaluating characteristic fire flickering and persistence of alarm state in sequence of frames. The resulting implementation is fast and therefore can run on wide range of affordable hardware.

  7. Cartan invariants and event horizon detection

    Science.gov (United States)

    Brooks, D.; Chavy-Waddy, P. C.; Coley, A. A.; Forget, A.; Gregoris, D.; MacCallum, M. A. H.; McNutt, D. D.

    2018-04-01

    We show that it is possible to locate the event horizon of a black hole (in arbitrary dimensions) by the zeros of certain Cartan invariants. This approach accounts for the recent results on the detection of stationary horizons using scalar polynomial curvature invariants, and improves upon them since the proposed method is computationally less expensive. As an application, we produce Cartan invariants that locate the event horizons for various exact four-dimensional and five-dimensional stationary, asymptotically flat (or (anti) de Sitter), black hole solutions and compare the Cartan invariants with the corresponding scalar curvature invariants that detect the event horizon.

  8. A time-varying subjective quality model for mobile streaming videos with stalling events

    Science.gov (United States)

    Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C.

    2015-09-01

    Over-the-top mobile video streaming is invariably influenced by volatile network conditions which cause playback interruptions (stalling events), thereby impairing users' quality of experience (QoE). Developing models that can accurately predict users' QoE could enable the more efficient design of quality-control protocols for video streaming networks that reduce network operational costs while still delivering high-quality video content to the customers. Existing objective models that predict QoE are based on global video features, such as the number of stall events and their lengths, and are trained and validated on a small pool of ad hoc video datasets, most of which are not publicly available. The model we propose in this work goes beyond previous models as it also accounts for the fundamental effect that a viewer's recent level of satisfaction or dissatisfaction has on their overall viewing experience. In other words, the proposed model accounts for and adapts to the recency, or hysteresis effect caused by a stall event in addition to accounting for the lengths, frequency of occurrence, and the positions of stall events - factors that interact in a complex way to affect a user's QoE. On the recently introduced LIVE-Avvasi Mobile Video Database, which consists of 180 distorted videos of varied content that are afflicted solely with over 25 unique realistic stalling events, we trained and validated our model to accurately predict the QoE, attaining standout QoE prediction performance.

  9. Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

    Directory of Open Access Journals (Sweden)

    V. B. Surya Prasath

    2016-12-01

    Full Text Available Video capsule endoscopy (VCE is used widely nowadays for visualizing the gastrointestinal (GI tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams, automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics, detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.

  10. Vehicle Plate Detection in Car Black Box Video

    Directory of Open Access Journals (Sweden)

    Dongjin Park

    2017-01-01

    Full Text Available Internet services that share vehicle black box videos need a way to obfuscate license plates in uploaded videos because of privacy issues. Thus, plate detection is one of the critical functions that such services rely on. Even though various types of detection methods are available, they are not suitable for black box videos because no assumption about size, number of plates, and lighting conditions can be made. We propose a method to detect Korean vehicle plates from black box videos. It works in two stages: the first stage aims to locate a set of candidate plate regions and the second stage identifies only actual plates from candidates by using a support vector machine classifier. The first stage consists of five sequential substeps. At first, it produces candidate regions by combining single character areas and then eliminates candidate regions that fail to meet plate conditions through the remaining substeps. For the second stage, we propose a feature vector that captures the characteristics of plates in texture and color. For performance evaluation, we compiled our dataset which contains 2,627 positive and negative images. The evaluation results show that the proposed method improves accuracy and sensitivity by at least 5% and is 30 times faster compared with an existing method.

  11. Context-aware event detection smartphone application for first responders

    Science.gov (United States)

    Boddhu, Sanjay K.; Dave, Rakesh P.; McCartney, Matt; West, James A.; Williams, Robert L.

    2013-05-01

    The rise of social networking platforms like Twitter, Facebook, etc…, have provided seamless sharing of information (as chat, video and other media) among its user community on a global scale. Further, the proliferation of the smartphones and their connectivity networks has powered the ordinary individuals to share and acquire information regarding the events happening in his/her immediate vicinity in a real-time fashion. This human-centric sensed data being generated in "human-as-sensor" approach is tremendously valuable as it delivered mostly with apt annotations and ground truth that would be missing in traditional machine-centric sensors, besides high redundancy factor (same data thru multiple users). Further, when appropriately employed this real-time data can support in detecting localized events like fire, accidents, shooting, etc…, as they unfold and pin-point individuals being affected by those events. This spatiotemporal information, when made available for first responders in the event vicinity (or approaching it) can greatly assist them to make effective decisions to protect property and life in a timely fashion. In this vein, under SATE and YATE programs, the research team at AFRL Tec^Edge Discovery labs had demonstrated the feasibility of developing Smartphone applications, that can provide a augmented reality view of the appropriate detected events in a given geographical location (localized) and also provide an event search capability over a large geographic extent. In its current state, the application thru its backend connectivity utilizes a data (Text & Image) processing framework, which deals with data challenges like; identifying and aggregating important events, analyzing and correlating the events temporally and spatially and building a search enabled event database. Further, the smartphone application with its backend data processing workflow has been successfully field tested with live user generated feeds.

  12. Semantic Context Detection Using Audio Event Fusion

    Directory of Open Access Journals (Sweden)

    Cheng Wen-Huang

    2006-01-01

    Full Text Available Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model and discriminative (support vector machine (SVM approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.

  13. Metrics for Polyphonic Sound Event Detection

    Directory of Open Access Journals (Sweden)

    Annamaria Mesaros

    2016-05-01

    Full Text Available This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.

  14. Guest Editorial: Analysis and Retrieval of Events/Actions and Workflows in Video Streams

    DEFF Research Database (Denmark)

    Doulamis, Anastasios; Doulamis, Nikolaos; Bertini, Marco

    2016-01-01

    .g., thematic parks, critical public infrastructures), crisis management in public service areas (e.g., train stations, airports), security (detection of abnormal behaviors in surveillance videos), semantic characterization, and annotation of video streams in various domains (e.g., broadcast or user...

  15. Detection of illegal transfer of videos over the Internet

    Science.gov (United States)

    Chaisorn, Lekha; Sainui, Janya; Manders, Corey

    2010-07-01

    In this paper, a method for detecting infringements or modifications of a video in real-time is proposed. The method first segments a video stream into shots, after which it extracts some reference frames as keyframes. This process is performed employing a Singular Value Decomposition (SVD) technique developed in this work. Next, for each input video (represented by its keyframes), ordinal-based signature and SIFT (Scale Invariant Feature Transform) descriptors are generated. The ordinal-based method employs a two-level bitmap indexing scheme to construct the index for each video signature. The first level clusters all input keyframes into k clusters while the second level converts the ordinal-based signatures into bitmap vectors. On the other hand, the SIFT-based method directly uses the descriptors as the index. Given a suspect video (being streamed or transferred on the Internet), we generate the signature (ordinal and SIFT descriptors) then we compute similarity between its signature and those signatures in the database based on ordinal signature and SIFT descriptors separately. For similarity measure, besides the Euclidean distance, Boolean operators are also utilized during the matching process. We have tested our system by performing several experiments on 50 videos (each about 1/2 hour in duration) obtained from the TRECVID 2006 data set. For experiments set up, we refer to the conditions provided by TRECVID 2009 on "Content-based copy detection" task. In addition, we also refer to the requirements issued in the call for proposals by MPEG standard on the similar task. Initial result shows that our framework is effective and robust. As compared to our previous work, on top of the achievement we obtained by reducing the storage space and time taken in the ordinal based method, by introducing the SIFT features, we could achieve an overall accuracy in F1 measure of about 96% (improved about 8%).

  16. AUTOMATIC FAST VIDEO OBJECT DETECTION AND TRACKING ON VIDEO SURVEILLANCE SYSTEM

    Directory of Open Access Journals (Sweden)

    V. Arunachalam

    2012-08-01

    Full Text Available This paper describes the advance techniques for object detection and tracking in video. Most visual surveillance systems start with motion detection. Motion detection methods attempt to locate connected regions of pixels that represent the moving objects within the scene; different approaches include frame-to-frame difference, background subtraction and motion analysis. The motion detection can be achieved by Principle Component Analysis (PCA and then separate an objects from background using background subtraction. The detected object can be segmented. Segmentation consists of two schemes: one for spatial segmentation and the other for temporal segmentation. Tracking approach can be done in each frame of detected Object. Pixel label problem can be alleviated by the MAP (Maximum a Posteriori technique.

  17. Short-term change detection for UAV video

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang

    2012-11-01

    In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer

  18. [Sleep paroxysmal events in children in video/polysomnography].

    Science.gov (United States)

    Zajac, Anna; Skowronek-Bała, Barbara; Wesołowska, Ewa; Kaciński, Marek

    2010-01-01

    It is estimated that about 25% of children have sleep disorders, from short problems with falling asleep to severe including primary sleep disorders. Majority of these problems are transitory and self-limiting and usually are not recognized by first care physicians and need education. Analysis of sleep structure at the developmental age and of sleep disorders associated with different sleep phases on the basis of video/polysomnography results. Literature review and illustration of fundamental problems associated with sleep physiology and pathology, with special attention to paroxysmal disorders. Additionally 4 cases from our own experience were presented with neurophysiological and clinical aspects. Discussion on REM and NREM sleep, its phases and alternating share according to child's age was conducted. Sleep disorders were in accordance with their international classification. Parasomnias, occupying most of the space, were divided in two groups: primary and secondary. Among primary parasomnias disorders associated with falling asleep (sleep myoclonus, hypnagogic hallucinations, sleep paralysis, rhythmic movement disorder, restless legs syndrome) are important. Another disorders are parasomians associated with light NREM sleep (bruxism, periodic limb movement disorder) and with deeper NREM sleep (confusional arousals, somnabulism, night terrors), with REM sleep (nightmares, REM sleep behavior disorder) and associated with NREM and REM sleep (catathrenia, sleep enuresis, sleep talking). Obstructive sleep apnea syndrome and epileptic seizures occurring during sleep also play an important role. Frontal lobe epilepsy and Panayiotopoulos syndrome should be considered in the first place in such cases. Our 4 cases document these diagnostic difficulties, requiring video/polysomnography examination 2 of them illustrate frontal lobe epilepsy and single ones myoclonic epilepsy graphy in children is a difficult technique and requires special device, local and trained

  19. A simple strategy for fall events detection

    KAUST Repository

    Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying; Houacine, Amrane

    2017-01-01

    the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart

  20. Adaptive prediction applied to seismic event detection

    International Nuclear Information System (INIS)

    Clark, G.A.; Rodgers, P.W.

    1981-01-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data

  1. Adaptive prediction applied to seismic event detection

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Rodgers, P.W.

    1981-09-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data.

  2. Real-time logo detection and tracking in video

    Science.gov (United States)

    George, M.; Kehtarnavaz, N.; Rahman, M.; Carlsohn, M.

    2010-05-01

    This paper presents a real-time implementation of a logo detection and tracking algorithm in video. The motivation of this work stems from applications on smart phones that require the detection of logos in real-time. For example, one application involves detecting company logos so that customers can easily get special offers in real-time. This algorithm uses a hybrid approach by initially running the Scale Invariant Feature Transform (SIFT) algorithm on the first frame in order to obtain the logo location and then by using an online calibration of color within the SIFT detected area in order to detect and track the logo in subsequent frames in a time efficient manner. The results obtained indicate that this hybrid approach allows robust logo detection and tracking to be achieved in real-time.

  3. Nest-crowdcontrol: Advanced video-based crowd monitoring for large public events

    OpenAIRE

    Monari, Eduardo; Fischer, Yvonne; Anneken, Mathias

    2015-01-01

    Current video surveillance systems still lack of intelligent video and data analysis modules for supporting situation awareness of decision makers. Especially in mass gatherings like large public events, the decision maker would benefit from different views of the area, especially from crowd density estimations. This article describes a multi-camera system called NEST and its application for crowd density analysis. First, the overall system design is presented. Based on this, the crowd densit...

  4. Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

    Science.gov (United States)

    Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

    2017-09-01

    Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

  5. VideoStory: A New Multimedia Embedding for Few Example Recognition and Translation of Events

    Science.gov (United States)

    2014-11-07

    series, and movie trailers . We observe these professional videos are typically semantically dissimilar to the event videos which we are interested in...a list of keywords from Wikipedia, which provides an extensive index of celebrity, TV series and movie names1. We exclude the videos whose...Swimming 0.520 0.489 0.691 0.764 Biking 0.324 0.307 0.420 0.561 Graduation 0.083 0.058 0.135 0.121 Birthday 0.149 0.216 0.187 0.257 Wedding reception

  6. Small Vocabulary with Saliency Matching for Video Copy Detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Moeslund, Thomas B.; Tang, Sheng

    2013-01-01

    The importance of copy detection has led to a substantial amount of research in recent years, among which Bag of visual Words (BoW) plays an important role due to its ability to effectively handling occlusion and some minor transformations. One crucial issue in BoW approaches is the size of vocab......The importance of copy detection has led to a substantial amount of research in recent years, among which Bag of visual Words (BoW) plays an important role due to its ability to effectively handling occlusion and some minor transformations. One crucial issue in BoW approaches is the size...... matching algorithm based on salient visual words selection. More specifically, the variation of visual words across a given video are represented as trajectories and those containing locally asymptotically stable points are selected as salient visual words. Then we attempt to measure the similarity of two...... videos through saliency matching merely based on the selected salient visual words to remove false positives. Our experiments show that a small codebook with saliency matching is quite competitive in video copy detection. With the incorporation of the proposed saliency matching, the precision can...

  7. Gas leak detection in infrared video with background modeling

    Science.gov (United States)

    Zeng, Xiaoxia; Huang, Likun

    2018-03-01

    Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

  8. Abnormal Event Detection Using Local Sparse Representation

    DEFF Research Database (Denmark)

    Ren, Huamin; Moeslund, Thomas B.

    2014-01-01

    We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality measurem...... is found that satisfies: the distance between its local space and the normal space is large. We evaluate our method on two public benchmark datasets: UCSD and Subway Entrance datasets. The comparison to the state-of-the-art methods validate our method's effectiveness....

  9. Recognising safety critical events: can automatic video processing improve naturalistic data analyses?

    Science.gov (United States)

    Dozza, Marco; González, Nieves Pañeda

    2013-11-01

    New trends in research on traffic accidents include Naturalistic Driving Studies (NDS). NDS are based on large scale data collection of driver, vehicle, and environment information in real world. NDS data sets have proven to be extremely valuable for the analysis of safety critical events such as crashes and near crashes. However, finding safety critical events in NDS data is often difficult and time consuming. Safety critical events are currently identified using kinematic triggers, for instance searching for deceleration below a certain threshold signifying harsh braking. Due to the low sensitivity and specificity of this filtering procedure, manual review of video data is currently necessary to decide whether the events identified by the triggers are actually safety critical. Such reviewing procedure is based on subjective decisions, is expensive and time consuming, and often tedious for the analysts. Furthermore, since NDS data is exponentially growing over time, this reviewing procedure may not be viable anymore in the very near future. This study tested the hypothesis that automatic processing of driver video information could increase the correct classification of safety critical events from kinematic triggers in naturalistic driving data. Review of about 400 video sequences recorded from the events, collected by 100 Volvo cars in the euroFOT project, suggested that drivers' individual reaction may be the key to recognize safety critical events. In fact, whether an event is safety critical or not often depends on the individual driver. A few algorithms, able to automatically classify driver reaction from video data, have been compared. The results presented in this paper show that the state of the art subjective review procedures to identify safety critical events from NDS can benefit from automated objective video processing. In addition, this paper discusses the major challenges in making such video analysis viable for future NDS and new potential

  10. Design and Implementation of Video Shot Detection on Field Programmable Gate Arrays

    Directory of Open Access Journals (Sweden)

    Jharna Majumdar

    2012-09-01

    Full Text Available Video has become an interactive medium of communication in everyday life. The sheer volume of video makes it extremely difficult to browse through and find the required data. Hence extraction of key frames from the video which represents the abstract of the entire video becomes necessary. The aim of the video shot detection is to find the position of the shot boundaries, so that key frames can be selected from each shot for subsequent processing such as video summarization, indexing etc. For most of the surveillance applications like video summery, face recognition etc., the hardware (real time implementation of these algorithms becomes necessary. Here in this paper we present the architecture for simultaneous accessing of consecutive frames, which are then used for the implementation of various Video Shot Detection algorithms. We also present the real time implementation of three video shot detection algorithms using the above mentioned architecture on FPGA (Field Programmable Gate Arrays.

  11. Short-Term Effects of Prosocial Video Games on Aggression: An Event-Related Potential Study

    Directory of Open Access Journals (Sweden)

    Yanling eLiu

    2015-07-01

    Full Text Available Previous research has shown that exposure to violent video games increases aggression, whereas exposure to prosocial video games can reduce aggressive behavior. However, little is known about the neural correlates of these behavioral effects. This work is the first to investigate the electrophysiological features of the relationship between playing a prosocial video game and inhibition of aggressive behavior. Forty-nine subjects played either a prosocial or a neutral video game for 20 minutes, then participated in an event-related potential (ERP experiment based on an oddball paradigm and designed to test electrophysiological responses to prosocial and violent words. Finally, subjects completed a competitive reaction time task (CRTT, which is based on Taylor’s Aggression Paradigm and measures both reaction time and noise intensity preference as indices of aggressive behavior. The results show that the prosocial video game group (compared to the neutral video game group displayed smaller P300 amplitudes, were more accurate in distinguishing violent words, and were less aggressive as evaluated by the CRTT (noise intensity preference. A mediation analysis shows that the P300 amplitude evoked by violent words partially mediates the relationship between type of video game and subsequent aggressive behavior. The results support theories based on the General Learning Model. We provide converging behavioral and neural evidence that exposure to prosocial media may reduce aggression.

  12. Short-term effects of prosocial video games on aggression: an event-related potential study

    Science.gov (United States)

    Liu, Yanling; Teng, Zhaojun; Lan, Haiying; Zhang, Xin; Yao, Dezhong

    2015-01-01

    Previous research has shown that exposure to violent video games increases aggression, whereas exposure to prosocial video games can reduce aggressive behavior. However, little is known about the neural correlates of these behavioral effects. This work is the first to investigate the electrophysiological features of the relationship between playing a prosocial video game and inhibition of aggressive behavior. Forty-nine subjects played either a prosocial or a neutral video game for 20 min, then participated in an event-related potential (ERP) experiment based on an oddball paradigm and designed to test electrophysiological responses to prosocial and violent words. Finally, subjects completed a competitive reaction time task (CRTT) which based on Taylor's Aggression Paradigm and contains reaction time and noise intensity chosen as a measure of aggressive behavior. The results show that the prosocial video game group (compared to the neutral video game group) displayed smaller P300 amplitudes, were more accurate in distinguishing violent words, and were less aggressive as evaluated by the CRTT of noise intensity chosen. A mediation analysis shows that the P300 amplitude evoked by violent words partially mediates the relationship between type of video game and subsequent aggressive behavior. The results support theories based on the General Learning Model. We provide converging behavioral and neural evidence that exposure to prosocial media may reduce aggression. PMID:26257620

  13. Short-term effects of prosocial video games on aggression: an event-related potential study.

    Science.gov (United States)

    Liu, Yanling; Teng, Zhaojun; Lan, Haiying; Zhang, Xin; Yao, Dezhong

    2015-01-01

    Previous research has shown that exposure to violent video games increases aggression, whereas exposure to prosocial video games can reduce aggressive behavior. However, little is known about the neural correlates of these behavioral effects. This work is the first to investigate the electrophysiological features of the relationship between playing a prosocial video game and inhibition of aggressive behavior. Forty-nine subjects played either a prosocial or a neutral video game for 20 min, then participated in an event-related potential (ERP) experiment based on an oddball paradigm and designed to test electrophysiological responses to prosocial and violent words. Finally, subjects completed a competitive reaction time task (CRTT) which based on Taylor's Aggression Paradigm and contains reaction time and noise intensity chosen as a measure of aggressive behavior. The results show that the prosocial video game group (compared to the neutral video game group) displayed smaller P300 amplitudes, were more accurate in distinguishing violent words, and were less aggressive as evaluated by the CRTT of noise intensity chosen. A mediation analysis shows that the P300 amplitude evoked by violent words partially mediates the relationship between type of video game and subsequent aggressive behavior. The results support theories based on the General Learning Model. We provide converging behavioral and neural evidence that exposure to prosocial media may reduce aggression.

  14. Automatic Emotional State Detection using Facial Expression Dynamic in Videos

    Directory of Open Access Journals (Sweden)

    Hongying Meng

    2014-11-01

    Full Text Available In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems.

  15. Optimizing a neural network for detection of moving vehicles in video

    Science.gov (United States)

    Fischer, Noëlle M.; Kruithof, Maarten C.; Bouma, Henri

    2017-10-01

    In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing, trafficking in human beings and smuggling. Many recent benchmarks have shown that convolutional neural networks are performing well in the detection of objects in images. Most deep-learning research effort focuses on classification or detection on single images. However, the detection of dynamic changes (e.g., moving objects, actions and events) in streaming video is extremely relevant for surveillance and forensic applications. In this paper, we combine an end-to-end feedforward neural network for static detection with a recurrent Long Short-Term Memory (LSTM) network for multi-frame analysis. We present a practical guide with special attention to the selection of the optimizer and batch size. The end-to-end network is able to localize and recognize the vehicles in video from traffic cameras. We show an efficient way to collect relevant in-domain data for training with minimal manual labor. Our results show that the combination with LSTM improves performance for the detection of moving vehicles.

  16. A Semi-Automatic, Remote-Controlled Video Observation System for Transient Luminous Events

    DEFF Research Database (Denmark)

    Allin, Thomas Højgaard; Neubert, Torsten; Laursen, Steen

    2003-01-01

    In support for global ELF/VLF observations, HF measurements in France, and conjugate photometry/VLF observations in South Africa, we developed and operated a semi-automatic, remotely controlled video system for the observation of middle-atmospheric transient luminous events (TLEs). Installed...

  17. First CNGS events detected by LVD

    International Nuclear Information System (INIS)

    Agafonova, N.Yu.; Boyarkin, V.V.; Kuznetsov, V.V.; Kuznetsov, V.A.; Malguin, A.S.; Ryasny, V.G.; Ryazhskaya, O.G.; Yakushev, V.F.; Zatsepin, G.T.; Aglietta, M.; Bonardi, A.; Fulgione, W.; Galeotti, P.; Porta, A.; Saavedra, O.; Vigorito, C.; Antonioli, P.; Bari, G.; Giusti, P.; Menghetti, H.; Persiani, R.; Pesci, A.; Sartorelli, G.; Selvi, M.; Zichichi, A.; Bruno, G.; Ghia, P.L.; Garbini, M.; Kemp, E.; Pless, I.A.; Votano, L.

    2007-01-01

    The CERN Neutrino to Gran Sasso (CNGS) project aims to produce a high energy, wide band ν μ beam at CERN and send it toward the INFN Gran Sasso National Laboratory (LNGS), 732 km away. Its main goal is the observation of the ν τ appearance, through neutrino flavour oscillation. The beam started its operation in August 2006 for about 12 days: a total amount of 7.6 x 10 17 protons were delivered to the target. The LVD detector, installed in hall A of the LNGS and mainly dedicated to the study of supernova neutrinos, was fully operating during the whole CNGS running time. A total number of 569 events were detected in coincidence with the beam spill time. This is in good agreement with the expected number of events from Monte Carlo simulations. (orig.)

  18. First CNGS events detected by LVD

    International Nuclear Information System (INIS)

    Selvi, M.

    2007-01-01

    The Cern Neutrino to Gran Sasso (CNGS) project aims to produce a high energy, wide band ν μ beam at Cern and send it towards the INFN Gran Sasso National Laboratory (LNGS), 732 km away. Its main goal is the observation of the ν τ appearance, through neutrino flavour oscillation. The beam started its operation in August 2006 for about 12 days: a total amount of 7.6 10 17 protons were delivered to the target. The LVD detector, installed in hall A of the LNGS and mainly dedicated to the study of supernova neutrinos, was fully operating during the whole CNGS running time. A total number of 569 events were detected in coincidence with the beam spill time. This is in good agreement with the expected number of events from Montecarlo simulations

  19. LAN attack detection using Discrete Event Systems.

    Science.gov (United States)

    Hubballi, Neminath; Biswas, Santosh; Roopa, S; Ratti, Ritesh; Nandi, Sukumar

    2011-01-01

    Address Resolution Protocol (ARP) is used for determining the link layer or Medium Access Control (MAC) address of a network host, given its Internet Layer (IP) or Network Layer address. ARP is a stateless protocol and any IP-MAC pairing sent by a host is accepted without verification. This weakness in the ARP may be exploited by malicious hosts in a Local Area Network (LAN) by spoofing IP-MAC pairs. Several schemes have been proposed in the literature to circumvent these attacks; however, these techniques either make IP-MAC pairing static, modify the existing ARP, patch operating systems of all the hosts etc. In this paper we propose a Discrete Event System (DES) approach for Intrusion Detection System (IDS) for LAN specific attacks which do not require any extra constraint like static IP-MAC, changing the ARP etc. A DES model is built for the LAN under both a normal and compromised (i.e., spoofed request/response) situation based on the sequences of ARP related packets. Sequences of ARP events in normal and spoofed scenarios are similar thereby rendering the same DES models for both the cases. To create different ARP events under normal and spoofed conditions the proposed technique uses active ARP probing. However, this probing adds extra ARP traffic in the LAN. Following that a DES detector is built to determine from observed ARP related events, whether the LAN is operating under a normal or compromised situation. The scheme also minimizes extra ARP traffic by probing the source IP-MAC pair of only those ARP packets which are yet to be determined as genuine/spoofed by the detector. Also, spoofed IP-MAC pairs determined by the detector are stored in tables to detect other LAN attacks triggered by spoofing namely, man-in-the-middle (MiTM), denial of service etc. The scheme is successfully validated in a test bed. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Detection of Upscale-Crop and Partial Manipulation in Surveillance Video Based on Sensor Pattern Noise

    Science.gov (United States)

    Hyun, Dai-Kyung; Ryu, Seung-Jin; Lee, Hae-Yeoun; Lee, Heung-Kyu

    2013-01-01

    In many court cases, surveillance videos are used as significant court evidence. As these surveillance videos can easily be forged, it may cause serious social issues, such as convicting an innocent person. Nevertheless, there is little research being done on forgery of surveillance videos. This paper proposes a forensic technique to detect forgeries of surveillance video based on sensor pattern noise (SPN). We exploit the scaling invariance of the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter to reliably unveil traces of upscaling in videos. By excluding the high-frequency components of the investigated video and adaptively choosing the size of the local search window, the proposed method effectively localizes partially manipulated regions. Empirical evidence from a large database of test videos, including RGB (Red, Green, Blue)/infrared video, dynamic-/static-scene video and compressed video, indicates the superior performance of the proposed method. PMID:24051524

  1. Video copy protection and detection framework (VPD) for e-learning systems

    Science.gov (United States)

    ZandI, Babak; Doustarmoghaddam, Danial; Pour, Mahsa R.

    2013-03-01

    This Article reviews and compares the copyright issues related to the digital video files, which can be categorized as contended based and Digital watermarking copy Detection. Then we describe how to protect a digital video by using a special Video data hiding method and algorithm. We also discuss how to detect the copy right of the file, Based on expounding Direction of the technology of the video copy detection, and Combining with the own research results, brings forward a new video protection and copy detection approach in terms of plagiarism and e-learning systems using the video data hiding technology. Finally we introduce a framework for Video protection and detection in e-learning systems (VPD Framework).

  2. Multilingual event extraction for epidemic detection.

    Science.gov (United States)

    Lejeune, Gaël; Brixtel, Romain; Doucet, Antoine; Lucas, Nadine

    2015-10-01

    This paper presents a multilingual news surveillance system applied to tele-epidemiology. It has been shown that multilingual approaches improve timeliness in detection of epidemic events across the globe, eliminating the wait for local news to be translated into major languages. We present here a system to extract epidemic events in potentially any language, provided a Wikipedia seed for common disease names exists. The Daniel system presented herein relies on properties that are common to news writing (the journalistic genre), the most useful being repetition and saliency. Wikipedia is used to screen common disease names to be matched with repeated characters strings. Language variations, such as declensions, are handled by processing text at the character-level, rather than at the word level. This additionally makes it possible to handle various writing systems in a similar fashion. As no multilingual ground truth existed to evaluate the Daniel system, we built a multilingual corpus from the Web, and collected annotations from native speakers of Chinese, English, Greek, Polish and Russian, with no connection or interest in the Daniel system. This data set is available online freely, and can be used for the evaluation of other event extraction systems. Experiments for 5 languages out of 17 tested are detailed in this paper: Chinese, English, Greek, Polish and Russian. The Daniel system achieves an average F-measure of 82% in these 5 languages. It reaches 87% on BEcorpus, the state-of-the-art corpus in English, slightly below top-performing systems, which are tailored with numerous language-specific resources. The consistent performance of Daniel on multiple languages is an important contribution to the reactivity and the coverage of epidemiological event detection systems. Most event extraction systems rely on extensive resources that are language-specific. While their sophistication induces excellent results (over 90% precision and recall), it restricts their

  3. Sunglass detection method for automation of video surveillance system

    Science.gov (United States)

    Sikandar, Tasriva; Samsudin, Wan Nur Azhani W.; Hawari Ghazali, Kamarul; Mohd, Izzeldin I.; Fazle Rabbi, Mohammad

    2018-04-01

    Wearing sunglass to hide face from surveillance camera is a common activity in criminal incidences. Therefore, sunglass detection from surveillance video has become a demanding issue in automation of security systems. In this paper we propose an image processing method to detect sunglass from surveillance images. Specifically, a unique feature using facial height and width has been employed to identify the covered region of the face. The presence of covered area by sunglass is evaluated using facial height-width ratio. Threshold value of covered area percentage is used to classify the glass wearing face. Two different types of glasses have been considered i.e. eye glass and sunglass. The results of this study demonstrate that the proposed method is able to detect sunglasses in two different illumination conditions such as, room illumination as well as in the presence of sunlight. In addition, due to the multi-level checking in facial region, this method has 100% accuracy of detecting sunglass. However, in an exceptional case where fabric surrounding the face has similar color as skin, the correct detection rate was found 93.33% for eye glass.

  4. Field Test Data for Detecting Vibrations of a Building Using High-Speed Video Cameras

    Science.gov (United States)

    2017-10-01

    ARL-TR-8185 ● OCT 2017 US Army Research Laboratory Field Test Data for Detecting Vibrations of a Building Using High-Speed Video...Field Test Data for Detecting Vibrations of a Building Using High-Speed Video Cameras by Caitlin P Conn and Geoffrey H Goldman Sensors and...June 2016 – October 2017 4. TITLE AND SUBTITLE Field Test Data for Detecting Vibrations of a Building Using High-Speed Video Cameras 5a. CONTRACT

  5. Video Analysis Verification of Head Impact Events Measured by Wearable Sensors.

    Science.gov (United States)

    Cortes, Nelson; Lincoln, Andrew E; Myer, Gregory D; Hepburn, Lisa; Higgins, Michael; Putukian, Margot; Caswell, Shane V

    2017-08-01

    Wearable sensors are increasingly used to quantify the frequency and magnitude of head impact events in multiple sports. There is a paucity of evidence that verifies head impact events recorded by wearable sensors. To utilize video analysis to verify head impact events recorded by wearable sensors and describe the respective frequency and magnitude. Cohort study (diagnosis); Level of evidence, 2. Thirty male (mean age, 16.6 ± 1.2 years; mean height, 1.77 ± 0.06 m; mean weight, 73.4 ± 12.2 kg) and 35 female (mean age, 16.2 ± 1.3 years; mean height, 1.66 ± 0.05 m; mean weight, 61.2 ± 6.4 kg) players volunteered to participate in this study during the 2014 and 2015 lacrosse seasons. Participants were instrumented with GForceTracker (GFT; boys) and X-Patch sensors (girls). Simultaneous game video was recorded by a trained videographer using a single camera located at the highest midfield location. One-third of the field was framed and panned to follow the ball during games. Videographic and accelerometer data were time synchronized. Head impact counts were compared with video recordings and were deemed valid if (1) the linear acceleration was ≥20 g, (2) the player was identified on the field, (3) the player was in camera view, and (4) the head impact mechanism could be clearly identified. Descriptive statistics of peak linear acceleration (PLA) and peak rotational velocity (PRV) for all verified head impacts ≥20 g were calculated. For the boys, a total recorded 1063 impacts (2014: n = 545; 2015: n = 518) were logged by the GFT between game start and end times (mean PLA, 46 ± 31 g; mean PRV, 1093 ± 661 deg/s) during 368 player-games. Of these impacts, 690 were verified via video analysis (65%; mean PLA, 48 ± 34 g; mean PRV, 1242 ± 617 deg/s). The X-Patch sensors, worn by the girls, recorded a total 180 impacts during the course of the games, and 58 (2014: n = 33; 2015: n = 25) were verified via video analysis (32%; mean PLA, 39 ± 21 g; mean PRV, 1664

  6. Object-Oriented Query Language For Events Detection From Images Sequences

    Science.gov (United States)

    Ganea, Ion Eugen

    2015-09-01

    In this paper is presented a method to represent the events extracted from images sequences and the query language used for events detection. Using an object oriented model the spatial and temporal relationships between salient objects and also between events are stored and queried. This works aims to unify the storing and querying phases for video events processing. The object oriented language syntax used for events processing allow the instantiation of the indexes classes in order to improve the accuracy of the query results. The experiments were performed on images sequences provided from sport domain and it shows the reliability and the robustness of the proposed language. To extend the language will be added a specific syntax for constructing the templates for abnormal events and for detection of the incidents as the final goal of the research.

  7. Damaged Watermarks Detection in Frequency Domain as a Primary Method for Video Concealment

    Directory of Open Access Journals (Sweden)

    Robert Hudec

    2011-01-01

    Full Text Available This paper deals with video transmission over lossy communication networks. The main idea is to develop video concealment method for information losses and errors correction. At the beginning, three main groups of video concealment methods, divided by encoder/decoder collaboration, are briefly described. The modified algorithm based on the detection and filtration of damaged watermark blocks encapsulated to the transmitted video was developed. Finally, the efficiency of developed algorithm is presented in experimental part of this paper.

  8. Automatic detection of artifacts in converted S3D video

    Science.gov (United States)

    Bokov, Alexander; Vatolin, Dmitriy; Zachesov, Anton; Belous, Alexander; Erofeev, Mikhail

    2014-03-01

    In this paper we present algorithms for automatically detecting issues specific to converted S3D content. When a depth-image-based rendering approach produces a stereoscopic image, the quality of the result depends on both the depth maps and the warping algorithms. The most common problem with converted S3D video is edge-sharpness mismatch. This artifact may appear owing to depth-map blurriness at semitransparent edges: after warping, the object boundary becomes sharper in one view and blurrier in the other, yielding binocular rivalry. To detect this problem we estimate the disparity map, extract boundaries with noticeable differences, and analyze edge-sharpness correspondence between views. We pay additional attention to cases involving a complex background and large occlusions. Another problem is detection of scenes that lack depth volume: we present algorithms for detecting at scenes and scenes with at foreground objects. To identify these problems we analyze the features of the RGB image as well as uniform areas in the depth map. Testing of our algorithms involved examining 10 Blu-ray 3D releases with converted S3D content, including Clash of the Titans, The Avengers, and The Chronicles of Narnia: The Voyage of the Dawn Treader. The algorithms we present enable improved automatic quality assessment during the production stage.

  9. Gradual cut detection using low-level vision for digital video

    Science.gov (United States)

    Lee, Jae-Hyun; Choi, Yeun-Sung; Jang, Ok-bae

    1996-09-01

    Digital video computing and organization is one of the important issues in multimedia system, signal compression, or database. Video should be segmented into shots to be used for identification and indexing. This approach requires a suitable method to automatically locate cut points in order to separate shot in a video. Automatic cut detection to isolate shots in a video has received considerable attention due to many practical applications; our video database, browsing, authoring system, retrieval and movie. Previous studies are based on a set of difference mechanisms and they measured the content changes between video frames. But they could not detect more special effects which include dissolve, wipe, fade-in, fade-out, and structured flashing. In this paper, a new cut detection method for gradual transition based on computer vision techniques is proposed. And then, experimental results applied to commercial video are presented and evaluated.

  10. Extending parental mentoring using an event-triggered video intervention in rural teen drivers.

    Science.gov (United States)

    McGehee, Daniel V; Raby, Mireille; Carney, Cher; Lee, John D; Reyes, Michelle L

    2007-01-01

    Teen drivers are at high risk for car crashes, especially during their first years of licensure. Providing novice teen drivers and their parents with a means of identifying their risky driving maneuvers may help them learn from their mistakes, thereby reducing their crash propensity. During the initial phase of learning, adult or parental supervision often provides such guidance. However, once teens obtain their license, adult supervision is no longer mandated, and teens are left to themselves to continue the learning process. This study is the first of its type to enhance this continued learning process using an event-triggered video device. By pairing this new technology with parental feedback in the form of a weekly video review and graphical report card, we extend parents' ability to teach their teens even after they begin driving independently. Twenty-six 16- to 17-year-old drivers were recruited from a small U.S. Midwestern rural high school. We equipped their vehicles with an event-triggered video device, designed to capture 20-sec clips of the forward and cabin views whenever the vehicle exceeded lateral or forward threshold accelerations. Preliminary findings suggest that combining this emerging technology with parental weekly review of safety-relevant incidents resulted in a significant decrease in events for the more at-risk teen drivers. Implications for how such an intervention could be implemented within GDL are also discussed.

  11. Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Benabbas Yassine

    2011-01-01

    Full Text Available Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearance ambiguity, and occlusion. In this work, we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge, split, walk, run, local dispersion, and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed.

  12. Review of passive-blind detection in digital video forgery based on sensing and imaging techniques

    Science.gov (United States)

    Tao, Junjie; Jia, Lili; You, Ying

    2016-01-01

    Advances in digital video compression and IP communication technologies raised new issues and challenges concerning the integrity and authenticity of surveillance videos. It is so important that the system should ensure that once recorded, the video cannot be altered; ensuring the audit trail is intact for evidential purposes. This paper gives an overview of passive techniques of Digital Video Forensics which are based on intrinsic fingerprints inherent in digital surveillance videos. In this paper, we performed a thorough research of literatures relevant to video manipulation detection methods which accomplish blind authentications without referring to any auxiliary information. We presents review of various existing methods in literature, and much more work is needed to be done in this field of video forensics based on video data analysis and observation of the surveillance systems.

  13. Intelligent video surveillance systems

    CERN Document Server

    Dufour, Jean-Yves

    2012-01-01

    Belonging to the wider academic field of computer vision, video analytics has aroused a phenomenal surge of interest since the current millennium. Video analytics is intended to solve the problem of the incapability of exploiting video streams in real time for the purpose of detection or anticipation. It involves analyzing the videos using algorithms that detect and track objects of interest over time and that indicate the presence of events or suspect behavior involving these objects.The aims of this book are to highlight the operational attempts of video analytics, to identify possi

  14. Segmentation Based Video Steganalysis to Detect Motion Vector Modification

    Directory of Open Access Journals (Sweden)

    Peipei Wang

    2017-01-01

    Full Text Available This paper presents a steganalytic approach against video steganography which modifies motion vector (MV in content adaptive manner. Current video steganalytic schemes extract features from fixed-length frames of the whole video and do not take advantage of the content diversity. Consequently, the effectiveness of the steganalytic feature is influenced by video content and the problem of cover source mismatch also affects the steganalytic performance. The goal of this paper is to propose a steganalytic method which can suppress the differences of statistical characteristics caused by video content. The given video is segmented to subsequences according to block’s motion in every frame. The steganalytic features extracted from each category of subsequences with close motion intensity are used to build one classifier. The final steganalytic result can be obtained by fusing the results of weighted classifiers. The experimental results have demonstrated that our method can effectively improve the performance of video steganalysis, especially for videos of low bitrate and low embedding ratio.

  15. FPGA-Based Real-Time Motion Detection for Automated Video Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Sanjay Singh

    2016-03-01

    Full Text Available Design of automated video surveillance systems is one of the exigent missions in computer vision community because of their ability to automatically select frames of interest in incoming video streams based on motion detection. This research paper focuses on the real-time hardware implementation of a motion detection algorithm for such vision based automated surveillance systems. A dedicated VLSI architecture has been proposed and designed for clustering-based motion detection scheme. The working prototype of a complete standalone automated video surveillance system, including input camera interface, designed motion detection VLSI architecture, and output display interface, with real-time relevant motion detection capabilities, has been implemented on Xilinx ML510 (Virtex-5 FX130T FPGA platform. The prototyped system robustly detects the relevant motion in real-time in live PAL (720 × 576 resolution video streams directly coming from the camera.

  16. Moving object detection in video satellite image based on deep learning

    Science.gov (United States)

    Zhang, Xueyang; Xiang, Junhua

    2017-11-01

    Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

  17. Detecting surface events at the COBRA experiment

    Energy Technology Data Exchange (ETDEWEB)

    Tebruegge, Jan [Exp. Physik IV, TU Dortmund (Germany); Collaboration: COBRA-Collaboration

    2015-07-01

    The aim of the COBRA experiment is to prove the existence of neutrinoless double-beta-decay and to measure its half-life. For this purpose the COBRA demonstrator, a prototype for a large-scale experiment, is operated at the Gran Sasso Underground Laboratory (LNGS) in Italy. The demonstrator is a detector array made of 64 Cadmium-Zinc-Telluride (CdZnTe) semiconductor detectors in the coplanar grid anode configuration. Each detector is 1**1 ccm in size. This setup is used to investigate the experimental issues of operating CdZnTe detectors in low background mode and identify potential background components. As the ''detector=source'' principle is used, the neutrinoless double beta decay COBRA searches for happens within the whole detector volume. Consequently, events on the surface of the detectors are considered as background. These surface events are a main background component, stemming mainly from the natural radioactivity, especially radon. This talk explains to what extent surface events occur and shows how these are recognized and vetoed in the analysis using pulse shape discrimination algorithms.

  18. Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

    Science.gov (United States)

    Sa, Qila; Wang, Zhihui

    2018-03-01

    At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

  19. Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos

    KAUST Repository

    Heilbron, Fabian Caba; Niebles, Juan Carlos; Ghanem, Bernard

    2016-01-01

    In many large-scale video analysis scenarios, one is interested in localizing and recognizing human activities that occur in short temporal intervals within long untrimmed videos. Current approaches for activity detection still struggle to handle large-scale video collections and the task remains relatively unexplored. This is in part due to the computational complexity of current action recognition approaches and the lack of a method that proposes fewer intervals in the video, where activity processing can be focused. In this paper, we introduce a proposal method that aims to recover temporal segments containing actions in untrimmed videos. Building on techniques for learning sparse dictionaries, we introduce a learning framework to represent and retrieve activity proposals. We demonstrate the capabilities of our method in not only producing high quality proposals but also in its efficiency. Finally, we show the positive impact our method has on recognition performance when it is used for action detection, while running at 10FPS.

  20. Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos

    KAUST Repository

    Heilbron, Fabian Caba

    2016-12-13

    In many large-scale video analysis scenarios, one is interested in localizing and recognizing human activities that occur in short temporal intervals within long untrimmed videos. Current approaches for activity detection still struggle to handle large-scale video collections and the task remains relatively unexplored. This is in part due to the computational complexity of current action recognition approaches and the lack of a method that proposes fewer intervals in the video, where activity processing can be focused. In this paper, we introduce a proposal method that aims to recover temporal segments containing actions in untrimmed videos. Building on techniques for learning sparse dictionaries, we introduce a learning framework to represent and retrieve activity proposals. We demonstrate the capabilities of our method in not only producing high quality proposals but also in its efficiency. Finally, we show the positive impact our method has on recognition performance when it is used for action detection, while running at 10FPS.

  1. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

    Directory of Open Access Journals (Sweden)

    Nouar AlDahoul

    2018-01-01

    Full Text Available Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN, pretrained CNN feature extractor, and hierarchical extreme learning machine for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running. Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM. H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU, H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU.

  2. Infrared video based gas leak detection method using modified FAST features

    Science.gov (United States)

    Wang, Min; Hong, Hanyu; Huang, Likun

    2018-03-01

    In order to detect the invisible leaking gas that is usually dangerous and easily leads to fire or explosion in time, many new technologies have arisen in the recent years, among which the infrared video based gas leak detection is widely recognized as a viable tool. However, all the moving regions of a video frame can be detected as leaking gas regions by the existing infrared video based gas leak detection methods, without discriminating the property of each detected region, e.g., a walking person in a video frame may be also detected as gas by the current gas leak detection methods.To solve this problem, we propose a novel infrared video based gas leak detection method in this paper, which is able to effectively suppress strong motion disturbances.Firstly, the Gaussian mixture model(GMM) is used to establish the background model.Then due to the observation that the shapes of gas regions are different from most rigid moving objects, we modify the Features From Accelerated Segment Test (FAST) algorithm and use the modified FAST (mFAST) features to describe each connected component. In view of the fact that the statistical property of the mFAST features extracted from gas regions is different from that of other motion regions, we propose the Pixel-Per-Points (PPP) condition to further select candidate connected components.Experimental results show that the algorithm is able to effectively suppress most strong motion disturbances and achieve real-time leaking gas detection.

  3. Event detection in athletics for personalized sports content delivery

    DEFF Research Database (Denmark)

    Katsarakis, N.; Pnevmatikakis, A.

    2009-01-01

    Broadcasting of athletics is nowadays biased towards running (sprint and longer distances) sports. Personalized content delivery can change that for users that wish to focus on different content. Using a combination of video signal processing algorithms and live information that accompanies the v...... algorithms needed for the extraction of the events that trigger both between and within sport camera selection, and describes a system that handles user preferences, live information andvideo-generated events to offer personalized content to the users.......Broadcasting of athletics is nowadays biased towards running (sprint and longer distances) sports. Personalized content delivery can change that for users that wish to focus on different content. Using a combination of video signal processing algorithms and live information that accompanies...... the video of large-scale sports like the Olympics, a system can attend to the preferences of users by selecting the most suitable camera view for them.There are two types of camera selection for personalized content delivery. According to the between sport camera selection, the view is changed between two...

  4. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2013-12-01

    Full Text Available The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM, combined with its sparsified version (sparse online LS-OC-SVM. LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

  5. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  6. Subsurface event detection and classification using Wireless Signal Networks.

    Science.gov (United States)

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-11-05

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  7. Lesson Plan Prototype for International Space Station's Interactive Video Education Events

    Science.gov (United States)

    Zigon, Thomas

    1999-01-01

    The outreach and education components of the International Space Station Program are creating a number of materials, programs, and activities that educate and inform various groups as to the implementation and purposes of the International Space Station. One of the strategies for disseminating this information to K-12 students involves an electronic class room using state of the art video conferencing technology. K-12 classrooms are able to visit the JSC, via an electronic field trip. Students interact with outreach personnel as they are taken on a tour of ISS mockups. Currently these events can be generally characterized as: Being limited to a one shot events, providing only one opportunity for students to view the ISS mockups; Using a "one to many" mode of communications; Using a transmissive, lecture based method of presenting information; Having student interactions limited to Q&A during the live event; Making limited use of media; and Lacking any formal, performance based, demonstration of learning on the part of students. My project involved developing interactive lessons for K-12 students (specifically 7th grade) that will reflect a 2nd generation design for electronic field trips. The goal of this design will be to create electronic field trips that will: Conform to national education standards; More fully utilize existing information resources; Integrate media into field trip presentations; Make support media accessible to both presenters and students; Challenge students to actively participate in field trip related activities; and Provide students with opportunities to demonstrate learning

  8. Video event data recording of a taxi driver used for diagnosis of epilepsy

    Directory of Open Access Journals (Sweden)

    Kotaro Sakurai

    2014-01-01

    Full Text Available A video event data recorder (VEDR in a motor vehicle records images before and after a traffic accident. This report describes a taxi driver whose seizures were recorded by VEDR, which was extremely useful for the diagnosis of epilepsy. The patient was a 63-year-old right-handed Japanese male taxi driver. He collided with a streetlight. Two years prior to this incident, he raced an engine for a long time while parked. The VEDR enabled confirmation that the accidents depended on an epileptic seizure and he was diagnosed with symptomatic localization-related epilepsy. The VEDR is useful not only for traffic accident evidence; it might also contribute to a driver's health care and road safety.

  9. A System based on Adaptive Background Subtraction Approach for Moving Object Detection and Tracking in Videos

    Directory of Open Access Journals (Sweden)

    Bahadır KARASULU

    2013-04-01

    Full Text Available Video surveillance systems are based on video and image processing research areas in the scope of computer science. Video processing covers various methods which are used to browse the changes in existing scene for specific video. Nowadays, video processing is one of the important areas of computer science. Two-dimensional videos are used to apply various segmentation and object detection and tracking processes which exists in multimedia content-based indexing, information retrieval, visual and distributed cross-camera surveillance systems, people tracking, traffic tracking and similar applications. Background subtraction (BS approach is a frequently used method for moving object detection and tracking. In the literature, there exist similar methods for this issue. In this research study, it is proposed to provide a more efficient method which is an addition to existing methods. According to model which is produced by using adaptive background subtraction (ABS, an object detection and tracking system’s software is implemented in computer environment. The performance of developed system is tested via experimental works with related video datasets. The experimental results and discussion are given in the study

  10. Event storm detection and identification in communication systems

    International Nuclear Information System (INIS)

    Albaghdadi, Mouayad; Briley, Bruce; Evens, Martha

    2006-01-01

    Event storms are the manifestation of an important class of abnormal behaviors in communication systems. They occur when a large number of nodes throughout the system generate a set of events within a small period of time. It is essential for network management systems to detect every event storm and identify its cause, in order to prevent and repair potential system faults. This paper presents a set of techniques for the effective detection and identification of event storms in communication systems. First, we introduce a new algorithm to synchronize events to a single node in the system. Second, the system's event log is modeled as a normally distributed random process. This is achieved by using data analysis techniques to explore and then model the statistical behavior of the event log. Third, event storm detection is proposed using a simple test statistic combined with an exponential smoothing technique to overcome the non-stationary behavior of event logs. Fourth, the system is divided into non-overlapping regions to locate the main contributing regions of a storm. We show that this technique provides us with a method for event storm identification. Finally, experimental results from a commercially deployed multimedia communication system that uses these techniques demonstrate their effectiveness

  11. Facial Video-Based Photoplethysmography to Detect HRV at Rest.

    Science.gov (United States)

    Moreno, J; Ramos-Castro, J; Movellan, J; Parrado, E; Rodas, G; Capdevila, L

    2015-06-01

    Our aim is to demonstrate the usefulness of photoplethysmography (PPG) for analyzing heart rate variability (HRV) using a standard 5-min test at rest with paced breathing, comparing the results with real RR intervals and testing supine and sitting positions. Simultaneous recordings of R-R intervals were conducted with a Polar system and a non-contact PPG, based on facial video recording on 20 individuals. Data analysis and editing were performed with individually designated software for each instrument. Agreement on HRV parameters was assessed with concordance correlations, effect size from ANOVA and Bland and Altman plots. For supine position, differences between video and Polar systems showed a small effect size in most HRV parameters. For sitting position, these differences showed a moderate effect size in most HRV parameters. A new procedure, based on the pixels that contained more heart beat information, is proposed for improving the signal-to-noise ratio in the PPG video signal. Results were acceptable in both positions but better in the supine position. Our approach could be relevant for applications that require monitoring of stress or cardio-respiratory health, such as effort/recuperation states in sports. © Georg Thieme Verlag KG Stuttgart · New York.

  12. Video-based real-time on-street parking occupancy detection system

    Science.gov (United States)

    Bulan, Orhan; Loce, Robert P.; Wu, Wencheng; Wang, YaoRong; Bernal, Edgar A.; Fan, Zhigang

    2013-10-01

    Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5 frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

  13. Do Instructional Videos on Sputum Submission Result in Increased Tuberculosis Case Detection? A Randomized Controlled Trial.

    Science.gov (United States)

    Mhalu, Grace; Hella, Jerry; Doulla, Basra; Mhimbira, Francis; Mtutu, Hawa; Hiza, Helen; Sasamalo, Mohamed; Rutaihwa, Liliana; Rieder, Hans L; Seimon, Tamsyn; Mutayoba, Beatrice; Weiss, Mitchell G; Fenner, Lukas

    2015-01-01

    We examined the effect of an instructional video about the production of diagnostic sputum on case detection of tuberculosis (TB), and evaluated the acceptance of the video. Randomized controlled trial. We prepared a culturally adapted instructional video for sputum submission. We analyzed 200 presumptive TB cases coughing for more than two weeks who attended the outpatient department of the governmental Municipal Hospital in Mwananyamala (Dar es Salaam, Tanzania). They were randomly assigned to either receive instructions on sputum submission using the video before submission (intervention group, n = 100) or standard of care (control group, n = 100). Sputum samples were examined for volume, quality and presence of acid-fast bacilli by experienced laboratory technicians blinded to study groups. Median age was 39.1 years (interquartile range 37.0-50.0); 94 (47%) were females, 106 (53%) were males, and 49 (24.5%) were HIV-infected. We found that the instructional video intervention was associated with detection of a higher proportion of microscopically confirmed cases (56%, 95% confidence interval [95% CI] 45.7-65.9%, sputum smear positive patients in the intervention group versus 23%, 95% CI 15.2-32.5%, in the control group, p sex, modified the effectiveness of the intervention by improving it positively. When asked how well the video instructions were understood, the majority of patients in the intervention group reported to have understood the video instructions well (97%). Most of the patients thought the video would be useful in the cultural setting of Tanzania (92%). Sputum submission instructional videos increased the yield of tuberculosis cases through better quality of sputum samples. If confirmed in larger studies, instructional videos may have a substantial effect on the case yield using sputum microscopy and also molecular tests. This low-cost strategy should be considered as part of the efforts to control TB in resource-limited settings. Pan African

  14. Amplitude Integrated Electroencephalography Compared With Conventional Video EEG for Neonatal Seizure Detection: A Diagnostic Accuracy Study.

    Science.gov (United States)

    Rakshasbhuvankar, Abhijeet; Rao, Shripada; Palumbo, Linda; Ghosh, Soumya; Nagarajan, Lakshmi

    2017-08-01

    This diagnostic accuracy study compared the accuracy of seizure detection by amplitude-integrated electroencephalography with the criterion standard conventional video EEG in term and near-term infants at risk of seizures. Simultaneous recording of amplitude-integrated EEG (2-channel amplitude-integrated EEG with raw trace) and video EEG was done for 24 hours for each infant. Amplitude-integrated EEG was interpreted by a neonatologist; video EEG was interpreted by a neurologist independently. Thirty-five infants were included in the analysis. In the 7 infants with seizures on video EEG, there were 169 seizure episodes on video EEG, of which only 57 were identified by amplitude-integrated EEG. Amplitude-integrated EEG had a sensitivity of 33.7% for individual seizure detection. Amplitude-integrated EEG had an 86% sensitivity for detection of babies with seizures; however, it was nonspecific, in that 50% of infants with seizures detected by amplitude-integrated EEG did not have true seizures by video EEG. In conclusion, our study suggests that amplitude-integrated EEG is a poor screening tool for neonatal seizures.

  15. Detecting failure events in buildings: a numerical and experimental analysis

    OpenAIRE

    Heckman, V. M.; Kohler, M. D.; Heaton, T. H.

    2010-01-01

    A numerical method is used to investigate an approach for detecting the brittle fracture of welds associated with beam -column connections in instrumented buildings in real time through the use of time-reversed Green’s functions and wave propagation reciprocity. The approach makes use of a prerecorded catalog of Green’s functions for an instrumented building to detect failure events in the building during a later seismic event by screening continuous data for the presence of wavef...

  16. Short-term effects of prosocial video games on aggression: an event-related potential study

    OpenAIRE

    Liu, Yanling; Teng, Zhaojun; Lan, Haiying; Zhang, Xin; Yao, Dezhong

    2015-01-01

    Previous research has shown that exposure to violent video games increases aggression, whereas exposure to prosocial video games can reduce aggressive behavior. However, little is known about the neural correlates of these behavioral effects. This work is the first to investigate the electrophysiological features of the relationship between playing a prosocial video game and inhibition of aggressive behavior. Forty-nine subjects played either a prosocial or a neutral video game for 20 min, th...

  17. Vision-based Event Detection of the Sit-to-Stand Transition

    Directory of Open Access Journals (Sweden)

    Victor Shia

    2015-12-01

    Full Text Available Sit-to-stand (STS motions are one of the most important activities of daily living as they serve as a precursor to mobility and walking. However, there exist no standard method of segmenting STS motions. This is partially due to the variety of different sensors and modalities used to study the STS motion such as force plate, vision, and accelerometers, each providing different types of data, and the variability of the STS motion in video data. In this work, we present a method using motion capture to detect events in the STS motion by estimating ground reaction forces, thereby eliminating the variability in joint angles from visual data. We illustrate the accuracy of this method with 10 subjects with an average difference of 16.5ms in event times obtained via motion capture vs force plate. This method serves as a proof of concept for detecting events in the STS motion via video which are comparable to those obtained via force plate.

  18. Shot Boundary Detection in Soccer Video using Twin-comparison Algorithm and Dominant Color Region

    Directory of Open Access Journals (Sweden)

    Matko Šarić

    2008-06-01

    Full Text Available The first step in generic video processing is temporal segmentation, i.e. shot boundary detection. Camera shot transitions can be either abrupt (e.g. cuts or gradual (e.g. fades, dissolves, wipes. Sports video is one of the most challenging domains for robust shot boundary detection. We proposed a shot boundary detection algorithm for soccer video based on the twin-comparison method and the absolute difference between frames in their ratios of dominant colored pixels to total number of pixels. With this approach the detection of gradual transitions is improved by decreasing the number of false positives caused by some camera operations. We also compared performances of our algorithm and the standard twin-comparison method.

  19. Speaker detection for conversational robots using synchrony between audio and video

    NARCIS (Netherlands)

    Noulas, A.; Englebienne, G.; Terwijn, B.; Kröse, B.; Hanheide, M.; Zender, H.

    2010-01-01

    This paper compares different methods for detecting the speaking person when multiple persons are interacting with a robot. We evaluate the state-of-the-art speaker detection methods on the iCat robot. These methods use the synchrony between audio and video to locate the most probable speaker. We

  20. Non-Linguistic Vocal Event Detection Using Online Random

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

    2014-01-01

    areas such as object detection, face recognition, and audio event detection. This paper proposes to use online random forest technique for detecting laughter and filler and for analyzing the importance of various features for non-linguistic vocal event classification through permutation. The results...... show that according to the Area Under Curve measure the online random forest achieved 88.1% compared to 82.9% obtained by the baseline support vector machines for laughter classification and 86.8% to 83.6% for filler classification....

  1. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhangbing Zhou

    2015-12-01

    Full Text Available With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s. When sensory data are collected at sink node(s, the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

  2. Detection and localization of copy-paste forgeries in digital videos.

    Science.gov (United States)

    Singh, Raahat Devender; Aggarwal, Naveen

    2017-12-01

    Amidst the continual march of technology, we find ourselves relying on digital videos to proffer visual evidence in several highly sensitive areas such as journalism, politics, civil and criminal litigation, and military and intelligence operations. However, despite being an indispensable source of information with high evidentiary value, digital videos are also extremely vulnerable to conscious manipulations. Therefore, in a situation where dependence on video evidence is unavoidable, it becomes crucial to authenticate the contents of this evidence before accepting them as an accurate depiction of reality. Digital videos can suffer from several kinds of manipulations, but perhaps, one of the most consequential forgeries is copy-paste forgery, which involves insertion/removal of objects into/from video frames. Copy-paste forgeries alter the information presented by the video scene, which has a direct effect on our basic understanding of what that scene represents, and so, from a forensic standpoint, the challenge of detecting such forgeries is especially significant. In this paper, we propose a sensor pattern noise based copy-paste detection scheme, which is an improved and forensically stronger version of an existing noise-residue based technique. We also study a demosaicing artifact based image forensic scheme to estimate the extent of its viability in the domain of video forensics. Furthermore, we suggest a simplistic clustering technique for the detection of copy-paste forgeries, and determine if it possess the capabilities desired of a viable and efficacious video forensic scheme. Finally, we validate these schemes on a set of realistically tampered MJPEG, MPEG-2, MPEG-4, and H.264/AVC encoded videos in a diverse experimental set-up by varying the strength of post-production re-compressions and transcodings, bitrates, and sizes of the tampered regions. Such an experimental set-up is representative of a neutral testing platform and simulates a real

  3. Real-time pedestrian detection with the videos of car camera

    Directory of Open Access Journals (Sweden)

    Yunling Zhang

    2015-12-01

    Full Text Available Pedestrians in the vehicle path are in danger of being hit, thus causing severe injury to pedestrians and vehicle occupants. Therefore, real-time pedestrian detection with the video of vehicle-mounted camera is of great significance to vehicle–pedestrian collision warning and traffic safety of self-driving car. In this article, a real-time scheme was proposed based on integral channel features and graphics processing unit. The proposed method does not need to resize the input image. Moreover, the computationally expensive convolution of the detectors and the input image was converted into the dot product of two larger matrixes, which can be computed effectively using a graphics processing unit. The experiments showed that the proposed method could be employed to detect pedestrians in the video of car camera at 20+ frames per second with acceptable error rates. Thus, it can be applied in real-time detection tasks with the videos of car camera.

  4. COMPARISON OF FOUR METHODS TO DETECT ADVERSE EVENTS IN HOSPITAL

    Directory of Open Access Journals (Sweden)

    Inge Dhamanti

    2015-09-01

    Full Text Available AbstrakDeteksi terjadinya kejadian yang tidak diharapkan (KTD telah menjadi salah satu tantangan dalam keselamatan pasien oleh karena itu metode untuk mendeteksi terjadinya KTD sangatlah penting untuk meningkatkan keselamatan pasien. Tujuan dari artikel ini adalah untuk membandingkan kelebihan dan kekurangan dari beberapa metode untuk mendeteksi terjadinya KTD di rumah sakit, meliputi review rekam medis, pelaporan insiden secara mandiri, teknologi informasi, dan pelaporan oleh pasien. Studi ini merupakan kajian literatur untuk membandingkan dan menganalisa metode terbaik untuk mendeteksi KTD yang dapat diimplementasikan oleh rumah sakit. Semua dari empat metode telah terbukti mampu untuk mendeteksi terjadinya KTD di rumah sakit, tetapi masing-masing metode mempunyai kelebihan dan kekurangan yang perlu diatasi. Tidak ada satu metode terbaik yang akan memberikan hasil terbaik untuk mendeteksi KTD di rumah sakit. Sehingga untuk mendeteksi lebih banyak KTD yang seharusnya dapat dicegah, atau KTD yang telah terjadi, rumah sakit seharusnya mengkombinasikan lebih dari satu metode untuk mendeteksi, karena masing-masing metode mempunyai sensitivitas berbeda-beda.AbstractDetecting adverse events has become one of the challenges in patient safety thus methods to detect adverse events become critical for improving patient safety. The purpose of this paper is to compare the strengths and weaknesses of several methods of identifying adverse events in hospital, including medical records reviews, self-reported incidents, information technology, and patient self-reports. This study is a literature review to compared and analyzed to determine the best method implemented by the hospital. All of four methods have been proved in their ability in detecting adverse events in hospitals, but each method had strengths and limitations to be overcome. There is no ‘best’ single method that will give the best results for adverse events detection in hospital. Thus to

  5. Deep Learning for Detection of Object-Based Forgery in Advanced Video

    Directory of Open Access Journals (Sweden)

    Ye Yao

    2017-12-01

    Full Text Available Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

  6. People detection in nuclear plants by video processing for safety purpose

    International Nuclear Information System (INIS)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Seixas, Jose M.; Silva, Eduardo Antonio B.; Cota, Raphael E.; Ramos, Bruno L.

    2011-01-01

    This work describes the development of a surveillance system for safety purposes in nuclear plants. The final objective is to track people online in videos, in order to estimate the dose received by personnel, during the execution of working tasks in nuclear plants. The estimation will be based on their tracked positions and on dose rate mapping in a real nuclear plant at Instituto de Engenharia Nuclear, Argonauta nuclear research reactor. Cameras have been installed within Argonauta's room, supplying the data needed. Both video processing and statistical signal processing techniques may be used for detection, segmentation and tracking people in video. This first paper reports people segmentation in video using background subtraction, by two different approaches, namely frame differences, and blind signal separation based on the independent component analysis method. Results are commented, along with perspectives for further work. (author)

  7. People detection in nuclear plants by video processing for safety purpose

    Energy Technology Data Exchange (ETDEWEB)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN), Rio de Janeiro, RJ (Brazil); Seixas, Jose M.; Silva, Eduardo Antonio B., E-mail: seixas@lps.ufrj.b, E-mail: eduardo@lps.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Eletrica; Cota, Raphael E.; Ramos, Bruno L., E-mail: brunolange@poli.ufrj.b [Universidade Federal do Rio de Janeiro (EP/UFRJ), RJ (Brazil). Dept. de Engenharia Eletronica e de Computacao

    2011-07-01

    This work describes the development of a surveillance system for safety purposes in nuclear plants. The final objective is to track people online in videos, in order to estimate the dose received by personnel, during the execution of working tasks in nuclear plants. The estimation will be based on their tracked positions and on dose rate mapping in a real nuclear plant at Instituto de Engenharia Nuclear, Argonauta nuclear research reactor. Cameras have been installed within Argonauta's room, supplying the data needed. Both video processing and statistical signal processing techniques may be used for detection, segmentation and tracking people in video. This first paper reports people segmentation in video using background subtraction, by two different approaches, namely frame differences, and blind signal separation based on the independent component analysis method. Results are commented, along with perspectives for further work. (author)

  8. Handbook of video databases design and applications

    CERN Document Server

    Furht, Borko

    2003-01-01

    INTRODUCTIONIntroduction to Video DatabasesOge Marques and Borko FurhtVIDEO MODELING AND REPRESENTATIONModeling Video Using Input/Output Markov Models with Application to Multi-Modal Event DetectionAshutosh Garg, Milind R. Naphade, and Thomas S. HuangStatistical Models of Video Structure and SemanticsNuno VasconcelosFlavor: A Language for Media RepresentationAlexandros Eleftheriadis and Danny HongIntegrating Domain Knowledge and Visual Evidence to Support Highlight Detection in Sports VideosJuergen Assfalg, Marco Bertini, Carlo Colombo, and Alberto Del BimboA Generic Event Model and Sports Vid

  9. A modular CUDA-based framework for scale-space feature detection in video streams

    International Nuclear Information System (INIS)

    Kinsner, M; Capson, D; Spence, A

    2010-01-01

    Multi-scale image processing techniques enable extraction of features where the size of a feature is either unknown or changing, but the requirement to process image data at multiple scale levels imposes a substantial computational load. This paper describes the architecture and emerging results from the implementation of a GPGPU-accelerated scale-space feature detection framework for video processing. A discrete scale-space representation is generated for image frames within a video stream, and multi-scale feature detection metrics are applied to detect ridges and Gaussian blobs at video frame rates. A modular structure is adopted, in which common feature extraction tasks such as non-maximum suppression and local extrema search may be reused across a variety of feature detectors. Extraction of ridge and blob features is achieved at faster than 15 frames per second on video sequences from a machine vision system, utilizing an NVIDIA GTX 480 graphics card. By design, the framework is easily extended to additional feature classes through the inclusion of feature metrics to be applied to the scale-space representation, and using common post-processing modules to reduce the required CPU workload. The framework is scalable across multiple and more capable GPUs, and enables previously intractable image processing at video frame rates using commodity computational hardware.

  10. An Examination of Three Spatial Event Cluster Detection Methods

    Directory of Open Access Journals (Sweden)

    Hensley H. Mariathas

    2015-03-01

    Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

  11. Analytic 3D image reconstruction using all detected events

    International Nuclear Information System (INIS)

    Kinahan, P.E.; Rogers, J.G.

    1988-11-01

    We present the results of testing a previously presented algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET volume-imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image

  12. Spatial-Temporal Event Detection from Geo-Tagged Tweets

    Directory of Open Access Journals (Sweden)

    Yuqian Huang

    2018-04-01

    Full Text Available As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.

  13. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO

    Directory of Open Access Journals (Sweden)

    Lixin Yan

    2016-07-01

    Full Text Available The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1 the Markov blanket (MB algorithm is employed to extract the main factors associated with hazardous traffic events; (2 a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G have significant influences on hazardous traffic events. The sequential minimal optimization (SMO algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

  14. Event-Triggered Fault Detection of Nonlinear Networked Systems.

    Science.gov (United States)

    Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

    2017-04-01

    This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

  15. Real-time billboard trademark detection and recognition in sports video

    Science.gov (United States)

    Bu, Jiang; Lao, Song-Yan; Bai, Liang

    2013-03-01

    Nowadays, different applications like automatic video indexing, keyword based video search and TV commercials can be developed by detecting and recognizing the billboard trademark. We propose a hierarchical solution for real-time billboard trademark recognition in various sports video, billboard frames are detected in the first level, fuzzy decision tree with easily-computing features are employed to accelerate the process, while in the second level, color and regional SIFT features are combined for the first time to describe the appearance of trademarks, and the shared nearest neighbor (SNN) clustering with x2 distance is utilized instead of traditional K-means clustering to construct the SIFT vocabulary, at last, Latent Semantic Analysis (LSA) based SIFT vocabulary matching is performed on the template trademark and the candidate regions in billboard frame. The preliminary experiments demonstrate the effectiveness of the hierarchical solution, and real time constraints are also met by our solution.

  16. TNO at TRECVID 2013: Multimedia Event Detection and Instance Search

    NARCIS (Netherlands)

    Bouma, H.; Azzopardi, G.; Spitters, M.M.; Wit, J.J. de; Versloot, C.A.; Zon, R.W.L. van der; Eendebak, P.T.; Baan, J.; Hove, R.J.M. ten; Eekeren, A.W.M. van; Haar, F.B. ter; Hollander, R.J.M. den; Huis, R.J. van; Boer, M.H.T. de; Antwerpen, G. van; Broekhuijsen, B.J.; Daniele, L.M.; Brandt, P.; Schavemaker, J.G.M.; Kraaij, W.; Schutte, K.

    2013-01-01

    We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level

  17. Distributed Event Detection in Wireless Sensor Networks for Disaster Management

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.

    2010-01-01

    Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for

  18. On Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Suresh, Mahima Agumbe

    2013-05-01

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

  19. Fast detection and modeling of human-body parts from monocular video

    NARCIS (Netherlands)

    Lao, W.; Han, Jungong; With, de P.H.N.; Perales, F.J.; Fisher, R.B.

    2009-01-01

    This paper presents a novel and fast scheme to detect different body parts in human motion. Using monocular video sequences, trajectory estimation and body modeling of moving humans are combined in a co-operating processing architecture. More specifically, for every individual person, features of

  20. Chemical Detection Architecture for a Subway System [video

    OpenAIRE

    Ignacio, Joselito; Center for Homeland Defense and Security Naval Postgraduate School

    2014-01-01

    This proposed system process aims to improve subway safety through better enabling the rapid detection and response to a chemical release in a subway system. The process is designed to be location-independent and generalized to most subway systems despite each system's unique characteristics.

  1. Do Instructional Videos on Sputum Submission Result in Increased Tuberculosis Case Detection? A Randomized Controlled Trial.

    Directory of Open Access Journals (Sweden)

    Grace Mhalu

    Full Text Available We examined the effect of an instructional video about the production of diagnostic sputum on case detection of tuberculosis (TB, and evaluated the acceptance of the video.Randomized controlled trial.We prepared a culturally adapted instructional video for sputum submission. We analyzed 200 presumptive TB cases coughing for more than two weeks who attended the outpatient department of the governmental Municipal Hospital in Mwananyamala (Dar es Salaam, Tanzania. They were randomly assigned to either receive instructions on sputum submission using the video before submission (intervention group, n = 100 or standard of care (control group, n = 100. Sputum samples were examined for volume, quality and presence of acid-fast bacilli by experienced laboratory technicians blinded to study groups.Median age was 39.1 years (interquartile range 37.0-50.0; 94 (47% were females, 106 (53% were males, and 49 (24.5% were HIV-infected. We found that the instructional video intervention was associated with detection of a higher proportion of microscopically confirmed cases (56%, 95% confidence interval [95% CI] 45.7-65.9%, sputum smear positive patients in the intervention group versus 23%, 95% CI 15.2-32.5%, in the control group, p <0.0001, an increase in volume of specimen defined as a volume ≥3ml (78%, 95% CI 68.6-85.7%, versus 45%, 95% CI 35.0-55.3%, p <0.0001, and specimens less likely to be salivary (14%, 95% CI 7.9-22.4%, versus 39%, 95% CI 29.4-49.3%, p = 0.0001. Older age, but not the HIV status or sex, modified the effectiveness of the intervention by improving it positively. When asked how well the video instructions were understood, the majority of patients in the intervention group reported to have understood the video instructions well (97%. Most of the patients thought the video would be useful in the cultural setting of Tanzania (92%.Sputum submission instructional videos increased the yield of tuberculosis cases through better quality of sputum

  2. Efficiently detecting outlying behavior in video-game players.

    Science.gov (United States)

    Kim, Young Bin; Kang, Shin Jin; Lee, Sang Hyeok; Jung, Jang Young; Kam, Hyeong Ryeol; Lee, Jung; Kim, Young Sun; Lee, Joonsoo; Kim, Chang Hun

    2015-01-01

    In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

  3. Efficiently detecting outlying behavior in video-game players

    Directory of Open Access Journals (Sweden)

    Young Bin Kim

    2015-12-01

    Full Text Available In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

  4. National Earthquake Information Center Seismic Event Detections on Multiple Scales

    Science.gov (United States)

    Patton, J.; Yeck, W. L.; Benz, H.; Earle, P. S.; Soto-Cordero, L.; Johnson, C. E.

    2017-12-01

    The U.S. Geological Survey National Earthquake Information Center (NEIC) monitors seismicity on local, regional, and global scales using automatic picks from more than 2,000 near-real time seismic stations. This presents unique challenges in automated event detection due to the high variability in data quality, network geometries and density, and distance-dependent variability in observed seismic signals. To lower the overall detection threshold while minimizing false detection rates, NEIC has begun to test the incorporation of new detection and picking algorithms, including multiband (Lomax et al., 2012) and kurtosis (Baillard et al., 2014) pickers, and a new bayesian associator (Glass 3.0). The Glass 3.0 associator allows for simultaneous processing of variably scaled detection grids, each with a unique set of nucleation criteria (e.g., nucleation threshold, minimum associated picks, nucleation phases) to meet specific monitoring goals. We test the efficacy of these new tools on event detection in networks of various scales and geometries, compare our results with previous catalogs, and discuss lessons learned. For example, we find that on local and regional scales, rapid nucleation of small events may require event nucleation with both P and higher-amplitude secondary phases (e.g., S or Lg). We provide examples of the implementation of a scale-independent associator for an induced seismicity sequence (local-scale), a large aftershock sequence (regional-scale), and for monitoring global seismicity. Baillard, C., Crawford, W. C., Ballu, V., Hibert, C., & Mangeney, A. (2014). An automatic kurtosis-based P-and S-phase picker designed for local seismic networks. Bulletin of the Seismological Society of America, 104(1), 394-409. Lomax, A., Satriano, C., & Vassallo, M. (2012). Automatic picker developments and optimization: FilterPicker - a robust, broadband picker for real-time seismic monitoring and earthquake early-warning, Seism. Res. Lett. , 83, 531-540, doi: 10

  5. Αutomated 2D shoreline detection from coastal video imagery: an example from the island of Crete

    Science.gov (United States)

    Velegrakis, A. F.; Trygonis, V.; Vousdoukas, M. I.; Ghionis, G.; Chatzipavlis, A.; Andreadis, O.; Psarros, F.; Hasiotis, Th.

    2015-06-01

    Beaches are both sensitive and critical coastal system components as they: (i) are vulnerable to coastal erosion (due to e.g. wave regime changes and the short- and long-term sea level rise) and (ii) form valuable ecosystems and economic resources. In order to identify/understand the current and future beach morphodynamics, effective monitoring of the beach spatial characteristics (e.g. the shoreline position) at adequate spatio-temporal resolutions is required. In this contribution we present the results of a new, fully-automated detection method of the (2-D) shoreline positions using high resolution video imaging from a Greek island beach (Ammoudara, Crete). A fully-automated feature detection method was developed/used to monitor the shoreline position in geo-rectified coastal imagery obtained through a video system set to collect 10 min videos every daylight hour with a sampling rate of 5 Hz, from which snapshot, time-averaged (TIMEX) and variance images (SIGMA) were generated. The developed coastal feature detector is based on a very fast algorithm using a localised kernel that progressively grows along the SIGMA or TIMEX digital image, following the maximum backscatter intensity along the feature of interest; the detector results were found to compare very well with those obtained from a semi-automated `manual' shoreline detection procedure. The automated procedure was tested on video imagery obtained from the eastern part of Ammoudara beach in two 5-day periods, a low wave energy period (6-10 April 2014) and a high wave energy period (1 -5 November 2014). The results showed that, during the high wave energy event, there have been much higher levels of shoreline variance which, however, appeared to be similarly unevenly distributed along the shoreline as that related to the low wave energy event, Shoreline variance `hot spots' were found to be related to the presence/architecture of an offshore submerged shallow beachrock reef, found at a distance of 50-80 m

  6. Improved people detection in nuclear plants by video processing for safety purpose

    Energy Technology Data Exchange (ETDEWEB)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Carvalho, Paulo Victor R., E-mail: calexandre@ien.gov.br, E-mail: mol@ien.gov.br, E-mail: paulov@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Seixas, Jose M.; Silva, Eduardo Antonio B., E-mail: seixas@lps.ufrj.br, E-mail: eduardo@smt.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Eletrica; Waintraub, Fabio, E-mail: fabiowaintraub@hotmail.com [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Escola Politecnica. Departamento de Engenharia Eletronica e de Computacao

    2013-07-01

    This work describes improvements in a surveillance system for safety purposes in nuclear plants. The objective is to track people online in video, in order to estimate the dose received by personnel, during working tasks executed in nuclear plants. The estimation will be based on their tracked positions and on dose rate mapping in a nuclear research reactor, Argonauta. Cameras have been installed within Argonauta room, supplying the data needed. Video processing methods were combined for detecting and tracking people in video. More specifically, segmentation, performed by background subtraction, was combined with a tracking method based on color distribution. The use of both methods improved the overall results. An alternative approach was also evaluated, by means of blind source signal separation. Results are commented, along with perspectives. (author)

  7. Improved people detection in nuclear plants by video processing for safety purpose

    International Nuclear Information System (INIS)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Carvalho, Paulo Victor R.; Seixas, Jose M.; Silva, Eduardo Antonio B.; Waintraub, Fabio

    2013-01-01

    This work describes improvements in a surveillance system for safety purposes in nuclear plants. The objective is to track people online in video, in order to estimate the dose received by personnel, during working tasks executed in nuclear plants. The estimation will be based on their tracked positions and on dose rate mapping in a nuclear research reactor, Argonauta. Cameras have been installed within Argonauta room, supplying the data needed. Video processing methods were combined for detecting and tracking people in video. More specifically, segmentation, performed by background subtraction, was combined with a tracking method based on color distribution. The use of both methods improved the overall results. An alternative approach was also evaluated, by means of blind source signal separation. Results are commented, along with perspectives. (author)

  8. Towards Detecting the Crowd Involved in Social Events

    Directory of Open Access Journals (Sweden)

    Wei Huang

    2017-10-01

    Full Text Available Knowing how people interact with urban environments is fundamental for a variety of fields, ranging from transportation to social science. Despite the fact that human mobility patterns have been a major topic of study in recent years, a challenge to understand large-scale human behavior when a certain event occurs remains due to a lack of either relevant data or suitable approaches. Psychological crowd refers to a group of people who are usually located at different places and show different behaviors, but who are very sensitively driven to take the same act (gather together by a certain event, which has been theoretically studied by social psychologists since the 19th century. This study aims to propose a computational approach using a machine learning method to model psychological crowds, contributing to the better understanding of human activity patterns under events. Psychological features and mental unity of the crowd are computed to detect the involved individuals. A national event happening across the USA in April, 2015 is analyzed using geotagged tweets as a case study to test our approach. The result shows that 81% of individuals in the crowd can be successfully detected. Through investigating the geospatial pattern of the involved users, not only can the event related users be identified but also those unobserved users before the event can be uncovered. The proposed approach can effectively represent the psychological feature and measure the mental unity of the psychological crowd, which sheds light on the study of large-scale psychological crowd and provides an innovative way to understanding human behavior under events.

  9. Neutron detector for detecting rare events of spontaneous fission

    International Nuclear Information System (INIS)

    Ter-Akop'yan, G.M.; Popeko, A.G.; Sokol, E.A.; Chelnokov, L.P.; Smirnov, V.I.; Gorshkov, V.A.

    1981-01-01

    The neutron detector for registering rare events of spontaneous fission by detecting multiple neutron emission is described. The detector represents a block of plexiglas of 550 mm diameter and 700 mm height in the centre of which there is a through 160 mm diameter channel for the sample under investigation. The detector comprises 56 3 He filled counters (up to 7 atm pressure) with 1% CO 2 addition. The counters have a 500 mm length and a 32 mm diameter. The sampling of fission events is realized by an electron system which allows determining the number of detected neutrons, numbers of operated counters, signal amplitude and time for fission event detecting. A block diagram of a neutron detector electron system is presented and its operation principle is considered. For protection against cosmic radiation the detector is surronded by a system of plastic scintillators and placed behind the concrete shield of 6 m thickness. The results of measurements of background radiation are given. It has been found that the background radiation of single neutron constitutes about 150 counts per hour, the detecting efficiency of single neutron equals 0.483 +- 0.005, for a 10l detector sensitive volume. By means of the detector described the parameters of multiplicity distribution of prompt neutrons for 256 Fm spontaneous fission are measured. The average multiplicity equals 3.59+-0.06 the dispersion being 2.30+-0.65

  10. Detection and interpretation of seismoacoustic events at German infrasound stations

    Science.gov (United States)

    Pilger, Christoph; Koch, Karl; Ceranna, Lars

    2016-04-01

    Three infrasound arrays with collocated or nearby installed seismometers are operated by the Federal Institute for Geosciences and Natural Resources (BGR) as the German National Data Center (NDC) for the verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Infrasound generated by seismoacoustic events is routinely detected at these infrasound arrays, but air-to-ground coupled acoustic waves occasionally show up in seismometer recordings as well. Different natural and artificial sources like meteoroids as well as industrial and mining activity generate infrasonic signatures that are simultaneously detected at microbarometers and seismometers. Furthermore, many near-surface sources like earthquakes and explosions generate both seismic and infrasonic waves that can be detected successively with both technologies. The combined interpretation of seismic and acoustic signatures provides additional information about the origin time and location of remote infrasound events or about the characterization of seismic events distinguishing man-made and natural origins. Furthermore, seismoacoustic studies help to improve the modelling of infrasound propagation and ducting in the atmosphere and allow quantifying the portion of energy coupled into ground and into air by seismoacoustic sources. An overview of different seismoacoustic sources and their detection by German infrasound stations as well as some conclusions on the benefit of a combined seismoacoustic analysis are presented within this study.

  11. Detecting Seismic Events Using a Supervised Hidden Markov Model

    Science.gov (United States)

    Burks, L.; Forrest, R.; Ray, J.; Young, C.

    2017-12-01

    We explore the use of supervised hidden Markov models (HMMs) to detect seismic events in streaming seismogram data. Current methods for seismic event detection include simple triggering algorithms, such as STA/LTA and the Z-statistic, which can lead to large numbers of false positives that must be investigated by an analyst. The hypothesis of this study is that more advanced detection methods, such as HMMs, may decreases false positives while maintaining accuracy similar to current methods. We train a binary HMM classifier using 2 weeks of 3-component waveform data from the International Monitoring System (IMS) that was carefully reviewed by an expert analyst to pick all seismic events. Using an ensemble of simple and discrete features, such as the triggering of STA/LTA, the HMM predicts the time at which transition occurs from noise to signal. Compared to the STA/LTA detection algorithm, the HMM detects more true events, but the false positive rate remains unacceptably high. Future work to potentially decrease the false positive rate may include using continuous features, a Gaussian HMM, and multi-class HMMs to distinguish between types of seismic waves (e.g., P-waves and S-waves). Acknowledgement: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.SAND No: SAND2017-8154 A

  12. Hybrid Video Stabilization for Mobile Vehicle Detection on SURF in Aerial Surveillance

    Directory of Open Access Journals (Sweden)

    Gao Chunxian

    2015-01-01

    Full Text Available Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.

  13. Polygraph lie detection on real events in a laboratory setting.

    Science.gov (United States)

    Bradley, M T; Cullen, M C

    1993-06-01

    This laboratory study dealt with real-life intense emotional events. Subjects generated embarrassing stories from their experience, then submitted to polygraph testing and, by lying, denied their stories and, by telling the truth, denied a randomly assigned story. Money was given as an incentive to be judged innocent on each story. An interrogator, blind to the stories, used Control Question Tests and found subjects more deceptive when lying than when truthful. Stories interacted with order such that lying on the second story was more easily detected than lying on the first. Embarrassing stories provide an alternative to the use of mock crimes to study lie detection in the laboratory.

  14. Complexity of deciding detectability in discrete event systems

    Czech Academy of Sciences Publication Activity Database

    Masopust, Tomáš

    2018-01-01

    Roč. 93, July (2018), s. 257-261 ISSN 0005-1098 Institutional support: RVO:67985840 Keywords : discrete event systems * finite automata * detectability Subject RIV: BA - General Mathematics OBOR OECD: Computer science s, information science , bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 5.451, year: 2016 https://www. science direct.com/ science /article/pii/S0005109818301730

  15. Complexity of deciding detectability in discrete event systems

    Czech Academy of Sciences Publication Activity Database

    Masopust, Tomáš

    2018-01-01

    Roč. 93, July (2018), s. 257-261 ISSN 0005-1098 Institutional support: RVO:67985840 Keywords : discrete event systems * finite automata * detectability Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 5.451, year: 2016 https://www.sciencedirect.com/science/article/pii/S0005109818301730

  16. Microseismic Events Detection on Xishancun Landslide, Sichuan Province, China

    Science.gov (United States)

    Sheng, M.; Chu, R.; Wei, Z.

    2016-12-01

    On landslide, the slope movement and the fracturing of the rock mass often lead to microearthquakes, which are recorded as weak signals on seismographs. The distribution characteristics of temporal and spatial regional unstability as well as the impact of external factors on the unstable regions can be understand and analyzed by monitoring those microseismic events. Microseismic method can provide some information inside the landslide, which can be used as supplementary of geodetic methods for monitoring the movement of landslide surface. Compared to drilling on landslide, microseismic method is more economical and safe. Xishancun Landslide is located about 60km northwest of Wenchuan earthquake centroid, it keep deforming after the earthquake, which greatly increases the probability of disasters. In the autumn of 2015, 30 seismometers were deployed on the landslide for 3 months with intervals of 200 500 meters. First, we used regional earthquakes for time correction of seismometers to eliminate the influence of inaccuracy GPS clocks and the subsurface structure of stations. Due to low velocity of the loose medium, the travel time difference of microseismic events on the landslide up to 5s. According to travel time and waveform characteristics, we found many microseismic events and converted them into envelopes as templates, then we used a sliding-window cross-correlation technique based on waveform envelope to detect the other microseismic events. Consequently, 100 microseismic events were detected with the waveforms recorded on all seismometers. Based on the location, we found most of them located on the front of the landslide while the others located on the back end. The bottom and top of the landslide accumulated considerable energy and deformed largely, radiated waves could be recorded by all stations. What's more, the bottom with more events seemed very active. In addition, there were many smaller events happened in middle part of the landslide where released

  17. Towards Optimal Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Agumbe Suresh, Mahima

    2012-01-03

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.

  18. T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

    OpenAIRE

    Kang, Kai; Li, Hongsheng; Yan, Junjie; Zeng, Xingyu; Yang, Bin; Xiao, Tong; Zhang, Cong; Wang, Zhe; Wang, Ruohui; Wang, Xiaogang; Ouyang, Wanli

    2016-01-01

    The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet and VGG, novel object detection frameworks such as R-CNN and its successors, Fast R-CNN and Faster R-CNN, play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and context...

  19. Pedestrian detection in video surveillance using fully convolutional YOLO neural network

    Science.gov (United States)

    Molchanov, V. V.; Vishnyakov, B. V.; Vizilter, Y. V.; Vishnyakova, O. V.; Knyaz, V. A.

    2017-06-01

    More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

  20. Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks.

    Directory of Open Access Journals (Sweden)

    Zobeida Jezabel Guzman-Zavaleta

    Full Text Available Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness. Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes

  1. Event Detection Intelligent Camera: Demonstration of flexible, real-time data taking and processing

    Energy Technology Data Exchange (ETDEWEB)

    Szabolics, Tamás, E-mail: szabolics.tamas@wigner.mta.hu; Cseh, Gábor; Kocsis, Gábor; Szepesi, Tamás; Zoletnik, Sándor

    2015-10-15

    Highlights: • We present EDICAM's operation principles description. • Firmware tests results. • Software test results. • Further developments. - Abstract: An innovative fast camera (EDICAM – Event Detection Intelligent CAMera) was developed by MTA Wigner RCP in the last few years. This new concept was designed for intelligent event driven processing to be able to detect predefined events and track objects in the plasma. The camera provides a moderate frame rate of 400 Hz at full frame resolution (1280 × 1024), and readout of smaller region of interests can be done in the 1–140 kHz range even during exposure of the full image. One of the most important advantages of this hardware is a 10 Gbit/s optical link which ensures very fast communication and data transfer between the PC and the camera, enabling two level of processing: primitive algorithms in the camera hardware and high-level processing in the PC. This camera hardware has successfully proven to be able to monitoring the plasma in several fusion devices for example at ASDEX Upgrade, KSTAR and COMPASS with the first version of firmware. A new firmware and software package is under development. It allows to detect predefined events in real time and therefore the camera is capable to change its own operation or to give warnings e.g. to the safety system of the experiment. The EDICAM system can handle a huge amount of data (up to TBs) with high data rate (950 MB/s) and will be used as the central element of the 10 camera overview video diagnostic system of Wendenstein 7-X (W7-X) stellarator. This paper presents key elements of the newly developed built-in intelligence stressing the revolutionary new features and the results of the test of the different software elements.

  2. CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

    Science.gov (United States)

    Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

    2018-01-01

    Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

  3. An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

    Directory of Open Access Journals (Sweden)

    B. Ravi Kiran

    2018-02-01

    Full Text Available Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.

  4. Machine learning for the automatic detection of anomalous events

    Science.gov (United States)

    Fisher, Wendy D.

    In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

  5. Detection of red tide events in the Ariake Sound, Japan

    Science.gov (United States)

    Ishizaka, Joji

    2003-05-01

    High resolution SeaWiFS data was used to detect a red tide event occurred in the Ariake Sound, Japan, in winter of 2000 to 2001. The area is small embayment surrounding by tidal flat, and it is known as one of the most productive areas in coast of Japan. The red tide event damaged to seaweed (Nori) culture, and the relation to the reclamation at the Isahaya Bay in the Sound has been discussed. SeaWiFS chlorophyll data showed the red tide started early December 2000, from the Isahaya Bay, although direct relationship to the reclamation was not clear. The red tide persisted to the end of February. Monthly average of SeaWiFS data from May 1998 to December 2001 indicated that the chlorophyll increased twice a year, early summer and fall after the rain. The red tide event was part of the fall bloom which started later and continued longer than other years. Ocean color is useful to detect the red tide; however, it is required to improve the algorithms to accurately estimate chlorophyll in high turbid water and to discriminate toxic flagellates.

  6. Reports on Polysomnograph Combined with Long-term Video Electroencephalogram for Monitoring Nocturnal Sleep-breath Events in 82 Epileptic Patients

    Directory of Open Access Journals (Sweden)

    Hongliang Li

    2013-06-01

    Full Text Available Objective: To investigate the effects of epileptic discharges in sleep of epileptic patients on sleepbreath events. Methods: Polysomnograph (PSG and long-term video electroencephalogram (LTVEEG were used to monitor 82 adult epileptic patients. The condition of paroxysmal events in nocturnal sleep was analyzed, and the epileptiform discharge and effects of antiepileptic drugs were explored. Results: In epileptic group, latency to persistent sleep (LPS and REM sleep latency increased, the proportion of light sleep increased while that of deep sleep decreased, sleep efficiency reduced, nocturnal arousal times increased and apnea hyponea indexes (AHI improved, which demonstrated significant differences by comparison to control group. Periodic leg movements (PLM had no conspicuous differences compared with control group. There were no specific effects of epileptiform discharge and antiepileptic drugs on AHI and PLM indexes. Conclusion: Epileptic patients have sleep structure disorders and sleep-disordered breathing, and arousal, respiratory and leg movement events influence mutually. Synchronous detection of PSG combined with LTVEEG is in favor of comprehensively analyzing the relationship between sleep structures and epilepsy-breath events.

  7. LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

    Directory of Open Access Journals (Sweden)

    Y. Li

    2016-06-01

    Full Text Available Mobile Mapping System (MMS simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the

  8. Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets

    Science.gov (United States)

    Bazzica, A.; van Gemert, J. C.; Liem, C. C. S.; Hanjalic, A.

    2017-05-01

    Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available in the audio domain, \\eg identifying which musicians are playing in large musical ensembles. In this paper, we consider a vision-based approach to note onset detection. As a case study we focus on challenging, real-world clarinetist videos and carry out preliminary experiments on a 3D convolutional neural network based on multiple streams and purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5 hours of clarinetist videos together with cleaned annotations which include about 36,000 onsets and the coordinates for a number of salient points and regions of interest. By performing several training trials on our dataset, we learned that the problem is challenging. We found that the CNN model is highly sensitive to the optimization algorithm and hyper-parameters, and that treating the problem as binary classification may prevent the joint optimization of precision and recall. To encourage further research, we publicly share our dataset, annotations and all models and detail which issues we came across during our preliminary experiments.

  9. Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling.

    Directory of Open Access Journals (Sweden)

    Laurissa Tokarchuk

    Full Text Available In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The

  10. Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling.

    Science.gov (United States)

    Tokarchuk, Laurissa; Wang, Xinyue; Poslad, Stefan

    2017-01-01

    In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM) framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The novel combination of

  11. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  12. Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

    Directory of Open Access Journals (Sweden)

    Baojun Zhao

    2018-03-01

    Full Text Available With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP.

  13. Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

    Science.gov (United States)

    Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

    2018-03-04

    With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

  14. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

    Directory of Open Access Journals (Sweden)

    Rami Alazrai

    2017-03-01

    Full Text Available This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD. Furthermore, we have developed a histogram-based representation (HBR based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.

  15. Moving object detection in top-view aerial videos improved by image stacking

    Science.gov (United States)

    Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

    2017-08-01

    Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

  16. Using Eulerian video magnification to enhance detection of fasciculations in people with amyotrophic lateral sclerosis.

    Science.gov (United States)

    Van Hillegondsberg, Ludo; Carr, Jonathan; Brey, Naeem; Henning, Franclo

    2017-12-01

    This study seeks to determine whether the use of Eulerian video magnification (EVM) increases the detection of muscle fasciculations in people with amyotrophic lateral sclerosis (PALS) compared with direct clinical observation (DCO). Thirty-second-long video recordings were taken of 9 body regions of 7 PALS and 7 controls, and fasciculations were counted by DCO during the same 30-s period. The video recordings were then motion magnified and reviewed by 2 independent assessors. In PALS, median fasciculation count per body region was 1 by DCO (range 0-10) and 3 in the EVM recordings (range 0-15; P < 0.0001). EVM revealed more fasciculations than DCO in 61% of recordings. In controls, median fasciculation count was 0 for both DCO and EVM. Compared with DCO, EVM significantly increased the detection of fasciculations in body regions of PALS. When it is used to supplement clinical examination, EVM has the potential to facilitate the diagnosis of ALS. Muscle Nerve 56: 1063-1067, 2017. © 2017 Wiley Periodicals, Inc.

  17. Feathering effect detection and artifact agglomeration index-based video deinterlacing technique

    Science.gov (United States)

    Martins, André Luis; Rodrigues, Evandro Luis Linhari; de Paiva, Maria Stela Veludo

    2018-03-01

    Several video deinterlacing techniques have been developed, and each one presents a better performance in certain conditions. Occasionally, even the most modern deinterlacing techniques create frames with worse quality than primitive deinterlacing processes. This paper validates that the final image quality can be improved by combining different types of deinterlacing techniques. The proposed strategy is able to select between two types of deinterlaced frames and, if necessary, make the local correction of the defects. This decision is based on an artifact agglomeration index obtained from a feathering effect detection map. Starting from a deinterlaced frame produced by the "interfield average" method, the defective areas are identified, and, if deemed appropriate, these areas are replaced by pixels generated through the "edge-based line average" method. Test results have proven that the proposed technique is able to produce video frames with higher quality than applying a single deinterlacing technique through getting what is good from intra- and interfield methods.

  18. A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

    Directory of Open Access Journals (Sweden)

    Fan Wang

    2014-01-01

    Full Text Available This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

  19. Establishing a Distance Learning Plan for International Space Station (ISS) Interactive Video Education Events (IVEE)

    Science.gov (United States)

    Wallington, Clint

    1999-01-01

    Educational outreach is an integral part of the International Space Station (ISS) mandate. In a few scant years, the International Space Station has already established a tradition of successful, general outreach activities. However, as the number of outreach events increased and began to reach school classrooms, those events came under greater scrutiny by the education community. Some of the ISS electronic field trips, while informative and helpful, did not meet the generally accepted criteria for education events, especially within the context of the classroom. To make classroom outreach events more acceptable to educators, the ISS outreach program must differentiate between communication events (meant to disseminate information to the general public) and education events (designed to facilitate student learning). In contrast to communication events, education events: are directed toward a relatively homogeneous audience who are gathered together for the purpose of learning, have specific performance objectives which the students are expected to master, include a method of assessing student performance, and include a series of structured activities that will help the students to master the desired skill(s). The core of the ISS education events is an interactive videoconference between students and ISS representatives. This interactive videoconference is to be preceded by and followed by classroom activities which help the students aftain the specified learning objectives. Using the interactive videoconference as the centerpiece of the education event lends a special excitement and allows students to ask questions about what they are learning and about the International Space Station and NASA. Whenever possible, the ISS outreach education events should be congruent with national guidelines for student achievement. ISS outreach staff should recognize that there are a number of different groups that will review the events, and that each group has different criteria

  20. Description and detection of burst events in turbulent flows

    Science.gov (United States)

    Schmid, P. J.; García-Gutierrez, A.; Jiménez, J.

    2018-04-01

    A mathematical and computational framework is developed for the detection and identification of coherent structures in turbulent wall-bounded shear flows. In a first step, this data-based technique will use an embedding methodology to formulate the fluid motion as a phase-space trajectory, from which state-transition probabilities can be computed. Within this formalism, a second step then applies repeated clustering and graph-community techniques to determine a hierarchy of coherent structures ranked by their persistencies. This latter information will be used to detect highly transitory states that act as precursors to violent and intermittent events in turbulent fluid motion (e.g., bursts). Used as an analysis tool, this technique allows the objective identification of intermittent (but important) events in turbulent fluid motion; however, it also lays the foundation for advanced control strategies for their manipulation. The techniques are applied to low-dimensional model equations for turbulent transport, such as the self-sustaining process (SSP), for varying levels of complexity.

  1. Immersive video

    Science.gov (United States)

    Moezzi, Saied; Katkere, Arun L.; Jain, Ramesh C.

    1996-03-01

    Interactive video and television viewers should have the power to control their viewing position. To make this a reality, we introduce the concept of Immersive Video, which employs computer vision and computer graphics technologies to provide remote users a sense of complete immersion when viewing an event. Immersive Video uses multiple videos of an event, captured from different perspectives, to generate a full 3D digital video of that event. That is accomplished by assimilating important information from each video stream into a comprehensive, dynamic, 3D model of the environment. Using this 3D digital video, interactive viewers can then move around the remote environment and observe the events taking place from any desired perspective. Our Immersive Video System currently provides interactive viewing and `walkthrus' of staged karate demonstrations, basketball games, dance performances, and typical campus scenes. In its full realization, Immersive Video will be a paradigm shift in visual communication which will revolutionize television and video media, and become an integral part of future telepresence and virtual reality systems.

  2. Shuttlecock detection system for fully-autonomous badminton robot with two high-speed video cameras

    Science.gov (United States)

    Masunari, T.; Yamagami, K.; Mizuno, M.; Une, S.; Uotani, M.; Kanematsu, T.; Demachi, K.; Sano, S.; Nakamura, Y.; Suzuki, S.

    2017-02-01

    Two high-speed video cameras are successfully used to detect the motion of a flying shuttlecock of badminton. The shuttlecock detection system is applied to badminton robots that play badminton fully autonomously. The detection system measures the three dimensional position and velocity of a flying shuttlecock, and predicts the position where the shuttlecock falls to the ground. The badminton robot moves quickly to the position where the shuttle-cock falls to, and hits the shuttlecock back into the opponent's side of the court. In the game of badminton, there is a large audience, and some of them move behind a flying shuttlecock, which are a kind of background noise and makes it difficult to detect the motion of the shuttlecock. The present study demonstrates that such noises can be eliminated by the method of stereo imaging with two high-speed cameras.

  3. Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection.

    Science.gov (United States)

    Sarikaya, Duygu; Corso, Jason J; Guru, Khurshid A

    2017-07-01

    Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.

  4. Enhanced change detection performance reveals improved strategy use in avid action video game players.

    Science.gov (United States)

    Clark, Kait; Fleck, Mathias S; Mitroff, Stephen R

    2011-01-01

    Recent research has shown that avid action video game players (VGPs) outperform non-video game players (NVGPs) on a variety of attentional and perceptual tasks. However, it remains unknown exactly why and how such differences arise; while some prior research has demonstrated that VGPs' improvements stem from enhanced basic perceptual processes, other work indicates that they can stem from enhanced attentional control. The current experiment used a change-detection task to explore whether top-down strategies can contribute to VGPs' improved abilities. Participants viewed alternating presentations of an image and a modified version of the image and were tasked with detecting and localizing the changed element. Consistent with prior claims of enhanced perceptual abilities, VGPs were able to detect the changes while requiring less exposure to the change than NVGPs. Further analyses revealed this improved change detection performance may result from altered strategy use; VGPs employed broader search patterns when scanning scenes for potential changes. These results complement prior demonstrations of VGPs' enhanced bottom-up perceptual benefits by providing new evidence of VGPs' potentially enhanced top-down strategic benefits. Copyright © 2010 Elsevier B.V. All rights reserved.

  5. Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports

    Directory of Open Access Journals (Sweden)

    Mi Chao

    2015-09-01

    Full Text Available With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG features of the human body will show great different between front & back standing (F&B and side standing (Side human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.

  6. Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

    Science.gov (United States)

    Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

    2009-06-01

    In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

  7. Mobile video-to-audio transducer and motion detection for sensory substitution

    Directory of Open Access Journals (Sweden)

    Maxime eAmbard

    2015-10-01

    Full Text Available Visuo-auditory sensory substitution systems are augmented reality devices that translate a video stream into an audio stream in order to help the blind in daily tasks requiring visuo-spatial information. In this work, we present both a new mobile device and a transcoding method specifically designed to sonify moving objects. Frame differencing is used to extract spatial features from the video stream and two-dimensional spatial information is converted into audio cues using pitch, interaural time difference and interaural level difference. Using numerical methods, we attempt to reconstruct visuo-spatial information based on audio signals generated from various video stimuli. We show that despite a contrasted visual background and a highly lossy encoding method, the information in the audio signal is sufficient to allow object localization, object trajectory evaluation, object approach detection, and spatial separation of multiple objects. We also show that this type of audio signal can be interpreted by human users by asking ten subjects to discriminate trajectories based on generated audio signals.

  8. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

    Directory of Open Access Journals (Sweden)

    Jaehoon Jung

    2016-06-01

    Full Text Available This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i automatic camera calibration using both moving objects and a background structure; (ii object depth estimation; and (iii detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems.

  9. Tracking of Vehicle Movement on a Parking Lot Based on Video Detection

    Directory of Open Access Journals (Sweden)

    Ján HALGAŠ

    2014-06-01

    Full Text Available This article deals with topic of transport vehicles identification for dynamic and static transport based on video detection. It explains some of the technologies and approaches necessary for processing of specific image information (transport situation. The paper also describes a design of algorithm for vehicle detection on parking lot and consecutive record of trajectory into virtual environment. It shows a new approach to moving object detection (vehicles, people, and handlers on an enclosed area with emphasis on secure parking. The created application enables automatic identification of trajectory of specific objects moving within the parking area. The application was created in program language C++ with using an open source library OpenCV.

  10. Multi-Model Estimation Based Moving Object Detection for Aerial Video

    Directory of Open Access Journals (Sweden)

    Yanning Zhang

    2015-04-01

    Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

  11. Automated High-Speed Video Detection of Small-Scale Explosives Testing

    Science.gov (United States)

    Ford, Robert; Guymon, Clint

    2013-06-01

    Small-scale explosives sensitivity test data is used to evaluate hazards of processing, handling, transportation, and storage of energetic materials. Accurate test data is critical to implementation of engineering and administrative controls for personnel safety and asset protection. Operator mischaracterization of reactions during testing contributes to either excessive or inadequate safety protocols. Use of equipment and associated algorithms to aid the operator in reaction determination can significantly reduce operator error. Safety Management Services, Inc. has developed an algorithm to evaluate high-speed video images of sparks from an ESD (Electrostatic Discharge) machine to automatically determine whether or not a reaction has taken place. The algorithm with the high-speed camera is termed GoDetect (patent pending). An operator assisted version for friction and impact testing has also been developed where software is used to quickly process and store video of sensitivity testing. We have used this method for sensitivity testing with multiple pieces of equipment. We present the fundamentals of GoDetect and compare it to other methods used for reaction detection.

  12. Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system.

    Science.gov (United States)

    Shieh, Wann-Yun; Huang, Ju-Chin

    2012-09-01

    For most elderly, unpredictable falling incidents may occur at the corner of stairs or a long corridor due to body frailty. If we delay to rescue a falling elder who is likely fainting, more serious consequent injury may occur. Traditional secure or video surveillance systems need caregivers to monitor a centralized screen continuously, or need an elder to wear sensors to detect falling incidents, which explicitly waste much human power or cause inconvenience for elders. In this paper, we propose an automatic falling-detection algorithm and implement this algorithm in a multi-camera video surveillance system. The algorithm uses each camera to fetch the images from the regions required to be monitored. It then uses a falling-pattern recognition algorithm to determine if a falling incident has occurred. If yes, system will send short messages to someone needs to be noticed. The algorithm has been implemented in a DSP-based hardware acceleration board for functionality proof. Simulation results show that the accuracy of falling detection can achieve at least 90% and the throughput of a four-camera surveillance system can be improved by about 2.1 times. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

  13. Efficient hemodynamic event detection utilizing relational databases and wavelet analysis

    Science.gov (United States)

    Saeed, M.; Mark, R. G.

    2001-01-01

    Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.

  14. Detecting and characterising ramp events in wind power time series

    International Nuclear Information System (INIS)

    Gallego, Cristóbal; Cuerva, Álvaro; Costa, Alexandre

    2014-01-01

    In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

  15. Experiences of citizen-based reporting of rainfall events using lab-generated videos

    Science.gov (United States)

    Alfonso, Leonardo; Chacon, Juan

    2016-04-01

    Hydrologic studies rely on the availability of good-quality precipitation estimates. However, in remote areas of the world and particularly in developing countries, ground-based measurement networks are either sparse or nonexistent. This creates difficulties in the estimation of precipitation, which limits the development of hydrologic forecasting and early warning systems for these regions. The EC-FP7 WeSenseIt project aims at exploring the involvement of citizens in the observation of the water cycle with innovative sensor technologies, including mobile telephony. In particular, the project explores the use of a smartphone applications to facilitate the reporting water-related situations. Apart from the challenge of using such information for scientific purposes, the citizen engagement is one of the most important issues to address. To this end effortless methods for reporting need to be developed in order to involve as many people as possible in these experiments. A potential solution to overcome these drawbacks, consisting on lab-controlled rainfall videos have been produced to help mapping the extent and distribution of rainfall fields with minimum effort [1]. In addition, the quality of the collected rainfall information has also been studied [2] by means of different experiments with students. The present research shows the latest results of the application of this method and evaluates the experiences in some cases. [1] Alfonso, L., J. Chacón, and G. Peña-Castellanos (2015), Allowing Citizens to Effortlessly Become Rainfall Sensors, in 36th IAHR World Congress edited, The Hague, the Netherlands [2] Cortes-Arevalo, J., J. Chacón, L. Alfonso, and T. Bogaard (2015), Evaluating data quality collected by using a video rating scale to estimate and report rainfall intensity, in 36th IAHR World Congress edited, The Hague, the Netherlands

  16. Exploring inter-frame correlation analysis and wavelet-domain modeling for real-time caption detection in streaming video

    Science.gov (United States)

    Li, Jia; Tian, Yonghong; Gao, Wen

    2008-01-01

    In recent years, the amount of streaming video has grown rapidly on the Web. Often, retrieving these streaming videos offers the challenge of indexing and analyzing the media in real time because the streams must be treated as effectively infinite in length, thus precluding offline processing. Generally speaking, captions are important semantic clues for video indexing and retrieval. However, existing caption detection methods often have difficulties to make real-time detection for streaming video, and few of them concern on the differentiation of captions from scene texts and scrolling texts. In general, these texts have different roles in streaming video retrieval. To overcome these difficulties, this paper proposes a novel approach which explores the inter-frame correlation analysis and wavelet-domain modeling for real-time caption detection in streaming video. In our approach, the inter-frame correlation information is used to distinguish caption texts from scene texts and scrolling texts. Moreover, wavelet-domain Generalized Gaussian Models (GGMs) are utilized to automatically remove non-text regions from each frame and only keep caption regions for further processing. Experiment results show that our approach is able to offer real-time caption detection with high recall and low false alarm rate, and also can effectively discern caption texts from the other texts even in low resolutions.

  17. Balloon-Borne Infrasound Detection of Energetic Bolide Events

    Science.gov (United States)

    Young, Eliot F.; Ballard, Courtney; Klein, Viliam; Bowman, Daniel; Boslough, Mark

    2016-10-01

    Infrasound is usually defined as sound waves below 20 Hz, the nominal limit of human hearing. Infrasound waves propagate over vast distances through the Earth's atmosphere: the CTBTO (Comprehensive Nuclear-Test-Ban Treaty Organization) has 48 installed infrasound-sensing stations around the world to detect nuclear detonations and other disturbances. In February 2013, several CTBTO infrasound stations detected infrasound signals from a large bolide that exploded over Chelyabinsk, Russia. Some stations recorded signals that had circumnavigated the Earth, over a day after the original event. The goal of this project is to improve upon the sensitivity of the CTBTO network by putting microphones on small, long-duration super-pressure balloons, with the overarching goal of studying the small end of the NEO population by using the Earth's atmosphere as a witness plate.A balloon-borne infrasound sensor is expected to have two advantages over ground-based stations: a lack of wind noise and a concentration of infrasound energy in the "stratospheric duct" between roughly 5 - 50 km altitude. To test these advantages, we have built a small balloon payload with five calibrated microphones. We plan to fly this payload on a NASA high-altitude balloon from Ft Sumner, NM in August 2016. We have arranged for three large explosions to take place in Socorro, NM while the balloon is aloft to assess the sensitivity of balloon-borne vs. ground-based infrasound sensors. We will report on the results from this test flight and the prospects for detecting/characterizing small bolides in the stratosphere.

  18. Multiple Moving Object Detection for Fast Video Content Description in Compressed Domain

    Directory of Open Access Journals (Sweden)

    Boris Mansencal

    2007-11-01

    Full Text Available Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the presence of moving foreground objects, their number, their shape, and their appearance can constitute a good mean for content description. For this reason, we propose to combine both motion information and region-based color segmentation to extract moving objects from an MPEG2 compressed video stream starting only considering low-resolution data. This approach, which we refer to as “rough indexing,” consists in processing P-frame motion information first, and then in performing I-frame color segmentation. Next, since many details can be lost due to the low-resolution data, to improve the object detection results, a novel spatiotemporal filtering has been developed which is constituted by a quadric surface modeling the object trace along time. This method enables to effectively correct possible former detection errors without heavily increasing the computational effort.

  19. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Science.gov (United States)

    Lawhern, Vernon; Hairston, W David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

  20. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Directory of Open Access Journals (Sweden)

    Vernon Lawhern

    Full Text Available Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG data as an additional illustration.

  1. Real-time DSP implementation for MRF-based video motion detection.

    Science.gov (United States)

    Dumontier, C; Luthon, F; Charras, J P

    1999-01-01

    This paper describes the real time implementation of a simple and robust motion detection algorithm based on Markov random field (MRF) modeling, MRF-based algorithms often require a significant amount of computations. The intrinsic parallel property of MRF modeling has led most of implementations toward parallel machines and neural networks, but none of these approaches offers an efficient solution for real-world (i.e., industrial) applications. Here, an alternative implementation for the problem at hand is presented yielding a complete, efficient and autonomous real-time system for motion detection. This system is based on a hybrid architecture, associating pipeline modules with one asynchronous module to perform the whole process, from video acquisition to moving object masks visualization. A board prototype is presented and a processing rate of 15 images/s is achieved, showing the validity of the approach.

  2. Automatic defect detection in video archives: application to Montreux Jazz Festival digital archives

    Science.gov (United States)

    Hanhart, Philippe; Rerabek, Martin; Ivanov, Ivan; Dufaux, Alain; Jones, Caryl; Delidais, Alexandre; Ebrahimi, Touradj

    2013-09-01

    Archival of audio-visual databases has become an important discipline in multimedia. Various defects are typ- ically present in such archives. Among those, one can mention recording related defects such as interference between audio and video signals, optical related artifacts, recording and play out artifacts such as horizontal lines, and dropouts, as well as those due to digitization such as diagonal lines. An automatic or semi-automatic detection to identify such defects is useful, especially for large databases. In this paper, we propose two auto- matic algorithms for detection of horizontal and diagonal lines, as well as dropouts that are among the most typical artifacts encountered. We then evaluate the performance of these algorithms by making use of ground truth scores obtained by human subjects.

  3. Knowledge based query expansion in complex multimedia event detection

    NARCIS (Netherlands)

    Boer, M. de; Schutte, K.; Kraaij, W.

    2016-01-01

    A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap

  4. Knowledge based query expansion in complex multimedia event detection

    NARCIS (Netherlands)

    Boer, M.H.T. de; Schutte, K.; Kraaij, W.

    2015-01-01

    A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap

  5. Acoustic Neuroma Educational Video

    Medline Plus

    Full Text Available ... for me? Find a Group Upcoming Events Video Library Photo Gallery One-on-One Support ANetwork Peer ... me? Find a group Back Upcoming events Video Library Photo Gallery One-on-One Support Back ANetwork ...

  6. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

    Science.gov (United States)

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

  7. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

    Directory of Open Access Journals (Sweden)

    Florian Eyben

    Full Text Available Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

  8. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Yuchou Chang

    2008-02-01

    Full Text Available Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  9. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Hong Yi

    2008-01-01

    Full Text Available Abstract Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  10. Speech Auditory Alerts Promote Memory for Alerted Events in a Video-Simulated Self-Driving Car Ride.

    Science.gov (United States)

    Nees, Michael A; Helbein, Benji; Porter, Anna

    2016-05-01

    Auditory displays could be essential to helping drivers maintain situation awareness in autonomous vehicles, but to date, few or no studies have examined the effectiveness of different types of auditory displays for this application scenario. Recent advances in the development of autonomous vehicles (i.e., self-driving cars) have suggested that widespread automation of driving may be tenable in the near future. Drivers may be required to monitor the status of automation programs and vehicle conditions as they engage in secondary leisure or work tasks (entertainment, communication, etc.) in autonomous vehicles. An experiment compared memory for alerted events-a component of Level 1 situation awareness-using speech alerts, auditory icons, and a visual control condition during a video-simulated self-driving car ride with a visual secondary task. The alerts gave information about the vehicle's operating status and the driving scenario. Speech alerts resulted in better memory for alerted events. Both auditory display types resulted in less perceived effort devoted toward the study tasks but also greater perceived annoyance with the alerts. Speech auditory displays promoted Level 1 situation awareness during a simulation of a ride in a self-driving vehicle under routine conditions, but annoyance remains a concern with auditory displays. Speech auditory displays showed promise as a means of increasing Level 1 situation awareness of routine scenarios during an autonomous vehicle ride with an unrelated secondary task. © 2016, Human Factors and Ergonomics Society.

  11. Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

    Directory of Open Access Journals (Sweden)

    Narasimha Reddy Soora

    2016-01-01

    Full Text Available Most of the existing license plate (LP detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases.

  12. Optimal detection of burst events in gravitational wave interferometric observatories

    International Nuclear Information System (INIS)

    Vicere, Andrea

    2002-01-01

    We consider the problem of detecting a burst signal of unknown shape in the data from gravitational wave interferometric detectors. We introduce a statistic which generalizes the excess power statistic proposed first by Flanagan and Hughes, and then extended by Anderson et al. to the multiple detector case. The statistic that we propose is shown to be optimal for an arbitrary noise spectral characteristic, under the two hypotheses that the noise is Gaussian, albeit colored, and that the prior for the signal is uniform. The statistic derivation is based on the assumption that a signal affects only N parallel samples in the data stream, but that no other information is a priori available, and that the value of the signal at each sample can be arbitrary. This is the main difference from previous works, where different assumptions were made, such as a signal distribution uniform with respect to the metric induced by the (inverse) noise correlation matrix. The two choices are equivalent if the noise is white, and in that limit the two statistics do indeed coincide. In the general case, we believe that the statistic we propose may be more appropriate, because it does not reflect the characteristics of the noise affecting the detector on the supposed distribution of the gravitational wave signal. Moreover, we show that the proposed statistic can be easily implemented in its exact form, combining standard time-series analysis tools which can be efficiently implemented. We generalize this version of an excess power statistic to the multiple detector case, considering first a noise uncorrelated among the different instruments, and then including the effect of correlated noise. We discuss exact and approximate forms of the statistic; the choice depends on the characteristics of the noise and on the assumed length of the burst event. As an example, we show the sensitivity of the network of interferometers to a δ-function burst

  13. Using Genetic Algorithm for Eye Detection and Tracking in Video Sequence

    Directory of Open Access Journals (Sweden)

    Takuya Akashi

    2007-04-01

    Full Text Available We propose a high-speed size and orientation invariant eye tracking method, which can acquire numerical parameters to represent the size and orientation of the eye. In this paper, we discuss that high tolerance in human head movement and real-time processing that are needed for many applications, such as eye gaze tracking. The generality of the method is also important. We use template matching with genetic algorithm, in order to overcome these problems. A high speed and accuracy tracking scheme using Evolutionary Video Processing for eye detection and tracking is proposed. Usually, a genetic algorithm is unsuitable for a real-time processing, however, we achieved real-time processing. The generality of this proposed method is provided by the artificial iris template used. In our simulations, an eye tracking accuracy is 97.9% and, an average processing time of 28 milliseconds per frame.

  14. Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.

    Science.gov (United States)

    Pampouchidou, Anastasia; Marias, Kostas; Tsiknakis, Manolis; Simos, Panagiotis; Fan Yang; Lemaitre, Guillaume; Meriaudeau, Fabrice

    2016-08-01

    Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6% classification accuracy, and the person-independed one yielding promising preliminary results of 74.5% accuracy. The paper concludes with open issues, proposed solutions, and future plans.

  15. VISDTA: A video imaging system for detection, tracking, and assessment: Prototype development and concept demonstration

    Energy Technology Data Exchange (ETDEWEB)

    Pritchard, D.A.

    1987-05-01

    It has been demonstrated that thermal imagers are an effective surveillance and assessment tool for security applications because: (1) they work day or night due to their sensitivity to thermal signatures; (2) penetrability through fog, rain, dust, etc., is better than human eyes; (3) short or long range operation is possible with various optics; and (4) they are strictly passive devices providing visible imagery which is readily interpreted by the operator with little training. Unfortunately, most thermal imagers also require the setup of a tripod, connection of batteries, cables, display, etc. When this is accomplished, the operator must manually move the camera back and forth searching for signs of aggressor activity. VISDTA is designed to provide automatic panning, and in a sense, ''watch'' the imagery in place of the operator. The idea behind the development of VISDTA is to provide a small, portable, rugged system to automatically scan areas and detect targets by computer processing of images. It would use a thermal imager and possibly an intensified day/night TV camera, a pan/ tilt mount, and a computer for system control. If mounted on a dedicated vehicle or on a tower, VISDTA will perform video motion detection functions on incoming video imagery, and automatically scan predefined patterns in search of abnormal conditions which may indicate attempted intrusions into the field-of-regard. In that respect, VISDTA is capable of improving the ability of security forces to maintain security of a given area of interest by augmenting present techniques and reducing operator fatigue.

  16. Developing Fluorescence Sensor Systems for Early Detection of Nitrification Events in Chloraminated Drinking Water Distribution Systems

    Science.gov (United States)

    Detection of nitrification events in chloraminated drinking water distribution systems remains an ongoing challenge for many drinking water utilities, including Dallas Water Utilities (DWU) and the City of Houston (CoH). Each year, these utilities experience nitrification events ...

  17. An On-Line Method for Thermal Diffusivity Detection of Thin Films Using Infrared Video

    Directory of Open Access Journals (Sweden)

    Dong Huilong

    2016-03-01

    Full Text Available A novel method for thermal diffusivity evolution of thin-film materials with pulsed Gaussian beam and infrared video is reported. Compared with common pulse methods performed in specialized labs, the proposed method implements a rapid on-line measurement without producing the off-centre detection error. Through mathematical deduction of the original heat conduction model, it is discovered that the area s, which is encircled by the maximum temperature curve rTMAX(θ, increases linearly over elapsed time. The thermal diffusivity is acquired from the growth rate of the area s. In this study, the off-centre detection error is avoided by performing the distance regularized level set evolution formulation. The area s was extracted from the binary images of temperature variation rate, without inducing errors from determination of the heat source centre. Thermal diffusivities of three materials, 304 stainless steel, titanium, and zirconium have been measured with the established on-line detection system, and the measurement errors are: −2.26%, −1.07%, and 1.61% respectively.

  18. A Comparative Survey of Methods for Remote Heart Rate Detection From Frontal Face Videos

    Directory of Open Access Journals (Sweden)

    Chen Wang

    2018-05-01

    Full Text Available Remotely measuring physiological activity can provide substantial benefits for both the medical and the affective computing applications. Recent research has proposed different methodologies for the unobtrusive detection of heart rate (HR using human face recordings. These methods are based on subtle color changes or motions of the face due to cardiovascular activities, which are invisible to human eyes but can be captured by digital cameras. Several approaches have been proposed such as signal processing and machine learning. However, these methods are compared with different datasets, and there is consequently no consensus on method performance. In this article, we describe and evaluate several methods defined in literature, from 2008 until present day, for the remote detection of HR using human face recordings. The general HR processing pipeline is divided into three stages: face video processing, face blood volume pulse (BVP signal extraction, and HR computation. Approaches presented in the paper are classified and grouped according to each stage. At each stage, algorithms are analyzed and compared based on their performance using the public database MAHNOB-HCI. Results found in this article are limited on MAHNOB-HCI dataset. Results show that extracted face skin area contains more BVP information. Blind source separation and peak detection methods are more robust with head motions for estimating HR.

  19. SPEED : a semantics-based pipeline for economic event detection

    NARCIS (Netherlands)

    Hogenboom, F.P.; Hogenboom, A.C.; Frasincar, F.; Kaymak, U.; Meer, van der O.; Schouten, K.; Vandic, D.; Parsons, J.; Motoshi, S.; Shoval, P.; Woo, C.; Wand, Y.

    2010-01-01

    Nowadays, emerging news on economic events such as acquisitions has a substantial impact on the financial markets. Therefore, it is important to be able to automatically and accurately identify events in news items in a timely manner. For this, one has to be able to process a large amount of

  20. Automated Detection of Financial Events in News Text

    NARCIS (Netherlands)

    F.P. Hogenboom (Frederik)

    2014-01-01

    markdownabstractToday’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news,

  1. Semantics-based information extraction for detecting economic events

    NARCIS (Netherlands)

    A.C. Hogenboom (Alexander); F. Frasincar (Flavius); K. Schouten (Kim); O. van der Meer

    2013-01-01

    textabstractAs today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for

  2. Prescription-event monitoring: developments in signal detection.

    Science.gov (United States)

    Ferreira, Germano

    2007-01-01

    Prescription-event monitoring (PEM) is a non-interventional intensive method for post-marketing drug safety monitoring of newly licensed medicines. PEM studies are cohort studies where exposure is obtained from a centralised service and outcomes from simple questionnaires completed by general practitioners. Follow-up forms are sent for selected events. Because PEM captures all events and not only the suspected adverse drug reactions, PEM cohorts potentially differ in respect to the distribution of number of events per person depending on the nature of the drug under study. This variance can be related either with the condition for which the drug is prescribed (e.g. a condition causing high morbidity will have, in average, a higher number of events per person compared with a condition with lower morbidity) or with the drug effect itself. This paper describes an exploratory investigation of the distortion caused by product-related variations of the number of events to the interpretation of the proportional reporting ratio (PRR) values ("the higher the PRR, the greater the strength of the signal") computed using drug-cohort data. We studied this effect by assessing the agreement between the PRR based on events (event of interest vs all other events) and PRR based on cases (cases with the event of interest vs cases with any other events). PRR were calculated for all combinations reported to ten selected drugs against a comparator of 81 other drugs. Three of the ten drugs had a cohort with an apparent higher proportion of patients with lower number of events. The PRRs based on events were systematically higher than the PRR based on cases for the combinations reported to these three drugs. Additionally, when applying the threshold criteria for signal screening (n > or =3, PRR > or =1.5 and Chi-squared > or =4), the binary agreement was generally high but apparently lower for these three drugs. In conclusion, the distribution of events per patient in drug cohorts shall be

  3. Dependency of human target detection performance on clutter and quality of supporting image analysis algorithms in a video surveillance task

    Science.gov (United States)

    Huber, Samuel; Dunau, Patrick; Wellig, Peter; Stein, Karin

    2017-10-01

    Background: In target detection, the success rates depend strongly on human observer performances. Two prior studies tested the contributions of target detection algorithms and prior training sessions. The aim of this Swiss-German cooperation study was to evaluate the dependency of human observer performance on the quality of supporting image analysis algorithms. Methods: The participants were presented 15 different video sequences. Their task was to detect all targets in the shortest possible time. Each video sequence showed a heavily cluttered simulated public area from a different viewing angle. In each video sequence, the number of avatars in the area was altered to 100, 150 and 200 subjects. The number of targets appearing was kept at 10%. The number of marked targets varied from 0, 5, 10, 20 up to 40 marked subjects while keeping the positive predictive value of the detection algorithm at 20%. During the task, workload level was assessed by applying an acoustic secondary task. Detection rates and detection times for the targets were analyzed using inferential statistics. Results: The study found Target Detection Time to increase and Target Detection Rates to decrease with increasing numbers of avatars. The same is true for the Secondary Task Reaction Time while there was no effect on Secondary Task Hit Rate. Furthermore, we found a trend for a u-shaped correlation between the numbers of markings and RTST indicating increased workload. Conclusion: The trial results may indicate useful criteria for the design of training and support of observers in observational tasks.

  4. Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

    Science.gov (United States)

    Housh, Mashor; Ohar, Ziv

    2017-03-01

    The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Changes are detected - cameras and video systems are monitoring the plant site, only rarely giving false alarm

    International Nuclear Information System (INIS)

    Zeissler, H.

    1988-01-01

    The main purpose of automatic data acquisition and processing for monitoring goals is to relieve the security personnel from monotonous observation tasks. The novel video systems can be programmed to detect moving target alarm signals, or accept alarm-suppressing image changes. This allows an intelligent alarm evaluation for physical protection in industry, differentiating between real and false alarm signals. (orig.) [de

  6. SMART VIDEO SURVEILLANCE SYSTEM FOR VEHICLE DETECTION AND TRAFFIC FLOW CONTROL

    Directory of Open Access Journals (Sweden)

    A. A. SHAFIE

    2011-08-01

    Full Text Available Traffic signal light can be optimized using vehicle flow statistics obtained by Smart Video Surveillance Software (SVSS. This research focuses on efficient traffic control system by detecting and counting the vehicle numbers at various times and locations. At present, one of the biggest problems in the main city in any country is the traffic jam during office hour and office break hour. Sometimes it can be seen that the traffic signal green light is still ON even though there is no vehicle coming. Similarly, it is also observed that long queues of vehicles are waiting even though the road is empty due to traffic signal light selection without proper investigation on vehicle flow. This can be handled by adjusting the vehicle passing time implementing by our developed SVSS. A number of experiment results of vehicle flows are discussed in this research graphically in order to test the feasibility of the developed system. Finally, adoptive background model is proposed in SVSS in order to successfully detect target objects such as motor bike, car, bus, etc.

  7. A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection

    Directory of Open Access Journals (Sweden)

    Stéphanie Jehan-Besson

    2002-06-01

    Full Text Available We propose a 3-step algorithm for the automatic detection of moving objects in video sequences using region-based active contours. First, we introduce a very full general framework for region-based active contours with a new Eulerian method to compute the evolution equation of the active contour from a criterion including both region-based and boundary-based terms. This framework can be easily adapted to various applications, thanks to the introduction of functions named descriptors of the different regions. With this new Eulerian method based on shape optimization principles, we can easily take into account the case of descriptors depending upon features globally attached to the regions. Second, we propose a 3-step algorithm for detection of moving objects, with a static or a mobile camera, using region-based active contours. The basic idea is to hierarchically associate temporal and spatial information. The active contour evolves with successively three sets of descriptors: a temporal one, and then two spatial ones. The third spatial descriptor takes advantage of the segmentation of the image in intensity homogeneous regions. User interaction is reduced to the choice of a few parameters at the beginning of the process. Some experimental results are supplied.

  8. Signal detection to identify serious adverse events (neuropsychiatric events in travelers taking mefloquine for chemoprophylaxis of malaria

    Directory of Open Access Journals (Sweden)

    Naing C

    2012-08-01

    Full Text Available Cho Naing,1,3 Kyan Aung,1 Syed Imran Ahmed,2 Joon Wah Mak31School of Medical Sciences, 2School of Pharmacy and Health Sciences, 3School of Postgraduate Studies and Research, International Medical University, Kuala Lumpur, MalaysiaBackground: For all medications, there is a trade-off between benefits and potential for harm. It is important for patient safety to detect drug-event combinations and analyze by appropriate statistical methods. Mefloquine is used as chemoprophylaxis for travelers going to regions with known chloroquine-resistant Plasmodium falciparum malaria. As such, there is a concern about serious adverse events associated with mefloquine chemoprophylaxis. The objective of the present study was to assess whether any signal would be detected for the serious adverse events of mefloquine, based on data in clinicoepidemiological studies.Materials and methods: We extracted data on adverse events related to mefloquine chemoprophylaxis from the two published datasets. Disproportionality reporting of adverse events such as neuropsychiatric events and other adverse events was presented in the 2 × 2 contingency table. Reporting odds ratio and corresponding 95% confidence interval [CI] data-mining algorithm was applied for the signal detection. The safety signals are considered significant when the ROR estimates and the lower limits of the corresponding 95% CI are ≥2.Results: Two datasets addressing adverse events of mefloquine chemoprophylaxis (one from a published article and one from a Cochrane systematic review were included for analyses. Reporting odds ratio 1.58, 95% CI: 1.49–1.68 based on published data in the selected article, and 1.195, 95% CI: 0.94–1.44 based on data in the selected Cochrane review. Overall, in both datasets, the reporting odds ratio values of lower 95% CI were less than 2.Conclusion: Based on available data, findings suggested that signals for serious adverse events pertinent to neuropsychiatric event were

  9. Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

    Science.gov (United States)

    Oliker, Nurit; Ostfeld, Avi

    2015-09-01

    Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Abnormal Event Detection in Wireless Sensor Networks Based on Multiattribute Correlation

    Directory of Open Access Journals (Sweden)

    Mengdi Wang

    2017-01-01

    Full Text Available Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.

  11. A software oscilloscope for DOS computers with an integrated remote control for a video tape recorder. The assignment of acoustic events to behavioural observations.

    Science.gov (United States)

    Höller, P

    1995-12-01

    With only a little knowledge of programming IBM compatible computers in Basic, it is possible to create a digital software oscilloscope with sampling rates up to 17 kHz (depending on the CPU- and bus-speed). The only additional hardware requirement is a common sound card compatible with the Soundblaster. The system presented in this paper is built to analyse the direction a flying bat is facing during sound emission. For this reason the system works with some additional hardware devices, in order to monitor video sequences at the computer screen, overlaid by an online oscillogram. Using an RS232-interface for a Panasonic video tape recorder both the oscillogram and the video tape recorder can be controlled simultaneously and moreover be analysed frame by frame. Not only acoustical events, but also APs, myograms, EEGs and other physiological data can be digitized and analysed in combination with the behavioural data of an experimental subject.

  12. Pilot study on real-time motion detection in UAS video data by human observer and image exploitation algorithm

    Science.gov (United States)

    Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Voit, Michael; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

    2017-05-01

    Real-time motion video analysis is a challenging and exhausting task for the human observer, particularly in safety and security critical domains. Hence, customized video analysis systems providing functions for the analysis of subtasks like motion detection or target tracking are welcome. While such automated algorithms relieve the human operators from performing basic subtasks, they impose additional interaction duties on them. Prior work shows that, e.g., for interaction with target tracking algorithms, a gaze-enhanced user interface is beneficial. In this contribution, we present an investigation on interaction with an independent motion detection (IDM) algorithm. Besides identifying an appropriate interaction technique for the user interface - again, we compare gaze-based and traditional mouse-based interaction - we focus on the benefit an IDM algorithm might provide for an UAS video analyst. In a pilot study, we exposed ten subjects to the task of moving target detection in UAS video data twice, once performing with automatic support, once performing without it. We compare the two conditions considering performance in terms of effectiveness (correct target selections). Additionally, we report perceived workload (measured using the NASA-TLX questionnaire) and user satisfaction (measured using the ISO 9241-411 questionnaire). The results show that a combination of gaze input and automated IDM algorithm provides valuable support for the human observer, increasing the number of correct target selections up to 62% and reducing workload at the same time.

  13. Multivariate algorithms for initiating event detection and identification in nuclear power plants

    International Nuclear Information System (INIS)

    Wu, Shun-Chi; Chen, Kuang-You; Lin, Ting-Han; Chou, Hwai-Pwu

    2018-01-01

    Highlights: •Multivariate algorithms for NPP initiating event detection and identification. •Recordings from multiple sensors are simultaneously considered for detection. •Both spatial and temporal information is used for event identification. •Untrained event isolation avoids falsely relating an untrained event. •Efficacy of the algorithms is verified with data from the Maanshan NPP simulator. -- Abstract: To prevent escalation of an initiating event into a severe accident, promptly detecting its occurrence and precisely identifying its type are essential. In this study, several multivariate algorithms for initiating event detection and identification are proposed to help maintain safe operations of nuclear power plants (NPPs). By monitoring changes in the NPP sensing variables, an event is detected when the preset thresholds are exceeded. Unlike existing approaches, recordings from sensors of the same type are simultaneously considered for detection, and no subjective reasoning is involved in setting these thresholds. To facilitate efficient event identification, a spatiotemporal feature extractor is proposed. The extracted features consist of the temporal traits used by existing techniques and the spatial signature of an event. Through an F-score-based feature ranking, only those that are most discriminant in classifying the events under consideration will be retained for identification. Moreover, an untrained event isolation scheme is introduced to avoid relating an untrained event to those in the event dataset so that improper recovery actions can be prevented. Results from experiments containing data of 12 event classes and a total of 125 events generated using a Taiwan’s Maanshan NPP simulator are provided to illustrate the efficacy of the proposed algorithms.

  14. Towards Optimal Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Agumbe Suresh, Mahima; Stoleru, Radu; Denton, Ron; Zechman, Emily; Shihada, Basem

    2012-01-01

    infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate

  15. On the event detected by the Mont Blanc underground neutrino detector on February 23, 1987

    Energy Technology Data Exchange (ETDEWEB)

    Dadykin, V L; Zatsepin, G T; Korchagin, V B

    1988-02-01

    The event detected by the Mont Balnc Soviet -Italian scintillation detector on February 23, 1987 at 2:52:37 are discussed. The corrected energies of the pulases of the event and the probability of the event imitation by the background are presented.

  16. A Dynamically Configurable Log-based Distributed Security Event Detection Methodology using Simple Event Correlator

    Science.gov (United States)

    2010-06-01

    Figures Figure Page 2.1. Verizon Data Breach Report: Detective Controls by percent of breach victims. [11...immature, operationally speaking. Figure 2.1: Verizon Data Breach Report: Detective Controls by percent of breach victims. [11] 7 The 2008 CSI Computer...Christopher Novak, Christopher Porter, Bryan Sartin, Peter Tippett, and J. Andrew Valentine. Data Breach Investigations Report. Technical report

  17. On Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Suresh, Mahima Agumbe; Stoleru, Radu; Zechman, Emily M.; Shihada, Basem

    2013-01-01

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against

  18. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

    Directory of Open Access Journals (Sweden)

    Angel Mur

    2016-04-01

    Full Text Available In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

  19. Background estimation and player detection in badminton video clips using histogram of pixel values along temporal dimension

    Science.gov (United States)

    Peng, Yahui; Ma, Xiao; Gao, Xinyu; Zhou, Fangxu

    2015-12-01

    Computer vision is an important tool for sports video processing. However, its application in badminton match analysis is very limited. In this study, we proposed a straightforward but robust histogram-based background estimation and player detection methods for badminton video clips, and compared the results with the naive averaging method and the mixture of Gaussians methods, respectively. The proposed method yielded better background estimation results than the naive averaging method and more accurate player detection results than the mixture of Gaussians player detection method. The preliminary results indicated that the proposed histogram-based method could estimate the background and extract the players accurately. We conclude that the proposed method can be used for badminton player tracking and further studies are warranted for automated match analysis.

  20. Detection and location of multiple events by MARS. Final report

    International Nuclear Information System (INIS)

    Wang, J.; Masso, J.F.; Archambeau, C.B.; Savino, J.M.

    1980-09-01

    Seismic data from two explosions was processed using the Systems Science and Software MARS (Multiple Arrival Recognition System) seismic event detector in an effort to determine their relative spatial and temporal separation on the basis of seismic data alone. The explosions were less than 1.0 kilometer apart and were separated by less than 0.5 sec in origin times. The seismic data consisted of nine local accelerograms (r < 1.0 km) and four regional (240 through 400 km) seismograms. The MARS processing clearly indicates the presence of multiple explosions, but the restricted frequency range of the data inhibits accurate time picks and hence limits the precision of the event location

  1. Deep learning based beat event detection in action movie franchises

    Science.gov (United States)

    Ejaz, N.; Khan, U. A.; Martínez-del-Amor, M. A.; Sparenberg, H.

    2018-04-01

    Automatic understanding and interpretation of movies can be used in a variety of ways to semantically manage the massive volumes of movies data. "Action Movie Franchises" dataset is a collection of twenty Hollywood action movies from five famous franchises with ground truth annotations at shot and beat level of each movie. In this dataset, the annotations are provided for eleven semantic beat categories. In this work, we propose a deep learning based method to classify shots and beat-events on this dataset. The training dataset for each of the eleven beat categories is developed and then a Convolution Neural Network is trained. After finding the shot boundaries, key frames are extracted for each shot and then three classification labels are assigned to each key frame. The classification labels for each of the key frames in a particular shot are then used to assign a unique label to each shot. A simple sliding window based method is then used to group adjacent shots having the same label in order to find a particular beat event. The results of beat event classification are presented based on criteria of precision, recall, and F-measure. The results are compared with the existing technique and significant improvements are recorded.

  2. [Microcytomorphometric video-image detection of nuclear chromatin in ovarian cancer].

    Science.gov (United States)

    Grzonka, Dariusz; Kamiński, Kazimierz; Kaźmierczak, Wojciech

    2003-09-01

    Technology of detection of tissue preparates precisious evaluates contents of nuclear chromatine, largeness and shape of cellular nucleus, indicators of mitosis, DNA index, ploidy, phase-S fraction and other parameters. Methods of detection of picture are: microcytomorphometry video-image (MCMM-VI), flow, double flow and activated by fluorescence. Diagnostic methods of malignant neoplasm of ovary are still nonspecific and not precise, that is a reason of unsatisfied results of treatment. Evaluation of microcytomorphometric measurements of nuclear chromatine histopathologic tissue preparates (HP) of ovarian cancer and comparison to normal ovarian tissue. Estimated 10 paraffin embedded tissue preparates of serous ovarian cancer, 4 preparates mucinous cancer and 2 cases of tumor Kruckenberg patients operated in Clinic of Perinatology and Gynaecology Silesian Medical Academy in Zabrze in period 2001-2002, MCMM-VI estimation based on computer aided analysis system: microscope Axioscop 20, camera tv JVCTK-C 1380, CarlZeiss KS Vision 400 rel.3.0 software. Following MCMM-VI parameters assessed: count of pathologic nucleus, diameter of nucleus, area, min/max diameter ratio, equivalent circle diameter (Dcircle), mean of brightness (mean D), integrated optical density (IOD = area x mean D), DNA index and 2.5 c exceeding rate percentage (2.5 c ER%). MCMM-VI performed on the 160 areas of 16 preparates of cancer and 100 areas of normal ovarian tissue. Statistical analysis was performed by used t-Student test. We obtained stastistically significant higher values parameters of nuclear chromatine, DI, 2.5 c ER of mucinous cancer and tumor Kruckenberg comparison to serous cancer. MCMM-VI parameters of chromatine malignant ovarian neoplasm were statistically significantly higher than normal ovarian tissue. Cytometric and karyometric parametres of nuclear chromatine estimated MCMM-VI are useful in the diagnostics and prognosis of ovarian cancer.

  3. Study on the Detection of Moving Target in the Mining Method Based on Hybrid Algorithm for Sports Video Analysis

    Directory of Open Access Journals (Sweden)

    Huang Tian

    2014-10-01

    Full Text Available Moving object detection and tracking is the computer vision and image processing is a hot research direction, based on the analysis of the moving target detection and tracking algorithm in common use, focus on the sports video target tracking non rigid body. In sports video, non rigid athletes often have physical deformation in the process of movement, and may be associated with the occurrence of moving target under cover. Media data is surging to fast search and query causes more difficulties in data. However, the majority of users want to be able to quickly from the multimedia data to extract the interested content and implicit knowledge (concepts, rules, rules, models and correlation, retrieval and query quickly to take advantage of them, but also can provide the decision support problem solving hierarchy. Based on the motion in sport video object as the object of study, conducts the system research from the theoretical level and technical framework and so on, from the layer by layer mining between low level motion features to high-level semantic motion video, not only provides support for users to find information quickly, but also can provide decision support for the user to solve the problem.

  4. Falls event detection using triaxial accelerometry and barometric pressure measurement.

    Science.gov (United States)

    Bianchi, Federico; Redmond, Stephen J; Narayanan, Michael R; Cerutti, Sergio; Celler, Branko G; Lovell, Nigel H

    2009-01-01

    A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

  5. Bidirectional RNN for Medical Event Detection in Electronic Health Records.

    Science.gov (United States)

    Jagannatha, Abhyuday N; Yu, Hong

    2016-06-01

    Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models.

  6. Power System Extreme Event Detection: The VulnerabilityFrontier

    Energy Technology Data Exchange (ETDEWEB)

    Lesieutre, Bernard C.; Pinar, Ali; Roy, Sandip

    2007-10-17

    In this work we apply graph theoretic tools to provide aclose bound on a frontier relating the number of line outages in a gridto the power disrupted by the outages. This frontier describes theboundary of a space relating the possible severity of a disturbance interms of power disruption, from zero to some maximum on the boundary, tothe number line outages involved in the event. We present the usefulnessof this analysis with a complete analysis of a 30 bus system, and presentresults for larger systems.

  7. ISOMER: Informative Segment Observations for Multimedia Event Recounting

    NARCIS (Netherlands)

    Sun, C.; Burns, B.; Nevatia, R.; Snoek, C.; Bolles, B.; Myers, G.; Wang, W.; Yeh, E.

    2014-01-01

    This paper describes a system for multimedia event detection and recounting. The goal is to detect a high level event class in unconstrained web videos and generate event oriented summarization for display to users. For this purpose, we detect informative segments and collect observations for them,

  8. A video authentication technique

    International Nuclear Information System (INIS)

    Johnson, C.S.

    1987-01-01

    Unattended video surveillance systems are particularly vulnerable to the substitution of false video images into the cable that connects the camera to the video recorder. New technology has made it practical to insert a solid state video memory into the video cable, freeze a video image from the camera, and hold this image as long as desired. Various techniques, such as line supervision and sync detection, have been used to detect video cable tampering. The video authentication technique described in this paper uses the actual video image from the camera as the basis for detecting any image substitution made during the transmission of the video image to the recorder. The technique, designed for unattended video systems, can be used for any video transmission system where a two-way digital data link can be established. The technique uses similar microprocessor circuitry at the video camera and at the video recorder to select sample points in the video image for comparison. The gray scale value of these points is compared at the recorder controller and if the values agree within limits, the image is authenticated. If a significantly different image was substituted, the comparison would fail at a number of points and the video image would not be authenticated. The video authentication system can run as a stand-alone system or at the request of another system

  9. Spatial-temporal event detection in climate parameter imagery.

    Energy Technology Data Exchange (ETDEWEB)

    McKenna, Sean Andrew; Gutierrez, Karen A.

    2011-10-01

    Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to the earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.

  10. Optical tweezers with 2.5 kHz bandwidth video detection for single-colloid electrophoresis

    Science.gov (United States)

    Otto, Oliver; Gutsche, Christof; Kremer, Friedrich; Keyser, Ulrich F.

    2008-02-01

    We developed an optical tweezers setup to study the electrophoretic motion of colloids in an external electric field. The setup is based on standard components for illumination and video detection. Our video based optical tracking of the colloid motion has a time resolution of 0.2ms, resulting in a bandwidth of 2.5kHz. This enables calibration of the optical tweezers by Brownian motion without applying a quadrant photodetector. We demonstrate that our system has a spatial resolution of 0.5nm and a force sensitivity of 20fN using a Fourier algorithm to detect periodic oscillations of the trapped colloid caused by an external ac field. The electrophoretic mobility and zeta potential of a single colloid can be extracted in aqueous solution avoiding screening effects common for usual bulk measurements.

  11. Full-waveform detection of non-impulsive seismic events based on time-reversal methods

    Science.gov (United States)

    Solano, Ericka Alinne; Hjörleifsdóttir, Vala; Liu, Qinya

    2017-12-01

    We present a full-waveform detection method for non-impulsive seismic events, based on time-reversal principles. We use the strain Green's tensor as a matched filter, correlating it with continuous observed seismograms, to detect non-impulsive seismic events. We show that this is mathematically equivalent to an adjoint method for detecting earthquakes. We define the detection function, a scalar valued function, which depends on the stacked correlations for a group of stations. Event detections are given by the times at which the amplitude of the detection function exceeds a given value relative to the noise level. The method can make use of the whole seismic waveform or any combination of time-windows with different filters. It is expected to have an advantage compared to traditional detection methods for events that do not produce energetic and impulsive P waves, for example glacial events, landslides, volcanic events and transform-fault earthquakes for events which velocity structure along the path is relatively well known. Furthermore, the method has advantages over empirical Greens functions template matching methods, as it does not depend on records from previously detected events, and therefore is not limited to events occurring in similar regions and with similar focal mechanisms as these events. The method is not specific to any particular way of calculating the synthetic seismograms, and therefore complicated structural models can be used. This is particularly beneficial for intermediate size events that are registered on regional networks, for which the effect of lateral structure on the waveforms can be significant. To demonstrate the feasibility of the method, we apply it to two different areas located along the mid-oceanic ridge system west of Mexico where non-impulsive events have been reported. The first study area is between Clipperton and Siqueiros transform faults (9°N), during the time of two earthquake swarms, occurring in March 2012 and May

  12. Detecting impacts of extreme events with ecological in situ monitoring networks

    Directory of Open Access Journals (Sweden)

    M. D. Mahecha

    2017-09-01

    Full Text Available Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR, identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with  ≈  100 randomly placed sites in Europe yield a  ≥  90 % chance of detecting the eight largest (typically very large extreme events; but only a  ≥  50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON reliably detect the largest extremes, but that the extreme event detection rates are not higher than would

  13. Understanding Behaviors in Videos through Behavior-Specific Dictionaries

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2018-01-01

    Understanding behaviors is the core of video content analysis, which is highly related to two important applications: abnormal event detection and action recognition. Dictionary learning, as one of the mid-level representations, is an important step to process a video. It has achieved state...

  14. Parts-based detection of AK-47s for forensic video analysis

    OpenAIRE

    Jones, Justin

    2010-01-01

    Approved for public release; distribution is unlimited Law enforcement, military personnel, and forensic analysts are increasingly reliant on imaging ystems to perform in a hostile environment and require a robust method to efficiently locate bjects of interest in videos and still images. Current approaches require a full-time operator to monitor a surveillance video or to sift a hard drive for suspicious content. In this thesis, we demonstrate the effectiveness of automated analysis tools...

  15. A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.

    Science.gov (United States)

    Oliker, Nurit; Ostfeld, Avi

    2014-03-15

    This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. The MediaMill TRECVID 2012 semantic video search engine

    NARCIS (Netherlands)

    Snoek, C.G.M.; van de Sande, K.E.A.; Habibian, A.; Kordumova, S.; Li, Z.; Mazloom, M.; Pintea, S.L.; Tao, R.; Koelma, D.C.; Smeulders, A.W.M.

    2012-01-01

    In this paper we describe our TRECVID 2012 video retrieval experiments. The MediaMill team participated in four tasks: semantic indexing, multimedia event detection, multimedia event recounting and instance search. The starting point for the MediaMill detection approach is our top-performing

  17. Intelligent keyframe extraction for video printing

    Science.gov (United States)

    Zhang, Tong

    2004-10-01

    Nowadays most digital cameras have the functionality of taking short video clips, with the length of video ranging from several seconds to a couple of minutes. The purpose of this research is to develop an algorithm which extracts an optimal set of keyframes from each short video clip so that the user could obtain proper video frames to print out. In current video printing systems, keyframes are normally obtained by evenly sampling the video clip over time. Such an approach, however, may not reflect highlights or regions of interest in the video. Keyframes derived in this way may also be improper for video printing in terms of either content or image quality. In this paper, we present an intelligent keyframe extraction approach to derive an improved keyframe set by performing semantic analysis of the video content. For a video clip, a number of video and audio features are analyzed to first generate a candidate keyframe set. These features include accumulative color histogram and color layout differences, camera motion estimation, moving object tracking, face detection and audio event detection. Then, the candidate keyframes are clustered and evaluated to obtain a final keyframe set. The objective is to automatically generate a limited number of keyframes to show different views of the scene; to show different people and their actions in the scene; and to tell the story in the video shot. Moreover, frame extraction for video printing, which is a rather subjective problem, is considered in this work for the first time, and a semi-automatic approach is proposed.

  18. Discrete Event Simulation Model of the Polaris 2.1 Gamma Ray Imaging Radiation Detection Device

    Science.gov (United States)

    2016-06-01

    release; distribution is unlimited DISCRETE EVENT SIMULATION MODEL OF THE POLARIS 2.1 GAMMA RAY IMAGING RADIATION DETECTION DEVICE by Andres T...ONLY (Leave blank) 2. REPORT DATE June 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE DISCRETE EVENT SIMULATION MODEL...modeled. The platform, Simkit, was utilized to create a discrete event simulation (DES) model of the Polaris. After carefully constructing the DES

  19. Event Detection Challenges, Methods, and Applications in Natural and Artificial Systems

    Science.gov (United States)

    2009-03-01

    Sauvageon, Agogino, Mehr, and Tumer [2006], for instance, use a fourth degree polynomial within an event detection algorithm to sense high... cancer , and coronary artery disease. His study examines the age at which to begin screening exams, the intervals between the exams, and (possibly...AM, Mehr AF, and Tumer IY. 2006. “Comparison of Event Detection Methods for Centralized Sensor Networks.” IEEE Sensors Applications Symposium 2006

  20. Screening DNA chip and event-specific multiplex PCR detection methods for biotech crops.

    Science.gov (United States)

    Lee, Seong-Hun

    2014-11-01

    There are about 80 biotech crop events that have been approved by safety assessment in Korea. They have been controlled by genetically modified organism (GMO) and living modified organism (LMO) labeling systems. The DNA-based detection method has been used as an efficient scientific management tool. Recently, the multiplex polymerase chain reaction (PCR) and DNA chip have been developed as simultaneous detection methods for several biotech crops' events. The event-specific multiplex PCR method was developed to detect five biotech maize events: MIR604, Event 3272, LY 038, MON 88017 and DAS-59122-7. The specificity was confirmed and the sensitivity was 0.5%. The screening DNA chip was developed from four endogenous genes of soybean, maize, cotton and canola respectively along with two regulatory elements and seven genes: P35S, tNOS, pat, bar, epsps1, epsps2, pmi, cry1Ac and cry3B. The specificity was confirmed and the sensitivity was 0.5% for four crops' 12 events: one soybean, six maize, three cotton and two canola events. The multiplex PCR and DNA chip can be available for screening, gene-specific and event-specific analysis of biotech crops as efficient detection methods by saving on workload and time. © 2014 Society of Chemical Industry. © 2014 Society of Chemical Industry.

  1. An integrated logit model for contamination event detection in water distribution systems.

    Science.gov (United States)

    Housh, Mashor; Ostfeld, Avi

    2015-05-15

    The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Energy-Efficient Fault-Tolerant Dynamic Event Region Detection in Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Enemark, Hans-Jacob; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    to a hybrid algorithm for dynamic event region detection, such as real-time tracking of chemical leakage regions. Considering the characteristics of the moving away dynamic events, we propose a return back condition for the hybrid algorithm from distributed neighborhood collaboration, in which a node makes......Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement...... its detection decision based on decisions received from its spatial and temporal neighbors, to local non-communicative decision making. The simulation results demonstrate that the improved algorithm does not degrade the detection accuracy of the original algorithm, while it has better energy...

  3. Adaptive Sensor Tuning for Seismic Event Detection in Environment with Electromagnetic Noise

    Science.gov (United States)

    Ziegler, Abra E.

    The goal of this research is to detect possible microseismic events at a carbon sequestration site. Data recorded on a continuous downhole microseismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project, were evaluated using machine learning and reinforcement learning techniques to determine their effectiveness at seismic event detection on a dataset with electromagnetic noise. The data were recorded from a passive vertical monitoring array consisting of 16 levels of 3-component 15 Hz geophones installed in the field and continuously recording since January 2014. Electromagnetic and other noise recorded on the array has significantly impacted the utility of the data and it was necessary to characterize and filter the noise in order to attempt event detection. Traditional detection methods using short-term average/long-term average (STA/LTA) algorithms were evaluated and determined to be ineffective because of changing noise levels. To improve the performance of event detection and automatically and dynamically detect seismic events using effective data processing parameters, an adaptive sensor tuning (AST) algorithm developed by Sandia National Laboratories was utilized. AST exploits neuro-dynamic programming (reinforcement learning) trained with historic event data to automatically self-tune and determine optimal detection parameter settings. The key metric that guides the AST algorithm is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The effects that changes in neighborhood configuration have on signal detection were explored, as it was determined that neighborhood-based detections significantly reduce the number of both missed and false detections in ground-truthed data. The performance of the AST algorithm was

  4. Automatic Detection and Classification of Audio Events for Road Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Noor Almaadeed

    2018-06-01

    Full Text Available This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.

  5. Video content analysis of surgical procedures.

    Science.gov (United States)

    Loukas, Constantinos

    2018-02-01

    In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain. In this paper, we review recent developments and analyze future directions in the field of content-based video analysis of surgical operations. The review was obtained from PubMed and Google Scholar search on combinations of the following keywords: 'surgery', 'video', 'phase', 'task', 'skills', 'event', 'shot', 'analysis', 'retrieval', 'detection', 'classification', and 'recognition'. The collected articles were categorized and reviewed based on the technical goal sought, type of surgery performed, and structure of the operation. A total of 81 articles were included. The publication activity is constantly increasing; more than 50% of these articles were published in the last 3 years. Significant research has been performed for video task detection and retrieval in eye surgery. In endoscopic surgery, the research activity is more diverse: gesture/task classification, skills assessment, tool type recognition, shot/event detection and retrieval. Recent works employ deep neural networks for phase and tool recognition as well as shot detection. Content-based video analysis of surgical operations is a rapidly expanding field. Several future prospects for research exist including, inter alia, shot boundary detection, keyframe extraction, video summarization, pattern discovery, and video annotation. The development of publicly available benchmark datasets to evaluate and compare task-specific algorithms is essential.

  6. Event-specific qualitative and quantitative detection of five genetically modified rice events using a single standard reference molecule.

    Science.gov (United States)

    Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Shin, Min-Ki; Moon, Gui-Im; Hong, Jin-Hwan; Kim, Hae-Yeong

    2017-07-01

    One novel standard reference plasmid, namely pUC-RICE5, was constructed as a positive control and calibrator for event-specific qualitative and quantitative detection of genetically modified (GM) rice (Bt63, Kemingdao1, Kefeng6, Kefeng8, and LLRice62). pUC-RICE5 contained fragments of a rice-specific endogenous reference gene (sucrose phosphate synthase) as well as the five GM rice events. An existing qualitative PCR assay approach was modified using pUC-RICE5 to create a quantitative method with limits of detection correlating to approximately 1-10 copies of rice haploid genomes. In this quantitative PCR assay, the square regression coefficients ranged from 0.993 to 1.000. The standard deviation and relative standard deviation values for repeatability ranged from 0.02 to 0.22 and 0.10% to 0.67%, respectively. The Ministry of Food and Drug Safety (Korea) validated the method and the results suggest it could be used routinely to identify five GM rice events. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Impact of sensor detection limits on protecting water distribution systems from contamination events

    International Nuclear Information System (INIS)

    McKenna, Sean Andrew; Hart, David Blaine; Yarrington, Lane

    2006-01-01

    Real-time water quality sensors are becoming commonplace in water distribution systems. However, field deployable, contaminant-specific sensors are still in the development stage. As development proceeds, the necessary operating parameters of these sensors must be determined to protect consumers from accidental and malevolent contamination events. This objective can be quantified in several different ways including minimization of: the time necessary to detect a contamination event, the population exposed to contaminated water, the extent of the contamination within the network, and others. We examine the ability of a sensor set to meet these objectives as a function of both the detection limit of the sensors and the number of sensors in the network. A moderately sized distribution network is used as an example and different sized sets of randomly placed sensors are considered. For each combination of a certain number of sensors and a detection limit, the mean values of the different objectives across multiple random sensor placements are calculated. The tradeoff between the necessary detection limit in a sensor and the number of sensors is evaluated. Results show that for the example problem examined here, a sensor detection limit of 0.01 of the average source concentration is adequate for maximum protection. Detection of events is dependent on the detection limit of the sensors, but for those events that are detected, the values of the performance measures are not a function of the sensor detection limit. The results of replacing a single sensor in a network with a sensor having a much lower detection limit show that while this replacement can improve results, the majority of the additional events detected had performance measures of relatively low consequence.

  8. Insertable cardiac event recorder in detection of atrial fibrillation after cryptogenic stroke: an audit report.

    Science.gov (United States)

    Etgen, Thorleif; Hochreiter, Manfred; Mundel, Markus; Freudenberger, Thomas

    2013-07-01

    Atrial fibrillation (AF) is the most frequent risk factor in ischemic stroke but often remains undetected. We analyzed the value of insertable cardiac event recorder in detection of AF in a 1-year cohort of patients with cryptogenic ischemic stroke. All patients with cryptogenic stroke and eligibility for oral anticoagulation were offered the insertion of a cardiac event recorder. Regular follow-up for 1 year recorded the incidence of AF. Of the 393 patients with ischemic stroke, 65 (16.5%) had a cryptogenic stroke, and in 22 eligible patients, an event recorder was inserted. After 1 year, in 6 of 22 patients (27.3%), AF was detected. These preliminary data show that insertion of cardiac event recorder was eligible in approximately one third of patients with cryptogenic stroke and detected in approximately one quarter of these patients new AF.

  9. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran

    2017-08-17

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

  10. Real-time detection and classification of anomalous events in streaming data

    Science.gov (United States)

    Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.; Laska, Jason A.; Harrison, Lane T.

    2016-04-19

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

  11. Mining the IPTV Channel Change Event Stream to Discover Insight and Detect Ads

    Directory of Open Access Journals (Sweden)

    Matej Kren

    2016-01-01

    Full Text Available IPTV has been widely deployed throughout the world, bringing significant advantages to users in terms of the channel offering, video on demand, and interactive applications. One aspect that has been often neglected is the ability of precise and unobtrusive telemetry. TV set-top boxes that are deployed in modern IPTV systems can be thought of as capable sensor nodes that collect vast amounts of data, representing both the user activity and the quality of service delivered by the system itself. In this paper we focus on the user-generated events and analyze how the data stream of channel change events received from the entire IPTV network can be mined to obtain insight about the content. We demonstrate that it is possible to predict the occurrence of TV ads with high probability and show that the approach could be extended to model the user behavior and classify the viewership in multiple dimensions.

  12. Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

    International Nuclear Information System (INIS)

    Semmens, Kathryn Alese; Ramage, Joan; Bartsch, Annett; Liston, Glen E

    2013-01-01

    High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer—EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change

  13. Video-based respiration monitoring with automatic region of interest detection

    NARCIS (Netherlands)

    Janssen, R.J.M.; Wang, Wenjin; Moço, A.; de Haan, G.

    2016-01-01

    Vital signs monitoring is ubiquitous in clinical environments and emerging in home-based healthcare applications. Still, since current monitoring methods require uncomfortable sensors, respiration rate remains the least measured vital sign. In this paper, we propose a video-based respiration

  14. Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity

    DEFF Research Database (Denmark)

    Haque, Mohammad Ahsanul; Irani, Ramin; Nasrollahi, Kamal

    2016-01-01

    the challenges originates from realistic sce-nario. A face quality assessment system was also incorporated in the proposed system to reduce erroneous results by discarding low quality faces that occurred in a video sequence due to problems in realistic lighting, head motion and pose variation. Experimental...

  15. Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks

    Directory of Open Access Journals (Sweden)

    Heng Yao

    2017-12-01

    Full Text Available Today’s H.264/AVC coded videos have a high quality, high data-compression ratio. They also have a strong fault tolerance, better network adaptability, and have been widely applied on the Internet. With the popularity of powerful and easy-to-use video editing software, digital videos can be tampered with in various ways. Therefore, the double compression in the H.264/AVC video can be used as a first step in the study of video-tampering forensics. This paper proposes a simple, but effective, double-compression detection method that analyzes the periodic features of the string of data bits (SODBs and the skip macroblocks (S-MBs for all I-frames and P-frames in a double-compressed H.264/AVC video. For a given suspicious video, the SODBs and S-MBs are extracted for each frame. Both features are then incorporated to generate one enhanced feature to represent the periodic artifact of the double-compressed video. Finally, a time-domain analysis is conducted to detect the periodicity of the features. The primary Group of Pictures (GOP size is estimated based on an exhaustive strategy. The experimental results demonstrate the efficacy of the proposed method.

  16. Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

    Directory of Open Access Journals (Sweden)

    Yingchi Mao

    2017-12-01

    Full Text Available Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability.

  17. SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

    Science.gov (United States)

    Işık, Şahin; Özkan, Kemal; Günal, Serkan; Gerek, Ömer Nezih

    2018-03-01

    Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.

  18. [The Questionnaire of Experiences Associated with Video games (CERV): an instrument to detect the problematic use of video games in Spanish adolescents].

    Science.gov (United States)

    Chamarro, Andres; Carbonell, Xavier; Manresa, Josep Maria; Munoz-Miralles, Raquel; Ortega-Gonzalez, Raquel; Lopez-Morron, M Rosa; Batalla-Martinez, Carme; Toran-Monserrat, Pere

    2014-01-01

    The aim of this study is to validate the Video Game-Related Experiences Questionnaire (CERV in Spanish). The questionnaire consists of 17 items, developed from the CERI (Internet-Related Experiences Questionnaire - Beranuy and cols.), and assesses the problematic use of non-massive video games. It was validated for adolescents in Compulsory Secondary Education. To validate the questionnaire, a confirmatory factor analysis (CFA) and an internal consistency analysis were carried out. The factor structure shows two factors: (a) Psychological dependence and use for evasion; and (b) Negative consequences of using video games. Two cut-off points were established for people with no problems in their use of video games (NP), with potential problems in their use of video games (PP), and with serious problems in their use of video games (SP). Results show that there is higher prevalence among males and that problematic use decreases with age. The CERV seems to be a good instrument for the screening of adolescents with difficulties deriving from video game use. Further research should relate problematic video game use with difficulties in other life domains, such as the academic field.

  19. Individual differences in event-based prospective memory: Evidence for multiple processes supporting cue detection.

    Science.gov (United States)

    Brewer, Gene A; Knight, Justin B; Marsh, Richard L; Unsworth, Nash

    2010-04-01

    The multiprocess view proposes that different processes can be used to detect event-based prospective memory cues, depending in part on the specificity of the cue. According to this theory, attentional processes are not necessary to detect focal cues, whereas detection of nonfocal cues requires some form of controlled attention. This notion was tested using a design in which we compared performance on a focal and on a nonfocal prospective memory task by participants with high or low working memory capacity. An interaction was found, such that participants with high and low working memory performed equally well on the focal task, whereas the participants with high working memory performed significantly better on the nonfocal task than did their counterparts with low working memory. Thus, controlled attention was only necessary for detecting event-based prospective memory cues in the nonfocal task. These results have implications for theories of prospective memory, the processes necessary for cue detection, and the successful fulfillment of intentions.

  20. On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Sofia Maria Dima

    2016-01-01

    Full Text Available Event detection in realistic WSN environments is a critical research domain, while the environmental monitoring comprises one of its most pronounced applications. Although efforts related to the environmental applications have been presented in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in wireless environments. Aiming at addressing this shortage, in this paper an advanced multimodal approach is followed based on fuzzy logic. The proposed fuzzy inference system (FIS is implemented on TelosB motes and evaluates the probability of fire detection while aiming towards power conservation. Additionally to a straightforward centralized approach, a distributed implementation of the above FIS is also proposed, aiming towards network congestion reduction while optimally distributing the energy consumption among network nodes so as to maximize network lifetime. Moreover this work proposes an event based execution of the aforementioned FIS aiming to further reduce the computational as well as the communication cost, compared to a periodical time triggered FIS execution. As a final contribution, performance metrics acquired from all the proposed FIS implementation techniques are thoroughly compared and analyzed with respect to critical network conditions aiming to offer realistic evaluation and thus objective conclusions’ extraction.

  1. Correspondence between audio and visual deep models for musical instrument detection in video recordings

    OpenAIRE

    Slizovskaia, Olga; Gómez, Emilia; Haro, Gloria

    2017-01-01

    This work aims at investigating cross-modal connections between audio and video sources in the task of musical instrument recognition. We also address in this work the understanding of the representations learned by convolutional neural networks (CNNs) and we study feature correspondence between audio and visual components of a multimodal CNN architecture. For each instrument category, we select the most activated neurons and investigate exist- ing cross-correlations between neurons from the ...

  2. Video-assisted laparoscopy for the detection and diagnosis of endometriosis: safety, reliability, and invasiveness

    OpenAIRE

    Schipper, Erica; Nezhat, Camran

    2012-01-01

    Erica Schipper,1 Camran Nezhat21Center for Minimally Invasive and Robotic Surgery, Palo Alto, CA; 2Obstetrics/Gynecology and Surgery, Stanford University Medical Center, Palo Alto, CA, USAAbstract: Endometriosis is a highly enigmatic disease with multiple presentations ranging from infertility to severe pain, often causing significant morbidity. Video-assisted laparoscopy (VALS) has now replaced laparotomy as the gold standard for the diagnosis and management of endometriosis. While imaging h...

  3. An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

    Science.gov (United States)

    LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

    2016-12-01

    Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

  4. Detection of unusual events and trends in complex non-stationary data streams

    International Nuclear Information System (INIS)

    Charlton-Perez, C.; Perez, R.B.; Protopopescu, V.; Worley, B.A.

    2011-01-01

    The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for diverse applications, ranging from power plant operation to homeland security. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden events inside intermittent signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.

  5. Method for detecting binding events using micro-X-ray fluorescence spectrometry

    Science.gov (United States)

    Warner, Benjamin P.; Havrilla, George J.; Mann, Grace

    2010-12-28

    Method for detecting binding events using micro-X-ray fluorescence spectrometry. Receptors are exposed to at least one potential binder and arrayed on a substrate support. Each member of the array is exposed to X-ray radiation. The magnitude of a detectable X-ray fluorescence signal for at least one element can be used to determine whether a binding event between a binder and a receptor has occurred, and can provide information related to the extent of binding between the binder and receptor.

  6. The comparison of CT virtual colonoscopy with video colonoscopy (the detection of simulated polyps in pig colon)

    International Nuclear Information System (INIS)

    Tang Wen; Gong Jianping; Gao Zhixin; Lu Zhian

    2000-01-01

    Objective: To assess the value of CT virtual colonoscopy for the detection of simulated polyps in pig colon. Methods: Injecting the smelted wax under the mucosa to simulate the polyps in pig colon, then detected by video colonoscopy and scanned by helical CT. The images were obtained with collimation 3 mm, 5 mm, 10 mm and with the table pitch 1 and 2. All images were reconstructed at 1 mm intervals. Results: The shapes were depicted as follows: those greater than 10 mm in diameter polyps were clearly depicted. 5-9 mm in diameter polyps were faintly depicted. Those smaller than 5 mm in diameter were depicted difficulty. The details depicted: polyps larger than 10 mm in diameter and 5-9 mm in diameter were clearly depicted and that smaller than 5 mm in diameter were depicted difficulty. The images quality lowered with the increasing collimation and pitch. Conclusion: CT virtual colonoscopy is a non-invasive diagnostic technique. It can show the inner wall of colon as same as video colonoscopy does, and is a good alternative in clinical application

  7. Why conventional detection methods fail in identifying the existence of contamination events.

    Science.gov (United States)

    Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han

    2016-04-15

    Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Detection Thresholds for Rotation and Translation Gains in 360° Video-Based Telepresence Systems.

    Science.gov (United States)

    Zhang, Jingxin; Langbehn, Eike; Krupke, Dennis; Katzakis, Nicholas; Steinicke, Frank

    2018-04-01

    Telepresence systems have the potential to overcome limits and distance constraints of the real-world by enabling people to remotely visit and interact with each other. However, current telepresence systems usually lack natural ways of supporting interaction and exploration of remote environments (REs). In particular, single webcams for capturing the RE provide only a limited illusion of spatial presence, and movement control of mobile platforms in today's telepresence systems are often restricted to simple interaction devices. One of the main challenges of telepresence systems is to allow users to explore a RE in an immersive, intuitive and natural way, e.g., by real walking in the user's local environment (LE), and thus controlling motions of the robot platform in the RE. However, the LE in which the user's motions are tracked usually provides a much smaller interaction space than the RE. In this context, redirected walking (RDW) is a very suitable approach to solve this problem. However, so far there is no previous work, which explored if and how RDW can be used in video-based 360° telepresence systems. In this article, we conducted two psychophysical experiments in which we have quantified how much humans can be unknowingly redirected on virtual paths in the RE, which are different from the physical paths that they actually walk in the LE. Experiment 1 introduces a discrimination task between local and remote translations, and in Experiment 2 we analyzed the discrimination between local and remote rotations. In Experiment 1 participants performed straightforward translations in the LE that were mapped to straightforward translations in the RE shown as 360° videos, which were manipulated by different gains. Then, participants had to estimate if the remotely perceived translation was faster or slower than the actual physically performed translation. Similarly, in Experiment 2 participants performed rotations in the LE that were mapped to the virtual rotations

  9. Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity

    OpenAIRE

    Koch, Christian; Lode, Moritz; Stohr, Denny; Rizk, Amr; Steinmetz, Ralf

    2018-01-01

    YouTube is one of the most popular platforms for streaming of user-generated video. Nowadays, professional YouTubers are organized in so called multi-channel networks (MCNs). These networks offer services such as brand deals, equipment, and strategic advice in exchange for a share of the YouTubers' revenue. A major strategy to gain more subscribers and, hence, revenue is collaborating with other YouTubers. Yet, collaborations on YouTube have not been studied in a detailed quantitative manner....

  10. Real-time vehicle detection and tracking in video based on faster R-CNN

    Science.gov (United States)

    Zhang, Yongjie; Wang, Jian; Yang, Xin

    2017-08-01

    Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

  11. Video Analytics for Business Intelligence

    CERN Document Server

    Porikli, Fatih; Xiang, Tao; Gong, Shaogang

    2012-01-01

    Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed pervasively in public spaces including retail centres and shopping malls. Intelligent video analytics aims to automatically analyze content of massive amount of public space video data and has been one of the most active areas of computer vision research in the last two decades. Current focus of video analytics research has been largely on detecting alarm events and abnormal behaviours for public safety and security applications. However, increasingly CCTV installations have also been exploited for gathering and analyzing business intelligence information, in order to enhance marketing and operational efficiency. For example, in retail environments, surveillance cameras can be utilised to collect statistical information about shopping behaviour and preference for marketing (e.g., how many people entered a shop; how many females/males or which age groups of people showed interests to a particular product; how long did they stay in the sho...

  12. Effect of parameters in moving average method for event detection enhancement using phase sensitive OTDR

    Science.gov (United States)

    Kwon, Yong-Seok; Naeem, Khurram; Jeon, Min Yong; Kwon, Il-bum

    2017-04-01

    We analyze the relations of parameters in moving average method to enhance the event detectability of phase sensitive optical time domain reflectometer (OTDR). If the external events have unique frequency of vibration, then the control parameters of moving average method should be optimized in order to detect these events efficiently. A phase sensitive OTDR was implemented by a pulsed light source, which is composed of a laser diode, a semiconductor optical amplifier, an erbium-doped fiber amplifier, a fiber Bragg grating filter, and a light receiving part, which has a photo-detector and high speed data acquisition system. The moving average method is operated with the control parameters: total number of raw traces, M, number of averaged traces, N, and step size of moving, n. The raw traces are obtained by the phase sensitive OTDR with sound signals generated by a speaker. Using these trace data, the relation of the control parameters is analyzed. In the result, if the event signal has one frequency, then the optimal values of N, n are existed to detect the event efficiently.

  13. Video-assisted laparoscopy for the detection and diagnosis of endometriosis: safety, reliability, and invasiveness

    Directory of Open Access Journals (Sweden)

    Schipper E

    2012-07-01

    Full Text Available Erica Schipper,1 Camran Nezhat21Center for Minimally Invasive and Robotic Surgery, Palo Alto, CA; 2Obstetrics/Gynecology and Surgery, Stanford University Medical Center, Palo Alto, CA, USAAbstract: Endometriosis is a highly enigmatic disease with multiple presentations ranging from infertility to severe pain, often causing significant morbidity. Video-assisted laparoscopy (VALS has now replaced laparotomy as the gold standard for the diagnosis and management of endometriosis. While imaging has a role in the evaluation of some patients, histologic examination is needed for a definitive diagnosis. Laboratory evaluation currently has a minor role in the diagnosis of endometriosis, although studies are underway investigating serum markers, genetic studies, and endometrial sampling. A high index of suspicion is essential to accurately diagnose this complex condition, and a multidisciplinary approach is often indicated. The following review discusses laparoscopic diagnosis of endometriosis from the pre-operative evaluation of patients suspected of having endometriosis to surgical technique for safe and adequate laparoscopic diagnosis of the condition and postsurgical care.Keywords: endometriosis, video-assisted, laparoscopy, diagnosis

  14. Hierarchical structure for audio-video based semantic classification of sports video sequences

    Science.gov (United States)

    Kolekar, M. H.; Sengupta, S.

    2005-07-01

    A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

  15. Secure access control and large scale robust representation for online multimedia event detection.

    Science.gov (United States)

    Liu, Changyu; Lu, Bin; Li, Huiling

    2014-01-01

    We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

  16. Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

    Directory of Open Access Journals (Sweden)

    Changyu Liu

    2014-01-01

    Full Text Available We developed an online multimedia event detection (MED system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

  17. Lane Detection in Video-Based Intelligent Transportation Monitoring via Fast Extracting and Clustering of Vehicle Motion Trajectories

    Directory of Open Access Journals (Sweden)

    Jianqiang Ren

    2014-01-01

    Full Text Available Lane detection is a crucial process in video-based transportation monitoring system. This paper proposes a novel method to detect the lane center via rapid extraction and high accuracy clustering of vehicle motion trajectories. First, we use the activity map to realize automatically the extraction of road region, the calibration of dynamic camera, and the setting of three virtual detecting lines. Secondly, the three virtual detecting lines and a local background model with traffic flow feedback are used to extract and group vehicle feature points in unit of vehicle. Then, the feature point groups are described accurately by edge weighted dynamic graph and modified by a motion-similarity Kalman filter during the sparse feature point tracking. After obtaining the vehicle trajectories, a rough k-means incremental clustering with Hausdorff distance is designed to realize the rapid online extraction of lane center with high accuracy. The use of rough set reduces effectively the accuracy decrease, which results from the trajectories that run irregularly. Experimental results prove that the proposed method can detect lane center position efficiently, the affected time of subsequent tasks can be reduced obviously, and the safety of traffic surveillance systems can be enhanced significantly.

  18. Use of wireless sensor networks for distributed event detection in disaster management applications

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.

    Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance

  19. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

    2017-01-01

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

  20. Process variant comparison: using event logs to detect differences in behavior and business rules

    NARCIS (Netherlands)

    Bolt, A.; de Leoni, M.; van der Aalst, W.M.P.

    2018-01-01

    This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with

  1. A novel seizure detection algorithm informed by hidden Markov model event states

    Science.gov (United States)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h-1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  2. Events

    Directory of Open Access Journals (Sweden)

    Igor V. Karyakin

    2016-02-01

    Full Text Available The 9th ARRCN Symposium 2015 was held during 21st–25th October 2015 at the Novotel Hotel, Chumphon, Thailand, one of the most favored travel destinations in Asia. The 10th ARRCN Symposium 2017 will be held during October 2017 in the Davao, Philippines. International Symposium on the Montagu's Harrier (Circus pygargus «The Montagu's Harrier in Europe. Status. Threats. Protection», organized by the environmental organization «Landesbund für Vogelschutz in Bayern e.V.» (LBV was held on November 20-22, 2015 in Germany. The location of this event was the city of Wurzburg in Bavaria.

  3. Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

    International Nuclear Information System (INIS)

    Treutterer, W.; Neu, G.; Rapson, C.; Raupp, G.; Zasche, D.; Zehetbauer, T.

    2013-01-01

    Highlights: •Event detection and exception handling is integrated in control system architecture. •Pulse control with local exception handling and pulse supervision with central exception handling are strictly separated. •Local exception handling limits the effect of an exception to a minimal part of the controlled system. •Central Exception Handling solves problems requiring coordinated action of multiple control components. -- Abstract: Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated as events. Similarly, technical systems are also subject to events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits. Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events, however, the complexity of even a medium-sized device as ASDEX Upgrade requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects events and handles exceptions in two stages: locally and centrally. The goal of local exception handling is to limit the effect of an unexpected or asynchronous event to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control

  4. Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

    Energy Technology Data Exchange (ETDEWEB)

    Treutterer, W., E-mail: Wolfgang.Treutterer@ipp.mpg.de; Neu, G.; Rapson, C.; Raupp, G.; Zasche, D.; Zehetbauer, T.

    2013-10-15

    Highlights: •Event detection and exception handling is integrated in control system architecture. •Pulse control with local exception handling and pulse supervision with central exception handling are strictly separated. •Local exception handling limits the effect of an exception to a minimal part of the controlled system. •Central Exception Handling solves problems requiring coordinated action of multiple control components. -- Abstract: Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated as events. Similarly, technical systems are also subject to events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits. Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events, however, the complexity of even a medium-sized device as ASDEX Upgrade requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects events and handles exceptions in two stages: locally and centrally. The goal of local exception handling is to limit the effect of an unexpected or asynchronous event to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control

  5. Detection of patient movement during CBCT examination using video observation compared with an accelerometer-gyroscope tracking system.

    Science.gov (United States)

    Spin-Neto, Rubens; Matzen, Louise H; Schropp, Lars; Gotfredsen, Erik; Wenzel, Ann

    2017-02-01

    To compare video observation (VO) with a novel three-dimensional registration method, based on an accelerometer-gyroscope (AG) system, to detect patient movement during CBCT examination. The movements were further analyzed according to complexity and patient age. In 181 patients (118 females/63 males; age average 30 years, range: 9-84 years), 206 CBCT examinations were performed, which were video-recorded during examination. An AG was, at the same time, attached to the patient head to track head position in three dimensions. Three observers scored patient movement (yes/no) by VO. AG provided movement data on the x-, y- and z-axes. Thresholds for AG-based registration were defined at 0.5, 1, 2, 3 and 4 mm (movement distance). Movement detected by VO was compared with that registered by AG, according to movement complexity (uniplanar vs multiplanar, as defined by AG) and patient age (≤15, 16-30 and ≥31 years). According to AG, movement ≥0.5 mm was present in 160 (77.7%) examinations. According to VO, movement was present in 46 (22.3%) examinations. One VO-detected movement was not registered by AG. Overall, VO did not detect 71.9% of the movements registered by AG at the 0.5-mm threshold. At a movement distance ≥4 mm, 20% of the AG-registered movements were not detected by VO. Multiplanar movements such as lateral head rotation (72.1%) and nodding/swallowing (52.6%) were more often detected by VO in comparison with uniplanar movements, such as head lifting (33.6%) and anteroposterior translation (35.6%), at the 0.5-mm threshold. The prevalence of patients who move was highest in patients younger than 16 years (64.3% for VO and 92.3% for AG-based registration at the 0.5-mm threshold). AG-based movement registration resulted in a higher prevalence of patient movement during CBCT examination than VO-based registration. Also, AG-registered multiplanar movements were more frequently detected by VO than uniplanar movements. The prevalence of patients who move

  6. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  7. Semi-automated detection of fractional shortening in zebrafish embryo heart videos

    Directory of Open Access Journals (Sweden)

    Nasrat Sara

    2016-09-01

    Full Text Available Quantifying cardiac functions in model organisms like embryonic zebrafish is of high importance in small molecule screens for new therapeutic compounds. One relevant cardiac parameter is the fractional shortening (FS. A method for semi-automatic quantification of FS in video recordings of zebrafish embryo hearts is presented. The software provides automated visual information about the end-systolic and end-diastolic stages of the heart by displaying corresponding colored lines into a Motion-mode display. After manually marking the ventricle diameters in frames of end-systolic and end-diastolic stages, the FS is calculated. The software was evaluated by comparing the results of the determination of FS with results obtained from another established method. Correlations of 0.96 < r < 0.99 between the two methods were found indicating that the new software provides comparable results for the determination of the FS.

  8. Ontology-based knowledge management for personalized adverse drug events detection.

    Science.gov (United States)

    Cao, Feng; Sun, Xingzhi; Wang, Xiaoyuan; Li, Bo; Li, Jing; Pan, Yue

    2011-01-01

    Since Adverse Drug Event (ADE) has become a leading cause of death around the world, there arises high demand for helping clinicians or patients to identify possible hazards from drug effects. Motivated by this, we present a personalized ADE detection system, with the focus on applying ontology-based knowledge management techniques to enhance ADE detection services. The development of electronic health records makes it possible to automate the personalized ADE detection, i.e., to take patient clinical conditions into account during ADE detection. Specifically, we define the ADE ontology to uniformly manage the ADE knowledge from multiple sources. We take advantage of the rich semantics from the terminology SNOMED-CT and apply it to ADE detection via the semantic query and reasoning.

  9. Pollen Bearing Honey Bee Detection in Hive Entrance Video Recorded by Remote Embedded System for Pollination Monitoring

    Science.gov (United States)

    Babic, Z.; Pilipovic, R.; Risojevic, V.; Mirjanic, G.

    2016-06-01

    Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.

  10. Detections of Planets in Binaries Through the Channel of Chang–Refsdal Gravitational Lensing Events

    Energy Technology Data Exchange (ETDEWEB)

    Han, Cheongho [Department of Physics, Chungbuk National University, Cheongju 361-763 (Korea, Republic of); Shin, In-Gu; Jung, Youn Kil [Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138 (United States)

    2017-02-01

    Chang–Refsdal (C–R) lensing, which refers to the gravitational lensing of a point mass perturbed by a constant external shear, provides a good approximation in describing lensing behaviors of either a very wide or a very close binary lens. C–R lensing events, which are identified by short-term anomalies near the peak of high-magnification lensing light curves, are routinely detected from lensing surveys, but not much attention is paid to them. In this paper, we point out that C–R lensing events provide an important channel to detect planets in binaries, both in close and wide binary systems. Detecting planets through the C–R lensing event channel is possible because the planet-induced perturbation occurs in the same region of the C–R lensing-induced anomaly and thus the existence of the planet can be identified by the additional deviation in the central perturbation. By presenting the analysis of the actually observed C–R lensing event OGLE-2015-BLG-1319, we demonstrate that dense and high-precision coverage of a C–R lensing-induced perturbation can provide a strong constraint on the existence of a planet in a wide range of planet parameters. The sample of an increased number of microlensing planets in binary systems will provide important observational constraints in giving shape to the details of planet formation, which have been restricted to the case of single stars to date.

  11. Semi-automated camera trap image processing for the detection of ungulate fence crossing events.

    Science.gov (United States)

    Janzen, Michael; Visser, Kaitlyn; Visscher, Darcy; MacLeod, Ian; Vujnovic, Dragomir; Vujnovic, Ksenija

    2017-09-27

    Remote cameras are an increasingly important tool for ecological research. While remote camera traps collect field data with minimal human attention, the images they collect require post-processing and characterization before it can be ecologically and statistically analyzed, requiring the input of substantial time and money from researchers. The need for post-processing is due, in part, to a high incidence of non-target images. We developed a stand-alone semi-automated computer program to aid in image processing, categorization, and data reduction by employing background subtraction and histogram rules. Unlike previous work that uses video as input, our program uses still camera trap images. The program was developed for an ungulate fence crossing project and tested against an image dataset which had been previously processed by a human operator. Our program placed images into categories representing the confidence of a particular sequence of images containing a fence crossing event. This resulted in a reduction of 54.8% of images that required further human operator characterization while retaining 72.6% of the known fence crossing events. This program can provide researchers using remote camera data the ability to reduce the time and cost required for image post-processing and characterization. Further, we discuss how this procedure might be generalized to situations not specifically related to animal use of linear features.

  12. Optimizing a neural network for detection of moving vehicles in video

    NARCIS (Netherlands)

    Fischer, N.M.; Kruithof, M.C.; Bouma, H.

    2017-01-01

    In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing,

  13. Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos

    DEFF Research Database (Denmark)

    Bahnsen, Chris; Moeslund, Thomas B.

    2015-01-01

    and weather conditions. On this dataset, the detection performance of right turning vehicles, left turn- ing vehicles, and straight going cyclists are evaluated. Results from both systems show good performance when detecting turning vehicles with a precision of 0.90 and above depending on environmental...

  14. Fundamental aspects of seismic event detection, magnitude estimation and their interrelation

    International Nuclear Information System (INIS)

    Ringdal, F.

    1977-01-01

    The main common subject of the papers forming this thesis is statistical model development within the seismological disciplines of seismic event detection and event magnitude estimation. As more high quality seismic data become available as a result of recent seismic network developments, the opportunity will exist for large scale application and further refinement of these models. It is hoped that the work presented here will facilitate improved understanding of the basic issues, both within earthquake-explosion discrimination, in the framework of which most of this work originated, and in seismology in general. (Auth.)

  15. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis

    Science.gov (United States)

    Vilar, Santiago; Harpaz, Rave; Chase, Herbert S; Costanzi, Stefano; Rabadan, Raul

    2011-01-01

    Background Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. Objective To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. Results The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. Conclusion The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection. PMID:21946238

  16. Microfluidic Arrayed Lab-On-A-Chip for Electrochemical Capacitive Detection of DNA Hybridization Events.

    Science.gov (United States)

    Ben-Yoav, Hadar; Dykstra, Peter H; Bentley, William E; Ghodssi, Reza

    2017-01-01

    A microfluidic electrochemical lab-on-a-chip (LOC) device for DNA hybridization detection has been developed. The device comprises a 3 × 3 array of microelectrodes integrated with a dual layer microfluidic valved manipulation system that provides controlled and automated capabilities for high throughput analysis of microliter volume samples. The surface of the microelectrodes is functionalized with single-stranded DNA (ssDNA) probes which enable specific detection of complementary ssDNA targets. These targets are detected by a capacitive technique which measures dielectric variation at the microelectrode-electrolyte interface due to DNA hybridization events. A quantitative analysis of the hybridization events is carried out based on a sensing modeling that includes detailed analysis of energy storage and dissipation components. By calculating these components during hybridization events the device is able to demonstrate specific and dose response sensing characteristics. The developed microfluidic LOC for DNA hybridization detection offers a technology for real-time and label-free assessment of genetic markers outside of laboratory settings, such as at the point-of-care or in-field environmental monitoring.

  17. Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

    Science.gov (United States)

    Li, Yunji; Wu, QingE; Peng, Li

    2018-01-23

    In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

  18. Integrated hydraulic and organophosphate pesticide injection simulations for enhancing event detection in water distribution systems.

    Science.gov (United States)

    Schwartz, Rafi; Lahav, Ori; Ostfeld, Avi

    2014-10-15

    As a complementary step towards solving the general event detection problem of water distribution systems, injection of the organophosphate pesticides, chlorpyrifos (CP) and parathion (PA), were simulated at various locations within example networks and hydraulic parameters were calculated over 24-h duration. The uniqueness of this study is that the chemical reactions and byproducts of the contaminants' oxidation were also simulated, as well as other indicative water quality parameters such as alkalinity, acidity, pH and the total concentration of free chlorine species. The information on the change in water quality parameters induced by the contaminant injection may facilitate on-line detection of an actual event involving this specific substance and pave the way to development of a generic methodology for detecting events involving introduction of pesticides into water distribution systems. Simulation of the contaminant injection was performed at several nodes within two different networks. For each injection, concentrations of the relevant contaminants' mother and daughter species, free chlorine species and water quality parameters, were simulated at nodes downstream of the injection location. The results indicate that injection of these substances can be detected at certain conditions by a very rapid drop in Cl2, functioning as the indicative parameter, as well as a drop in alkalinity concentration and a small decrease in pH, both functioning as supporting parameters, whose usage may reduce false positive alarms. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Detection of visual events along the apparent motion trace in patients with paranoid schizophrenia.

    Science.gov (United States)

    Sanders, Lia Lira Olivier; Muckli, Lars; de Millas, Walter; Lautenschlager, Marion; Heinz, Andreas; Kathmann, Norbert; Sterzer, Philipp

    2012-07-30

    Dysfunctional prediction in sensory processing has been suggested as a possible causal mechanism in the development of delusions in patients with schizophrenia. Previous studies in healthy subjects have shown that while the perception of apparent motion can mask visual events along the illusory motion trace, such motion masking is reduced when events are spatio-temporally compatible with the illusion, and, therefore, predictable. Here we tested the hypothesis that this specific detection advantage for predictable target stimuli on the apparent motion trace is reduced in patients with paranoid schizophrenia. Our data show that, although target detection along the illusory motion trace is generally impaired, both patients and healthy control participants detect predictable targets more often than unpredictable targets. Patients had a stronger motion masking effect when compared to controls. However, patients showed the same advantage in the detection of predictable targets as healthy control subjects. Our findings reveal stronger motion masking but intact prediction of visual events along the apparent motion trace in patients with paranoid schizophrenia and suggest that the sensory prediction mechanism underlying apparent motion is not impaired in paranoid schizophrenia. Copyright © 2012. Published by Elsevier Ireland Ltd.

  20. Detecting PHG frames in wireless capsule endoscopy video by integrating rough global dominate-color with fine local texture features

    Science.gov (United States)

    Liu, Xiaoqi; Wang, Chengliang; Bai, Jianying; Liao, Guobin

    2018-02-01

    Portal hypertensive gastropathy (PHG) is common in gastrointestinal (GI) diseases, and a severe stage of PHG (S-PHG) is a source of gastrointestinal active bleeding. Generally, the diagnosis of PHG is made visually during endoscopic examination; compared with traditional endoscopy, (wireless capsule endoscopy) WCE with noninvasive and painless is chosen as a prevalent tool for visual observation of PHG. However, accurate measurement of WCE images with PHG is a difficult task due to faint contrast and confusing variations in background gastric mucosal tissue for physicians. Therefore, this paper proposes a comprehensive methodology to automatically detect S-PHG images in WCE video to help physicians accurately diagnose S-PHG. Firstly, a rough dominatecolor-tone extraction approach is proposed for better describing global color distribution information of gastric mucosa. Secondly, a hybrid two-layer texture acquisition model is designed by integrating co-occurrence matrix into local binary pattern to depict complex and unique gastric mucosal microstructure local variation. Finally, features of mucosal color and microstructure texture are merged into linear support vector machine to accomplish this automatic classification task. Experiments were implemented on an annotated data set including 1,050 SPHG and 1,370 normal images collected from 36 real patients of different nationalities, ages and genders. By comparison with three traditional texture extraction methods, our method, combined with experimental results, performs best in detection of S-PHG images in WCE video: the maximum of accuracy, sensitivity and specificity reach 0.90, 0.92 and 0.92 respectively.

  1. Visual content highlighting via automatic extraction of embedded captions on MPEG compressed video

    Science.gov (United States)

    Yeo, Boon-Lock; Liu, Bede

    1996-03-01

    Embedded captions in TV programs such as news broadcasts, documentaries and coverage of sports events provide important information on the underlying events. In digital video libraries, such captions represent a highly condensed form of key information on the contents of the video. In this paper we propose a scheme to automatically detect the presence of captions embedded in video frames. The proposed method operates on reduced image sequences which are efficiently reconstructed from compressed MPEG video and thus does not require full frame decompression. The detection, extraction and analysis of embedded captions help to capture the highlights of visual contents in video documents for better organization of video, to present succinctly the important messages embedded in the images, and to facilitate browsing, searching and retrieval of relevant clips.

  2. The MediaMill TRECVID 2011 semantic video search engine

    NARCIS (Netherlands)

    Snoek, C.G.M.; van de Sande, K.E.A.; Li, X.; Mazloom, M.; Jiang, Y.; Koelma, D.C.; Smeulders, A.W.M.

    2011-01-01

    In this paper we describe our TRECVID 2011 video retrieval experiments. The MediaMill team participated in two tasks: semantic indexing and multimedia event detection. The starting point for the MediaMill detection approach is our top-performing bag-of-words system of TRECVID 2010, which uses

  3. Latency and mode of error detection as reflected in Swedish licensee event reports

    Energy Technology Data Exchange (ETDEWEB)

    Svenson, Ola; Salo, Ilkka [Stockholm Univ., (Sweden). Dept. of Psychology

    2002-03-01

    Licensee event reports (LERs) from an industry provide important information feedback about safety to the industry itself, the regulators and to the public. LERs from four nuclear power reactors were analyzed to find out about detection times, mode of detection and qualitative differences in reports from different reactors. The reliability of the coding was satisfactory and measured as the covariance between the ratings from two independent judges. The results showed differences in detection time across the reactors. On the average about ten percent of the errors remained undetected for 100 weeks or more, but the great majority of errors were detected soon after their first appearance in the plant. On the average 40 percent of the errors were detected in regular tests and 40 per cent through alarms. Operators found about 10 per cent of the errors through noticing something abnormal in the plant. The remaining errors were detected in various other ways. There were qualitative differences between the LERs from the different reactors reflecting the different conditions in the plants. The number of reports differed by a magnitude 1:2 between the different plants. However, a greater number of LERs can indicate both higher safety standards (e.g., a greater willingness to report all possible events to be able to learn from them) and lower safety standards (e.g., reporting as few events as possible to make a good impression). It was pointed out that LERs are indispensable in order to maintain safety of an industry and that the differences between plants found in the analyses of this study indicate how error reports can be used to initiate further investigations for improved safety.

  4. Latency and mode of error detection as reflected in Swedish licensee event reports

    International Nuclear Information System (INIS)

    Svenson, Ola; Salo, Ilkka

    2002-03-01

    Licensee event reports (LERs) from an industry provide important information feedback about safety to the industry itself, the regulators and to the public. LERs from four nuclear power reactors were analyzed to find out about detection times, mode of detection and qualitative differences in reports from different reactors. The reliability of the coding was satisfactory and measured as the covariance between the ratings from two independent judges. The results showed differences in detection time across the reactors. On the average about ten percent of the errors remained undetected for 100 weeks or more, but the great majority of errors were detected soon after their first appearance in the plant. On the average 40 percent of the errors were detected in regular tests and 40 per cent through alarms. Operators found about 10 per cent of the errors through noticing something abnormal in the plant. The remaining errors were detected in various other ways. There were qualitative differences between the LERs from the different reactors reflecting the different conditions in the plants. The number of reports differed by a magnitude 1:2 between the different plants. However, a greater number of LERs can indicate both higher safety standards (e.g., a greater willingness to report all possible events to be able to learn from them) and lower safety standards (e.g., reporting as few events as possible to make a good impression). It was pointed out that LERs are indispensable in order to maintain safety of an industry and that the differences between plants found in the analyses of this study indicate how error reports can be used to initiate further investigations for improved safety

  5. Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams

    International Nuclear Information System (INIS)

    Perez, Rafael B.; Protopopescu, Vladimir A.; Worley, Brian Addison; Perez, Cristina

    2006-01-01

    The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method

  6. Energy Reconstruction for Events Detected in TES X-ray Detectors

    Science.gov (United States)

    Ceballos, M. T.; Cardiel, N.; Cobo, B.

    2015-09-01

    The processing of the X-ray events detected by a TES (Transition Edge Sensor) device (such as the one that will be proposed in the ESA AO call for instruments for the Athena mission (Nandra et al. 2013) as a high spectral resolution instrument, X-IFU (Barret et al. 2013)), is a several step procedure that starts with the detection of the current pulses in a noisy signal and ends up with their energy reconstruction. For this last stage, an energy calibration process is required to convert the pseudo energies measured in the detector to the real energies of the incoming photons, accounting for possible nonlinearity effects in the detector. We present the details of the energy calibration algorithm we implemented as the last part of the Event Processing software that we are developing for the X-IFU instrument, that permits the calculation of the calibration constants in an analytical way.

  7. A novel CUSUM-based approach for event detection in smart metering

    Science.gov (United States)

    Zhu, Zhicheng; Zhang, Shuai; Wei, Zhiqiang; Yin, Bo; Huang, Xianqing

    2018-03-01

    Non-intrusive load monitoring (NILM) plays such a significant role in raising consumer awareness on household electricity use to reduce overall energy consumption in the society. With regard to monitoring low power load, many researchers have introduced CUSUM into the NILM system, since the traditional event detection method is not as effective as expected. Due to the fact that the original CUSUM faces limitations given the small shift is below threshold, we therefore improve the test statistic which allows permissible deviation to gradually rise as the data size increases. This paper proposes a novel event detection and corresponding criterion that could be used in NILM systems to recognize transient states and to help the labelling task. Its performance has been tested in a real scenario where eight different appliances are connected to main line of electric power.

  8. Automatic detection of esophageal pressure events. Is there an alternative to rule-based criteria?

    DEFF Research Database (Denmark)

    Kruse-Andersen, S; Rütz, K; Kolberg, Jens Godsk

    1995-01-01

    of relevant pressure peaks at the various recording levels. Until now, this selection has been performed entirely by rule-based systems, requiring each pressure deflection to fit within predefined rigid numerical limits in order to be detected. However, due to great variations in the shapes of the pressure...... curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...

  9. Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei Jiang

    2012-01-01

    Full Text Available Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.

  10. Flow detection via sparse frame analysis for suspicious event recognition in infrared imagery

    Science.gov (United States)

    Fernandes, Henrique C.; Batista, Marcos A.; Barcelos, Celia A. Z.; Maldague, Xavier P. V.

    2013-05-01

    It is becoming increasingly evident that intelligent systems are very bene¯cial for society and that the further development of such systems is necessary to continue to improve society's quality of life. One area that has drawn the attention of recent research is the development of automatic surveillance systems. In our work we outline a system capable of monitoring an uncontrolled area (an outside parking lot) using infrared imagery and recognizing suspicious events in this area. The ¯rst step is to identify moving objects and segment them from the scene's background. Our approach is based on a dynamic background-subtraction technique which robustly adapts detection to illumination changes. It is analyzed only regions where movement is occurring, ignoring in°uence of pixels from regions where there is no movement, to segment moving objects. Regions where movement is occurring are identi¯ed using °ow detection via sparse frame analysis. During the tracking process the objects are classi¯ed into two categories: Persons and Vehicles, based on features such as size and velocity. The last step is to recognize suspicious events that may occur in the scene. Since the objects are correctly segmented and classi¯ed it is possible to identify those events using features such as velocity and time spent motionless in one spot. In this paper we recognize the suspicious event suspicion of object(s) theft from inside a parked vehicle at spot X by a person" and results show that the use of °ow detection increases the recognition of this suspicious event from 78:57% to 92:85%.

  11. Detection of invisible and crucial events: from seismic fluctuations to the war against terrorism

    Energy Technology Data Exchange (ETDEWEB)

    Allegrini, Paolo; Fronzoni, Leone; Grigolini, Paolo; Latora, Vito; Mega, Mirko S.; Palatella, Luigi E-mail: luigi.palatella@df.unipi.it; Rapisarda, Andrea; Vinciguerra, Sergio

    2004-04-01

    We argue that the recent discovery of the non-Poissonian statistics of the seismic main-shocks is a special case of a more general approach to the detection of the distribution of the time increments between one crucial but invisible event and the next. We make the conjecture that the proposed approach can be applied to the analysis of terrorist network with significant benefits for the Intelligence Community.

  12. Automated Feature and Event Detection with SDO AIA and HMI Data

    Science.gov (United States)

    Davey, Alisdair; Martens, P. C. H.; Attrill, G. D. R.; Engell, A.; Farid, S.; Grigis, P. C.; Kasper, J.; Korreck, K.; Saar, S. H.; Su, Y.; Testa, P.; Wills-Davey, M.; Savcheva, A.; Bernasconi, P. N.; Raouafi, N.-E.; Delouille, V. A.; Hochedez, J. F..; Cirtain, J. W.; Deforest, C. E.; Angryk, R. A.; de Moortel, I.; Wiegelmann, T.; Georgouli, M. K.; McAteer, R. T. J.; Hurlburt, N.; Timmons, R.

    The Solar Dynamics Observatory (SDO) represents a new frontier in quantity and quality of solar data. At about 1.5 TB/day, the data will not be easily digestible by solar physicists using the same methods that have been employed for images from previous missions. In order for solar scientists to use the SDO data effectively they need meta-data that will allow them to identify and retrieve data sets that address their particular science questions. We are building a comprehensive computer vision pipeline for SDO, abstracting complete metadata on many of the features and events detectable on the Sun without human intervention. Our project unites more than a dozen individual, existing codes into a systematic tool that can be used by the entire solar community. The feature finding codes will run as part of the SDO Event Detection System (EDS) at the Joint Science Operations Center (JSOC; joint between Stanford and LMSAL). The metadata produced will be stored in the Heliophysics Event Knowledgebase (HEK), which will be accessible on-line for the rest of the world directly or via the Virtual Solar Observatory (VSO) . Solar scientists will be able to use the HEK to select event and feature data to download for science studies.

  13. VAP/VAT: video analytics platform and test bed for testing and deploying video analytics

    Science.gov (United States)

    Gorodnichy, Dmitry O.; Dubrofsky, Elan

    2010-04-01

    Deploying Video Analytics in operational environments is extremely challenging. This paper presents a methodological approach developed by the Video Surveillance and Biometrics Section (VSB) of the Science and Engineering Directorate (S&E) of the Canada Border Services Agency (CBSA) to resolve these problems. A three-phase approach to enable VA deployment within an operational agency is presented and the Video Analytics Platform and Testbed (VAP/VAT) developed by the VSB section is introduced. In addition to allowing the integration of third party and in-house built VA codes into an existing video surveillance infrastructure, VAP/VAT also allows the agency to conduct an unbiased performance evaluation of the cameras and VA software available on the market. VAP/VAT consists of two components: EventCapture, which serves to Automatically detect a "Visual Event", and EventBrowser, which serves to Display & Peruse of "Visual Details" captured at the "Visual Event". To deal with Open architecture as well as with Closed architecture cameras, two video-feed capture mechanisms have been developed within the EventCapture component: IPCamCapture and ScreenCapture.

  14. Sudden Event Recognition: A Survey

    Directory of Open Access Journals (Sweden)

    Mohd Asyraf Zulkifley

    2013-08-01

    Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

  15. Detecting adverse events in surgery: comparing events detected by the Veterans Health Administration Surgical Quality Improvement Program and the Patient Safety Indicators.

    Science.gov (United States)

    Mull, Hillary J; Borzecki, Ann M; Loveland, Susan; Hickson, Kathleen; Chen, Qi; MacDonald, Sally; Shin, Marlena H; Cevasco, Marisa; Itani, Kamal M F; Rosen, Amy K

    2014-04-01

    The Patient Safety Indicators (PSIs) use administrative data to screen for select adverse events (AEs). In this study, VA Surgical Quality Improvement Program (VASQIP) chart review data were used as the gold standard to measure the criterion validity of 5 surgical PSIs. Independent chart review was also used to determine reasons for PSI errors. The sensitivity, specificity, and positive predictive value of PSI software version 4.1a were calculated among Veterans Health Administration hospitalizations (2003-2007) reviewed by VASQIP (n = 268,771). Nurses re-reviewed a sample of hospitalizations for which PSI and VASQIP AE detection disagreed. Sensitivities ranged from 31% to 68%, specificities from 99.1% to 99.8%, and positive predictive values from 31% to 72%. Reviewers found that coding errors accounted for some PSI-VASQIP disagreement; some disagreement was also the result of differences in AE definitions. These results suggest that the PSIs have moderate criterion validity; however, some surgical PSIs detect different AEs than VASQIP. Future research should explore using both methods to evaluate surgical quality. Published by Elsevier Inc.

  16. Temporal and spatial predictability of an irrelevant event differently affect detection and memory of items in a visual sequence

    Directory of Open Access Journals (Sweden)

    Junji eOhyama

    2016-02-01

    Full Text Available We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition, it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection reaction times were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images.

  17. Computer Games as a Tool for Implementation of Memory Policy (on the Example of Displaying Events of The Great Patriotic War in Video Games

    Directory of Open Access Journals (Sweden)

    Сергей Игоревич Белов

    2018-12-01

    Full Text Available The presented work is devoted to the study of the practice of using computer games as a tool of the memory policy. The relevance of this study determines both the growth of the importance of video games as a means of forming ideas about the events of the past, and a low degree of study of this topic. As the goal of the author's research, the research is to identify the prospects for using computer games as an instrument for implementing the memory policy within the framework of the case of the events of the Great Patriotic War. The empirical base of the work was formed due to the generalization of the content of such video games as “Call of Duty 1”, “Call of Duty 14: WWII”, “Company of Heroes 2” and “Commandos 3: Destination Berlin”. The methodological base of the research is formed due to the involvement of elements of descriptive political analysis, the theory of operant conditioning B.F. Skinner and the concept of social identity H. Tajfel and J. Turner. The author comes to the conclusion that familiarization of users with the designated games contributes to the consolidation in the minds of users of negative stereotypes regarding the participation of the Red Army in the Great Patriotic War. The process of integration of negative images is carried out using the methods of operant conditioning. The integration of the system of negative images into the mass consciousness of the inhabitants of the post-Soviet space makes it difficult to preserve the remnants of Soviet political symbols and elements constructed on their basis identity. The author puts forward the hypothesis that in the case of complete desovietization of the public policy space in the states that emerged as a result of the collapse of the USSR, the task of revising the history of the Great Patriotic War will be greatly facilitated, and with the subsequent departure from the life of the last eyewitnesses of the relevant events, achieving this goal will be only a

  18. Method for the depth corrected detection of ionizing events from a co-planar grids sensor

    Science.gov (United States)

    De Geronimo, Gianluigi [Syosset, NY; Bolotnikov, Aleksey E [South Setauket, NY; Carini, Gabriella [Port Jefferson, NY

    2009-05-12

    A method for the detection of ionizing events utilizing a co-planar grids sensor comprising a semiconductor substrate, cathode electrode, collecting grid and non-collecting grid. The semiconductor substrate is sensitive to ionizing radiation. A voltage less than 0 Volts is applied to the cathode electrode. A voltage greater than the voltage applied to the cathode is applied to the non-collecting grid. A voltage greater than the voltage applied to the non-collecting grid is applied to the collecting grid. The collecting grid and the non-collecting grid are summed and subtracted creating a sum and difference respectively. The difference and sum are divided creating a ratio. A gain coefficient factor for each depth (distance between the ionizing event and the collecting grid) is determined, whereby the difference between the collecting electrode and the non-collecting electrode multiplied by the corresponding gain coefficient is the depth corrected energy of an ionizing event. Therefore, the energy of each ionizing event is the difference between the collecting grid and the non-collecting grid multiplied by the corresponding gain coefficient. The depth of the ionizing event can also be determined from the ratio.

  19. Presentation of the results of a Bayesian automatic event detection and localization program to human analysts

    Science.gov (United States)

    Kushida, N.; Kebede, F.; Feitio, P.; Le Bras, R.

    2016-12-01

    The Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has been developing and testing NET-VISA (Arora et al., 2013), a Bayesian automatic event detection and localization program, and evaluating its performance in a realistic operational mode. In our preliminary testing at the CTBTO, NET-VISA shows better performance than its currently operating automatic localization program. However, given CTBTO's role and its international context, a new technology should be introduced cautiously when it replaces a key piece of the automatic processing. We integrated the results of NET-VISA into the Analyst Review Station, extensively used by the analysts so that they can check the accuracy and robustness of the Bayesian approach. We expect the workload of the analysts to be reduced because of the better performance of NET-VISA in finding missed events and getting a more complete set of stations than the current system which has been operating for nearly twenty years. The results of a series of tests indicate that the expectations born from the automatic tests, which show an overall overlap improvement of 11%, meaning that the missed events rate is cut by 42%, hold for the integrated interactive module as well. New events are found by analysts, which qualify for the CTBTO Reviewed Event Bulletin, beyond the ones analyzed through the standard procedures. Arora, N., Russell, S., and Sudderth, E., NET-VISA: Network Processing Vertically Integrated Seismic Analysis, 2013, Bull. Seismol. Soc. Am., 103, 709-729.

  20. Automatic lameness detection based on consecutive 3D-video recordings

    NARCIS (Netherlands)

    Hertem, van T.; Viazzi, S.; Steensels, M.; Maltz, E.; Antler, A.; Alchanatis, V.; Schlageter-Tello, A.; Lokhorst, C.; Romanini, C.E.B.; Bahr, C.; Berckmans, D.; Halachmi, I.

    2014-01-01

    Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive

  1. Detection of distorted frames in retinal video-sequences via machine learning

    Science.gov (United States)

    Kolar, Radim; Liberdova, Ivana; Odstrcilik, Jan; Hracho, Michal; Tornow, Ralf P.

    2017-07-01

    This paper describes detection of distorted frames in retinal sequences based on set of global features extracted from each frame. The feature vector is consequently used in classification step, in which three types of classifiers are tested. The best classification accuracy 96% has been achieved with support vector machine approach.

  2. Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

    Energy Technology Data Exchange (ETDEWEB)

    Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

    2007-01-30

    In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

  3. Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

    International Nuclear Information System (INIS)

    Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

    2007-01-01

    In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

  4. A Novel Event-Based Incipient Slip Detection Using Dynamic Active-Pixel Vision Sensor (DAVIS).

    Science.gov (United States)

    Rigi, Amin; Baghaei Naeini, Fariborz; Makris, Dimitrios; Zweiri, Yahya

    2018-01-24

    In this paper, a novel approach to detect incipient slip based on the contact area between a transparent silicone medium and different objects using a neuromorphic event-based vision sensor (DAVIS) is proposed. Event-based algorithms are developed to detect incipient slip, slip, stress distribution and object vibration. Thirty-seven experiments were performed on five objects with different sizes, shapes, materials and weights to compare precision and response time of the proposed approach. The proposed approach is validated by using a high speed constitutional camera (1000 FPS). The results indicate that the sensor can detect incipient slippage with an average of 44.1 ms latency in unstructured environment for various objects. It is worth mentioning that the experiments were conducted in an uncontrolled experimental environment, therefore adding high noise levels that affected results significantly. However, eleven of the experiments had a detection latency below 10 ms which shows the capability of this method. The results are very promising and show a high potential of the sensor being used for manipulation applications especially in dynamic environments.

  5. Real-Time Straight-Line Detection for XGA-Size Videos by Hough Transform with Parallelized Voting Procedures.

    Science.gov (United States)

    Guan, Jungang; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Mattausch, Hans Jürgen

    2017-01-30

    The Hough Transform (HT) is a method for extracting straight lines from an edge image. The main limitations of the HT for usage in actual applications are computation time and storage requirements. This paper reports a hardware architecture for HT implementation on a Field Programmable Gate Array (FPGA) with parallelized voting procedure. The 2-dimensional accumulator array, namely the Hough space in parametric form (ρ, θ), for computing the strength of each line by a voting mechanism is mapped on a 1-dimensional array with regular increments of θ. Then, this Hough space is divided into a number of parallel parts. The computation of (ρ, θ) for the edge pixels and the voting procedure for straight-line determination are therefore executable in parallel. In addition, a synchronized initialization for the Hough space further increases the speed of straight-line detection, so that XGA video processing becomes possible. The designed prototype system has been synthesized on a DE4 platform with a Stratix-IV FPGA device. In the application of road-lane detection, the average processing speed of this HT implementation is 5.4ms per XGA-frame at 200 MHz working frequency.

  6. Automatic detection of lexical change: an auditory event-related potential study.

    Science.gov (United States)

    Muller-Gass, Alexandra; Roye, Anja; Kirmse, Ursula; Saupe, Katja; Jacobsen, Thomas; Schröger, Erich

    2007-10-29

    We investigated the detection of rare task-irrelevant changes in the lexical status of speech stimuli. Participants performed a nonlinguistic task on word and pseudoword stimuli that occurred, in separate conditions, rarely or frequently. Task performance for pseudowords was deteriorated relative to words, suggesting unintentional lexical analysis. Furthermore, rare word and pseudoword changes had a similar effect on the event-related potentials, starting as early as 165 ms. This is the first demonstration of the automatic detection of change in lexical status that is not based on a co-occurring acoustic change. We propose that, following lexical analysis of the incoming stimuli, a mental representation of the lexical regularity is formed and used as a template against which lexical change can be detected.

  7. High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm.

    Science.gov (United States)

    Fan, Kai; Sun, Xingzhi; Tao, Ying; Xu, Linhao; Wang, Chen; Mao, Xianling; Peng, Bo; Pan, Yue

    2010-11-13

    Post-marketing pharmacovigilance is important for public health, as many Adverse Drug Events (ADEs) are unknown when those drugs were approved for marketing. However, due to the large number of reported drugs and drug combinations, detecting ADE signals by mining these reports is becoming a challenging task in terms of computational complexity. Recently, a parallel programming model, MapReduce has been introduced by Google to support large-scale data intensive applications. In this study, we proposed a MapReduce-based algorithm, for common ADE detection approach, Proportional Reporting Ratio (PRR), and tested it in mining spontaneous ADE reports from FDA. The purpose is to investigate the possibility of using MapReduce principle to speed up biomedical data mining tasks using this pharmacovigilance case as one specific example. The results demonstrated that MapReduce programming model could improve the performance of common signal detection algorithm for pharmacovigilance in a distributed computation environment at approximately liner speedup rates.

  8. High-Speed Video System for Micro-Expression Detection and Recognition

    Directory of Open Access Journals (Sweden)

    Diana Borza

    2017-12-01

    Full Text Available Micro-expressions play an essential part in understanding non-verbal communication and deceit detection. They are involuntary, brief facial movements that are shown when a person is trying to conceal something. Automatic analysis of micro-expression is challenging due to their low amplitude and to their short duration (they occur as fast as 1/15 to 1/25 of a second. We propose a fully micro-expression analysis system consisting of a high-speed image acquisition setup and a software framework which can detect the frames when the micro-expressions occurred as well as determine the type of the emerged expression. The detection and classification methods use fast and simple motion descriptors based on absolute image differences. The recognition module it only involves the computation of several 2D Gaussian probabilities. The software framework was tested on two publicly available high speed micro-expression databases and the whole system was used to acquire new data. The experiments we performed show that our solution outperforms state of the art works which use more complex and computationally intensive descriptors.

  9. EVENT DETECTION USING MOBILE PHONE MASS GPS DATA AND THEIR RELIAVILITY VERIFICATION BY DMSP/OLS NIGHT LIGHT IMAGE

    Directory of Open Access Journals (Sweden)

    A. Yuki

    2016-06-01

    Full Text Available In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an “Event,” all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

  10. [Detection of adverse events in hospitalized adult patients by using the Global Trigger Tool method].

    Science.gov (United States)

    Guzmán-Ruiz, O; Ruiz-López, P; Gómez-Cámara, A; Ramírez-Martín, M

    2015-01-01

    To identify and characterize adverse events (AE) in an Internal Medicine Department of a district hospital using an extension of the Global Trigger Tool (GTT), analyzing the diagnostic validity of the tool. An observational, analytical, descriptive and retrospective study was conducted on 2013 clinical charts from an Internal Medicine Department in order to detect EA through the identification of 'triggers' (an event often related to an AE). The 'triggers' and AE were located by systematic review of clinical documentation. The AE were characterized after they were identified. A total of 149 AE were detected in 291 clinical charts during 2013, of which 75.3% were detected directly by the tool, while the rest were not associated with a trigger. The percentage of charts that had at least one AE was 35.4%. The most frequent AE found was pressure ulcer (12%), followed by delirium, constipation, nosocomial respiratory infection and altered level of consciousness by drugs. Almost half (47.6%) of the AE were related to drug use, and 32.2% of all AE were considered preventable. The tool demonstrated a sensitivity of 91.3% (95%CI: 88.9-93.2) and a specificity of 32.5% (95%CI: 29.9-35.1). It had a positive predictive value of 42.5% (95%CI: 40.1-45.1) and a negative predictive value of 87.1% (95%CI: 83.8-89.9). The tool used in this study is valid, useful and reproducible for the detection of AE. It also serves to determine rates of injury and to observe their progression over time. A high frequency of both AE and preventable events were observed in this study. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

  11. Acoustic Neuroma Educational Video

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  12. Predictive modeling of structured electronic health records for adverse drug event detection.

    Science.gov (United States)

    Zhao, Jing; Henriksson, Aron; Asker, Lars; Boström, Henrik

    2015-01-01

    The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and

  13. An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs

    Institute of Scientific and Technical Information of China (English)

    Ruifeng Xu; Jiannan Hu; Qin Lu; Dongyin Wu; Lin Gui

    2017-01-01

    In this paper,we present a new challenging task for emotion analysis,namely emotion cause extraction.In this task,we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion,rather than the regular emotion classification or emotion component extraction.Since there is no open dataset for this task available,we first designed and annotated an emotion cause dataset which follows the scheme of W3C Emotion Markup Language.We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events.Since the distribution of events is imbalanced in the training data,we propose an under-sampling-based bagging algorithm to solve this problem.Even with a limited training set,the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method.Evaluations show that our approach achieves an F-measure 7.04% higher than the state-of-the-art methods.

  14. Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

    Science.gov (United States)

    LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

    2017-12-01

    With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

  15. The necessity of recognizing all events in x-ray detection

    International Nuclear Information System (INIS)

    Papp, T.; Maxwell, J.A.; Papp, A.T.

    2008-01-01

    -ray detection. Examples will be given in detection of x-rays in nuclear backgrounds, and in industrial measurements for ROHS and WEEE compliance with input rates of up to several hundred thousands counts per seconds. The availability of all the events allows one to see the other part of the spectrum, and thus offer explanations why the basic parameters are in such a bad shape

  16. Performance Evaluation of Wireless Sensor Networks for Event-Detection with Shadowing-Induced Radio Irregularities

    Directory of Open Access Journals (Sweden)

    Giuseppe De Marco

    2007-01-01

    Full Text Available In this paper, we study a particular application of wireless sensor networks for event-detection and tracking. In this kind of application, the transport of data is simplified, and guaranteeing a minimum number of packets at the monitoring node is the only constraint on the performance of the sensor network. This minimum number of packets is called event-reliability. Contrary to other studies on the subject, here we consider the behavior of such a network in presence of a realistic radio model, such as the shadowing of the radio signal. With this setting, we extend our previous analysis of the event-reliability approach for the transport of data. In particular, both regular and random networks are considered. The contribute of this work is to show via simulations that, in the presence of randomness or irregularities in the radio channel, the event-reliability can be jeopardized, that is the constraint on the minimum number of packets at the sink node could not be satisfied.

  17. Real-time movement detection and analysis for video surveillance applications

    Science.gov (United States)

    Hueber, Nicolas; Hennequin, Christophe; Raymond, Pierre; Moeglin, Jean-Pierre

    2014-06-01

    Pedestrian movement along critical infrastructures like pipes, railways or highways, is of major interest in surveillance applications as well as its behavior in urban environment. The goal is to anticipate illicit or dangerous human activities. For this purpose, we propose an all-in-one small autonomous system which delivers high level statistics and reports alerts in specific cases. This situational awareness project leads us to manage efficiently the scene by performing movement analysis. A dynamic background extraction algorithm is developed to reach the degree of robustness against natural and urban environment perturbations and also to match the embedded implementation constraints. When changes are detected in the scene, specific patterns are applied to detect and highlight relevant movements. Depending on the applications, specific descriptors can be extracted and fused in order to reach a high level of interpretation. In this paper, our approach is applied to two operational use cases: pedestrian urban statistics and railway surveillance. In the first case, a grid of prototypes is deployed over a city centre to collect pedestrian movement statistics up to a macroscopic level of analysis. The results demonstrate the relevance of the delivered information; in particular, the flow density map highlights pedestrian preferential paths along the streets. In the second case, one prototype is set next to high speed train tracks to secure the area. The results exhibit a low false alarm rate and assess our approach of a large sensor network for delivering a precise operational picture without overwhelming a supervisor.

  18. Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray.

    Science.gov (United States)

    Xu, Xiaodan; Li, Yingcong; Zhao, Heng; Wen, Si-yuan; Wang, Sheng-qi; Huang, Jian; Huang, Kun-lun; Luo, Yun-bo

    2005-05-18

    To devise a rapid and reliable method for the detection and identification of genetically modified (GM) events, we developed a multiplex polymerase chain reaction (PCR) coupled with a DNA microarray system simultaneously aiming at many targets in a single reaction. The system included probes for screening gene, species reference gene, specific gene, construct-specific gene, event-specific gene, and internal and negative control genes. 18S rRNA was combined with species reference genes as internal controls to assess the efficiency of all reactions and to eliminate false negatives. Two sets of the multiplex PCR system were used to amplify four and five targets, respectively. Eight different structure genes could be detected and identified simultaneously for Roundup Ready soybean in a single microarray. The microarray specificity was validated by its ability to discriminate two GM maizes Bt176 and Bt11. The advantages of this method are its high specificity and greatly reduced false-positives and -negatives. The multiplex PCR coupled with microarray technology presented here is a rapid and reliable tool for the simultaneous detection of GM organism ingredients.

  19. Femtomolar detection of single mismatches by discriminant analysis of DNA hybridization events using gold nanoparticles.

    Science.gov (United States)

    Ma, Xingyi; Sim, Sang Jun

    2013-03-21

    Even though DNA-based nanosensors have been demonstrated for quantitative detection of analytes and diseases, hybridization events have never been numerically investigated for further understanding of DNA mediated interactions. Here, we developed a nanoscale platform with well-designed capture and detection gold nanoprobes to precisely evaluate the hybridization events. The capture gold nanoprobes were mono-laid on glass and the detection probes were fabricated via a novel competitive conjugation method. The two kinds of probes combined in a suitable orientation following the hybridization with the target. We found that hybridization efficiency was markedly dependent on electrostatic interactions between DNA strands, which can be tailored by adjusting the salt concentration of the incubation solution. Due to the much lower stability of the double helix formed by mismatches, the hybridization efficiencies of single mismatched (MMT) and perfectly matched DNA (PMT) were different. Therefore, we obtained an optimized salt concentration that allowed for discrimination of MMT from PMT without stringent control of temperature or pH. The results indicated this to be an ultrasensitive and precise nanosensor for the diagnosis of genetic diseases.

  20. Detection of genetically modified maize events in Brazilian maize-derived food products

    Directory of Open Access Journals (Sweden)

    Maria Regina Branquinho

    2013-09-01

    Full Text Available The Brazilian government has approved many transgenic maize lines for commercialization and has established a threshold of 1% for food labeling, which underscores need for monitoring programs. Thirty four samples including flours and different types of nacho chips were analyzed by conventional and real-time PCR in 2011 and 2012. The events MON810, Bt11, and TC1507 were detected in most of the samples, and NK603 was present only in the samples analyzed in 2012. The authorized lines GA21, T25, and the unauthorized Bt176 were not detected. All positive samples in the qualitative tests collected in 2011 showed a transgenic content higher than 1%, and none of them was correctly labeled. Regarding the samples collected in 2012, all positive samples were quantified higher than the threshold, and 47.0% were not correctly labeled. The overall results indicated that the major genetically modified organisms detected were MON810, TC1507, Bt11, and NK603 events. Some industries that had failed to label their products in 2011 started labeling them in 2012, demonstrating compliance with the current legislation observing the consumer rights. Although these results are encouraging, it has been clearly demonstrated the need for continuous monitoring programs to ensure consumers that food products are labeled properly.

  1. Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

    Science.gov (United States)

    Albayrak, Arif; Teng, William

    2015-01-01

    As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

  2. Very low frequency earthquakes (VLFEs) detected during episodic tremor and slip (ETS) events in Cascadia using a match filter method indicate repeating events

    Science.gov (United States)

    Hutchison, A. A.; Ghosh, A.

    2016-12-01

    Very low frequency earthquakes (VLFEs) occur in transitional zones of faults, releasing seismic energy in the 0.02-0.05 Hz frequency band over a 90 s duration and typically have magntitudes within the range of Mw 3.0-4.0. VLFEs can occur down-dip of the seismogenic zone, where they can transfer stress up-dip potentially bringing the locked zone closer to a critical failure stress. VLFEs also occur up-dip of the seismogenic zone in a region along the plate interface that can rupture coseismically during large megathrust events, such as the 2011 Tohoku-Oki earthquake [Ide et al., 2011]. VLFEs were first detected in Cascadia during the 2011 episodic tremor and slip (ETS) event, occurring coincidentally with tremor [Ghosh et al., 2015]. However, during the 2014 ETS event, VLFEs were spatially and temporally asynchronous with tremor activity [Hutchison and Ghosh, 2016]. Such contrasting behaviors remind us that the mechanics behind such events remain elusive, yet they are responsible for the largest portion of the moment release during an ETS event. Here, we apply a match filter method using known VLFEs as template events to detect additional VLFEs. Using a grid-search centroid moment tensor inversion method, we invert stacks of the resulting match filter detections to ensure moment tensor solutions are similar to that of the respective template events. Our ability to successfully employ a match filter method to VLFE detection in Cascadia intrinsically indicates that these events can be repeating, implying that the same asperities are likely responsible for generating multiple VLFEs.

  3. An automatic eye detection and tracking technique for stereo video sequences

    Science.gov (United States)

    Paduru, Anirudh; Charalampidis, Dimitrios; Fouts, Brandon; Jovanovich, Kim

    2009-05-01

    Human-computer interfacing (HCI) describes a system or process with which two information processors, namely a human and a computer, attempt to exchange information. Computer-to-human (CtH) information transfer has been relatively effective through visual displays and sound devices. On the other hand, the human-tocomputer (HtC) interfacing avenue has yet to reach its full potential. For instance, the most common HtC communication means are the keyboard and mouse, which are already becoming a bottleneck in the effective transfer of information. The solution to the problem is the development of algorithms that allow the computer to understand human intentions based on their facial expressions, head motion patterns, and speech. In this work, we are investigating the feasibility of a stereo system to effectively determine the head position, including the head rotation angles, based on the detection of eye pupils.

  4. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

    Directory of Open Access Journals (Sweden)

    Andrew Cron

    Full Text Available Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less. Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a

  5. Signal Detection of Imipenem Compared to Other Drugs from Korea Adverse Event Reporting System Database.

    Science.gov (United States)

    Park, Kyounghoon; Soukavong, Mick; Kim, Jungmee; Kwon, Kyoung Eun; Jin, Xue Mei; Lee, Joongyub; Yang, Bo Ram; Park, Byung Joo

    2017-05-01

    To detect signals of adverse drug events after imipenem treatment using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD). We performed data mining using KIDS-KD, which was constructed using spontaneously reported adverse event (AE) reports between December 1988 and June 2014. We detected signals calculated the proportional reporting ratio, reporting odds ratio, and information component of imipenem. We defined a signal as any AE that satisfied all three indices. The signals were compared with drug labels of nine countries. There were 807582 spontaneous AEs reports in the KIDS-KD. Among those, the number of antibiotics related AEs was 192510; 3382 reports were associated with imipenem. The most common imipenem-associated AE was the drug eruption; 353 times. We calculated the signal by comparing with all other antibiotics and drugs; 58 and 53 signals satisfied the three methods. We compared the drug labelling information of nine countries, including the USA, the UK, Japan, Italy, Switzerland, Germany, France, Canada, and South Korea, and discovered that the following signals were currently not included in drug labels: hypokalemia, cardiac arrest, cardiac failure, Parkinson's syndrome, myocardial infarction, and prostate enlargement. Hypokalemia was an additional signal compared with all other antibiotics, and the other signals were not different compared with all other antibiotics and all other drugs. We detected new signals that were not listed on the drug labels of nine countries. However, further pharmacoepidemiologic research is needed to evaluate the causality of these signals. © Copyright: Yonsei University College of Medicine 2017

  6. Quench detection of fast plasma events for the JT-60SA central solenoid

    International Nuclear Information System (INIS)

    Murakami, Haruyuki; Kizu, Kaname; Tsuchiya, Katsuhiko; Kamiya, Koji; Takahashi, Yoshikazu; Yoshida, Kiyoshi

    2012-01-01

    Highlights: ► Pick-up coil method is used for the quench detection of JT-60SA magnet system. ► Disk-shaped pick-up coils are inserted in CS module to compensate inductive voltage. ► Applicability of pick-up coil is evaluated by two dimensional analysis. ► Pick-up coil is applicable whenever disruption, mini collapse and other plasma event. - Abstract: The JT-60 is planned to be modified to a full-superconducting tokamak referred to as the JT-60 Super Advanced (JT-60SA). The maximum temperature of the magnet during its quench might reach the temperature of higher than several hundreds Kelvin that will damage the superconducting magnet itself. The high precision quench detection system, therefore, is one of the key technologies in the superconducting magnet protection system. The pick-up coil method, which is using voltage taps to detect the normal voltage, is used for the quench detection of the JT-60SA superconducting magnet system. The disk-shaped pick-up coils are inserted in the central solenoid (CS) module to compensate the inductive voltage. In the previous study, the quench detection system requires a large number of pick-up coils. The reliability of quench detection system would be higher by simplifying the detection system such as reducing the number of pick-up coils. Simplifying the quench detection system is also important to reduce the total cost of the protection system. Hence the design method is improved by increasing optimizing parameters. The improved design method can reduce the number of pick-up coils without reducing the sensitivity of detection; consequently the protection system can be designed with higher reliability and lower cost. The applicability of the disk-shaped pick-up coil for quench detection system is evaluated by the two dimensional analysis. In the previous study, however, the analysis model only took into account the CS, EF (equilibrium field) coils and plasma. Therefore, applicability of the disk-shaped pick-up coil for

  7. Analysis of arrhythmic events is useful to detect lead failure earlier in patients followed by remote monitoring.

    Science.gov (United States)

    Nishii, Nobuhiro; Miyoshi, Akihito; Kubo, Motoki; Miyamoto, Masakazu; Morimoto, Yoshimasa; Kawada, Satoshi; Nakagawa, Koji; Watanabe, Atsuyuki; Nakamura, Kazufumi; Morita, Hiroshi; Ito, Hiroshi

    2018-03-01

    Remote monitoring (RM) has been advocated as the new standard of care for patients with cardiovascular implantable electronic devices (CIEDs). RM has allowed the early detection of adverse clinical events, such as arrhythmia, lead failure, and battery depletion. However, lead failure was often identified only by arrhythmic events, but not impedance abnormalities. To compare the usefulness of arrhythmic events with conventional impedance abnormalities for identifying lead failure in CIED patients followed by RM. CIED patients in 12 hospitals have been followed by the RM center in Okayama University Hospital. All transmitted data have been analyzed and summarized. From April 2009 to March 2016, 1,873 patients have been followed by the RM center. During the mean follow-up period of 775 days, 42 lead failure events (atrial lead 22, right ventricular pacemaker lead 5, implantable cardioverter defibrillator [ICD] lead 15) were detected. The proportion of lead failures detected only by arrhythmic events, which were not detected by conventional impedance abnormalities, was significantly higher than that detected by impedance abnormalities (arrhythmic event 76.2%, 95% CI: 60.5-87.9%; impedance abnormalities 23.8%, 95% CI: 12.1-39.5%). Twenty-seven events (64.7%) were detected without any alert. Of 15 patients with ICD lead failure, none has experienced inappropriate therapy. RM can detect lead failure earlier, before clinical adverse events. However, CIEDs often diagnose lead failure as just arrhythmic events without any warning. Thus, to detect lead failure earlier, careful human analysis of arrhythmic events is useful. © 2017 Wiley Periodicals, Inc.

  8. An analog cell to detect single event transients in voltage references

    Energy Technology Data Exchange (ETDEWEB)

    Franco, F.J., E-mail: fjfranco@fis.ucm.es [Departamento de Física Aplicada III, Facultad de Físicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain); Palomar, C. [Departamento de Física Aplicada III, Facultad de Físicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain); Izquierdo, J.G. [Centro de Láseres Ultrarrápidos, Facultad de Químicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain); Agapito, J.A. [Departamento de Física Aplicada III, Facultad de Físicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain)

    2015-01-11

    A reliable voltage reference is mandatory in mixed-signal systems. However, this family of components can undergo very long single event transients when operating in radiation environments such as space and nuclear facilities due to the impact of heavy ions. The purpose of the present paper is to demonstrate how a simple cell can be used to detect these transients. The cell was implemented with typical COTS components and its behavior was verified by SPICE simulations and in a laser facility. Different applications of the cell are explored as well.

  9. Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Mingjian; Zhang, Jie; Florita, Anthony R.; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-06

    Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

  10. Highly specific detection of genetic modification events using an enzyme-linked probe hybridization chip.

    Science.gov (United States)

    Zhang, M Z; Zhang, X F; Chen, X M; Chen, X; Wu, S; Xu, L L

    2015-08-10

    The enzyme-linked probe hybridization chip utilizes a method based on ligase-hybridizing probe chip technology, with the principle of using thio-primers for protection against enzyme digestion, and using lambda DNA exonuclease to cut multiple PCR products obtained from the sample being tested into single-strand chains for hybridization. The 5'-end amino-labeled probe was fixed onto the aldehyde chip, and hybridized with the single-stranded PCR product, followed by addition of a fluorescent-modified probe that was then enzymatically linked with the adjacent, substrate-bound probe in order to achieve highly specific, parallel, and high-throughput detection. Specificity and sensitivity testing demonstrated that enzyme-linked probe hybridization technology could be applied to the specific detection of eight genetic modification events at the same time, with a sensitivity reaching 0.1% and the achievement of accurate, efficient, and stable results.

  11. A power filter for the detection of burst events based on time-frequency spectrum estimation

    International Nuclear Information System (INIS)

    Guidi, G M; Cuoco, E; Vicere, A

    2004-01-01

    We propose as a statistic for the detection of bursts in a gravitational wave interferometer the 'energy' of the events estimated with a time-dependent calculation of the spectrum. This statistic has an asymptotic Gaussian distribution with known statistical moments, which makes it possible to perform a uniformly most powerful test (McDonough R N and Whalen A D 1995 Detection of Signals in Noise (New York: Academic)) on the energy mean. We estimate the receiver operating characteristic (ROC, from the same book) of this statistic for different levels of the signal-to-noise ratio in the specific case of a simulated noise having the spectral density expected for Virgo, using test signals taken from a library of possible waveforms emitted during the collapse of the core of type II supernovae

  12. Automatic detection of whole night snoring events using non-contact microphone.

    Directory of Open Access Journals (Sweden)

    Eliran Dafna

    Full Text Available OBJECTIVE: Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. DESIGN: Sounds during polysomnography (PSG were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. PATIENTS: Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2, m/f 40/27 referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. MEASUREMENTS AND RESULTS: To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental. A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore and specificity of 98.3% (noise as noise. CONCLUSIONS: Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

  13. Detection of planets in extremely weak central perturbation microlensing events via next-generation ground-based surveys

    International Nuclear Information System (INIS)

    Chung, Sun-Ju; Lee, Chung-Uk; Koo, Jae-Rim

    2014-01-01

    Even though the recently discovered high-magnification event MOA-2010-BLG-311 had complete coverage over its peak, confident planet detection did not happen due to extremely weak central perturbations (EWCPs, fractional deviations of ≲ 2%). For confident detection of planets in EWCP events, it is necessary to have both high cadence monitoring and high photometric accuracy better than those of current follow-up observation systems. The next-generation ground-based observation project, Korea Microlensing Telescope Network (KMTNet), satisfies these conditions. We estimate the probability of occurrence of EWCP events with fractional deviations of ≤2% in high-magnification events and the efficiency of detecting planets in the EWCP events using the KMTNet. From this study, we find that the EWCP events occur with a frequency of >50% in the case of ≲ 100 M E planets with separations of 0.2 AU ≲ d ≲ 20 AU. We find that for main-sequence and sub-giant source stars, ≳ 1 M E planets in EWCP events with deviations ≤2% can be detected with frequency >50% in a certain range that changes with the planet mass. However, it is difficult to detect planets in EWCP events of bright stars like giant stars because it is easy for KMTNet to be saturated around the peak of the events because of its constant exposure time. EWCP events are caused by close, intermediate, and wide planetary systems with low-mass planets and close and wide planetary systems with massive planets. Therefore, we expect that a much greater variety of planetary systems than those already detected, which are mostly intermediate planetary systems, regardless of the planet mass, will be significantly detected in the near future.

  14. Detection of Water Contamination Events Using Fluorescence Spectroscopy and Alternating Trilinear Decomposition Algorithm

    Directory of Open Access Journals (Sweden)

    Jie Yu

    2017-01-01

    Full Text Available The method based on conventional index and UV-vision has been widely applied in the field of water quality abnormality detection. This paper presents a qualitative analysis approach to detect the water contamination events with unknown pollutants. Fluorescence spectra were used as water quality monitoring tools, and the detection method of unknown contaminants in water based on alternating trilinear decomposition (ATLD is proposed to analyze the excitation and emission spectra of the samples. The Delaunay triangulation interpolation method was used to make the pretreatment of three-dimensional fluorescence spectra data, in order to estimate the effect of Rayleigh and Raman scattering; ATLD model was applied to establish the model of normal water sample, and the residual matrix was obtained by subtracting the measured matrix from the model matrix; the residual sum of squares obtained from the residual matrix and threshold was used to make qualitative discrimination of test samples and distinguish drinking water samples and organic pollutant samples. The results of the study indicate that ATLD modeling with three-dimensional fluorescence spectra can provide a tool for detecting unknown organic pollutants in water qualitatively. The method based on fluorescence spectra can be complementary to the method based on conventional index and UV-vision.

  15. An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

    Science.gov (United States)

    Putra, I Putu Edy Suardiyana; Brusey, James; Gaura, Elena; Vesilo, Rein

    2017-12-22

    The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k -nearest neighbor ( k -NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

  16. Endpoint visual detection of three genetically modified rice events by loop-mediated isothermal amplification.

    Science.gov (United States)

    Chen, Xiaoyun; Wang, Xiaofu; Jin, Nuo; Zhou, Yu; Huang, Sainan; Miao, Qingmei; Zhu, Qing; Xu, Junfeng

    2012-11-07

    Genetically modified (GM) rice KMD1, TT51-1, and KF6 are three of the most well known transgenic Bt rice lines in China. A rapid and sensitive molecular assay for risk assessment of GM rice is needed. Polymerase chain reaction (PCR), currently the most common method for detecting genetically modified organisms, requires temperature cycling and relatively complex procedures. Here we developed a visual and rapid loop-mediated isothermal amplification (LAMP) method to amplify three GM rice event-specific junction sequences. Target DNA was amplified and visualized by two indicators (SYBR green or hydroxy naphthol blue [HNB]) within 60 min at an isothermal temperature of 63 °C. Different kinds of plants were selected to ensure the specificity of detection and the results of the non-target samples were negative, indicating that the primer sets for the three GM rice varieties had good levels of specificity. The sensitivity of LAMP, with detection limits at low concentration levels (0.01%−0.005% GM), was 10- to 100-fold greater than that of conventional PCR. Additionally, the LAMP assay coupled with an indicator (SYBR green or HNB) facilitated analysis. These findings revealed that the rapid detection method was suitable as a simple field-based test to determine the status of GM crops.

  17. Development of electrochemical biosensor for detection of pathogenic microorganism in Asian dust events.

    Science.gov (United States)

    Yoo, Min-Sang; Shin, Minguk; Kim, Younghun; Jang, Min; Choi, Yoon-E; Park, Si Jae; Choi, Jonghoon; Lee, Jinyoung; Park, Chulhwan

    2017-05-01

    We developed a single-walled carbon nanotubes (SWCNTs)-based electrochemical biosensor for the detection of Bacillus subtilis, one of the microorganisms observed in Asian dust events, which causes respiratory diseases such as asthma and pneumonia. SWCNTs plays the role of a transducer in biological antigen/antibody reaction for the electrical signal while 1-pyrenebutanoic acid succinimidyl ester (1-PBSE) and ant-B. subtilis were performed as a chemical linker and an acceptor, respectively, for the adhesion of target microorganism in the developed biosensor. The detection range (10 2 -10 10  CFU/mL) and the detection limit (10 2  CFU/mL) of the developed biosensor were identified while the response time was 10 min. The amount of target B. subtilis was the highest in the specificity test of the developed biosensor, compared with the other tested microorganisms (Staphylococcus aureus, Flavobacterium psychrolimnae, and Aquabacterium commune). In addition, target B. subtilis detected by the developed biosensor was observed by scanning electron microscope (SEM) analysis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Endpoint Visual Detection of Three Genetically Modified Rice Events by Loop-Mediated Isothermal Amplification

    Directory of Open Access Journals (Sweden)

    Qing Zhu

    2012-11-01

    Full Text Available Genetically modified (GM rice KMD1, TT51-1, and KF6 are three of the most well known transgenic Bt rice lines in China. A rapid and sensitive molecular assay for risk assessment of GM rice is needed. Polymerase chain reaction (PCR, currently the most common method for detecting genetically modified organisms, requires temperature cycling and relatively complex procedures. Here we developed a visual and rapid loop-mediated isothermal amplification (LAMP method to amplify three GM rice event-specific junction sequences. Target DNA was amplified and visualized by two indicators (SYBR green or hydroxy naphthol blue [HNB] within 60 min at an isothermal temperature of 63 °C. Different kinds of plants were selected to ensure the specificity of detection and the results of the non-target samples were negative, indicating that the primer sets for the three GM rice varieties had good levels of specificity. The sensitivity of LAMP, with detection limits at low concentration levels (0.01%–0.005% GM, was 10- to 100-fold greater than that of conventional PCR. Additionally, the LAMP assay coupled with an indicator (SYBR green or HNB facilitated analysis. These findings revealed that the rapid detection method was suitable as a simple field-based test to determine the status of GM crops.

  19. A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    ZiQi Hao

    2015-01-01

    Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

  20. NEI You Tube Videos: Amblyopia

    Medline Plus

    Full Text Available ... the special health problems and requirements of the blind.” News & Events Events Calendar NEI Press Releases News ... Videos Home Age-Related Macular Degeneration Amblyopia Animations Blindness Cataract Convergence Insufficiency Diabetic Eye Disease Dilated Eye ...

  1. Advanced digital video surveillance for safeguard and physical protection

    International Nuclear Information System (INIS)

    Kumar, R.

    2002-01-01

    Full text: Video surveillance is a very crucial component in safeguard and physical protection. Digital technology has revolutionized the surveillance scenario and brought in various new capabilities like better image quality, faster search and retrieval of video images, less storage space for recording, efficient transmission and storage of video, better protection of recorded video images, and easy remote accesses to live and recorded video etc. The basic safeguard requirement for verifiably uninterrupted surveillance has remained largely unchanged since its inception. However, changes to the inspection paradigm to admit automated review and remote monitoring have dramatically increased the demands on safeguard surveillance system. Today's safeguard systems can incorporate intelligent motion detection with very low rate of false alarm and less archiving volume, embedded image processing capability for object behavior and event based indexing, object recognition, efficient querying and report generation etc. It also demands cryptographically authenticating, encrypted, and highly compressed video data for efficient, secure, tamper indicating and transmission. In physical protection, intelligent on robust video motion detection, real time moving object detection and tracking from stationary and moving camera platform, multi-camera cooperative tracking, activity detection and recognition, human motion analysis etc. is going to play a key rote in perimeter security. Incorporation of front and video imagery exploitation tools like automatic number plate recognition, vehicle identification and classification, vehicle undercarriage inspection, face recognition, iris recognition and other biometric tools, gesture recognition etc. makes personnel and vehicle access control robust and foolproof. Innovative digital image enhancement techniques coupled with novel sensor design makes low cost, omni-directional vision capable, all weather, day night surveillance a reality

  2. Detecting regular sound changes in linguistics as events of concerted evolution.

    Science.gov (United States)

    Hruschka, Daniel J; Branford, Simon; Smith, Eric D; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2015-01-05

    Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  3. Detection of ULF geomagnetic signals associated with seismic events in Central Mexico using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    O. Chavez

    2010-12-01

    Full Text Available The geomagnetic observatory of Juriquilla Mexico, located at longitude –100.45° and latitude 20.70°, and 1946 m a.s.l., has been operational since June 2004 compiling geomagnetic field measurements with a three component fluxgate magnetometer. In this paper, the results of the analysis of these measurements in relation to important seismic activity in the period of 2007 to 2009 are presented. For this purpose, we used superposed epochs of Discrete Wavelet Transform of filtered signals for the three components of the geomagnetic field during relative seismic calm, and it was compared with seismic events of magnitudes greater than Ms > 5.5, which have occurred in Mexico. The analysed epochs consisted of 18 h of observations for a dataset corresponding to 18 different earthquakes (EQs. The time series were processed for a period of 9 h prior to and 9 h after each seismic event. This data processing was compared with the same number of observations during a seismic calm. The proposed methodology proved to be an efficient tool to detect signals associated with seismic activity, especially when the seismic events occur in a distance (D from the observatory to the EQ, such that the ratio D/ρ < 1.8 where ρ is the earthquake radius preparation zone. The methodology presented herein shows important anomalies in the Ultra Low Frequency Range (ULF; 0.005–1 Hz, primarily for 0.25 to 0.5 Hz. Furthermore, the time variance (σ2 increases prior to, during and after the seismic event in relation to the coefficient D1 obtained, principally in the Bx (N-S and By (E-W geomagnetic components. Therefore, this paper proposes and develops a new methodology to extract the abnormal signals of the geomagnetic anomalies related to different stages of the EQs.

  4. Statistical improvement in detection level of gravitational microlensing events from their light curves

    Science.gov (United States)

    Ibrahim, Ichsan; Malasan, Hakim L.; Kunjaya, Chatief; Timur Jaelani, Anton; Puannandra Putri, Gerhana; Djamal, Mitra

    2018-04-01

    In astronomy, the brightness of a source is typically expressed in terms of magnitude. Conventionally, the magnitude is defined by the logarithm of received flux. This relationship is known as the Pogson formula. For received flux with a small signal to noise ratio (S/N), however, the formula gives a large magnitude error. We investigate whether the use of Inverse Hyperbolic Sine function (hereafter referred to as the Asinh magnitude) in the modified formulae could allow for an alternative calculation of magnitudes for small S/N flux, and whether the new approach is better for representing the brightness of that region. We study the possibility of increasing the detection level of gravitational microlensing using 40 selected microlensing light curves from the 2013 and 2014 seasons and by using the Asinh magnitude. Photometric data of the selected events are obtained from the Optical Gravitational Lensing Experiment (OGLE). We found that utilization of the Asinh magnitude makes the events brighter compared to using the logarithmic magnitude, with an average of about 3.42 × 10‑2 magnitude and an average in the difference of error between the logarithmic and the Asinh magnitude of about 2.21 × 10‑2 magnitude. The microlensing events OB140847 and OB140885 are found to have the largest difference values among the selected events. Using a Gaussian fit to find the peak for OB140847 and OB140885, we conclude statistically that the Asinh magnitude gives better mean squared values of the regression and narrower residual histograms than the Pogson magnitude. Based on these results, we also attempt to propose a limit in magnitude value for which use of the Asinh magnitude is optimal with small S/N data.

  5. Building a Global Catalog of Nonvolcanic Tremor Events Using an Automatic Detection Algorithm

    Science.gov (United States)

    Bagley, B. C.; Revenaugh, J.

    2009-12-01

    Nonvolcanic tremor is characterized by a long-period seismic event containing a series of low-frequency earthquakes (LFEs). Tremor has been detected in regions of subduction (e.g. Kao et. al. 2007, 2008; Shelly 2006) and beneath the San Andreas fault near Cholame, California (e.g. Nadeau and Dolenc, 2005). In some cases tremor events seem to have periodicity, and these are often referred to as episodic tremor and slip (ETS). The origin of nonvolcanic tremor has been ascribed to shear slip along plate boundaries and/or high pore-fluid pressure. The apparent periodicity and tectonic setting associated with ETS has led to the suggestion that there may be a link between ETS and megathrust earthquakes. Until recently tremor detection has been a manual process requiring visual inspection of seismic data. In areas that have dense seismic arrays (e.g. Japan) waveform cross correlation techniques have been successfully employed (e.g. Obara, 2002). Kao et al. (2007) developed an algorithm for automatic detection of seismic tremor that can be used in regions without dense arrays. This method has been used to create the Tremor Activity Monitoring System (TAMS), which is used by the Geologic Survey of Canada to monitor northern Cascadia. So far the study of nonvolcanic tremor has been limited to regions of subduction or along major transform faults. It is unknown if tremor events occur in other tectonic settings, or if the current detection schemes will be useful for finding them. We propose to look for tremor events in non-subduction regions. It is possible that if tremor exists in other regions it will have different characteristics and may not trigger the TAMS system or be amenable to other existing detection schemes. We are developing algorithms for searching sparse array data sets for quasi-harmonic energy bursts in hopes of recognizing and cataloging nonvolcanic tremor in an expanded tectonic setting. Statistical comparisons against the TAMS algorithm will be made if

  6. Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

    Science.gov (United States)

    Kreilinger, Alex; Hiebel, Hannah; Müller-Putz, Gernot R

    2016-03-01

    This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.

  7. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Rodrigo Tavares; Martinelli Filho, Martino, E-mail: martino@cardiol.br; Peixoto, Giselle de Lima; Lima, José Jayme Galvão de; Siqueira, Sérgio Freitas de; Costa, Roberto; Gowdak, Luís Henrique Wolff [Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP (Brazil); Paula, Flávio Jota de [Unidade de Transplante Renal - Divisão de Urologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP (Brazil); Kalil Filho, Roberto; Ramires, José Antônio Franchini [Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP (Brazil)

    2015-11-15

    The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used. We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR). A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE. During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041). In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT.

  8. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    Directory of Open Access Journals (Sweden)

    Rodrigo Tavares Silva

    2015-11-01

    Full Text Available AbstractBackground:The recording of arrhythmic events (AE in renal transplant candidates (RTCs undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used.Objective:We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR.Methods:A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE.Results:During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002, and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001 were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041.Conclusions:In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT.

  9. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    Science.gov (United States)

    Silva, Rodrigo Tavares; Martinelli Filho, Martino; Peixoto, Giselle de Lima; de Lima, José Jayme Galvão; de Siqueira, Sérgio Freitas; Costa, Roberto; Gowdak, Luís Henrique Wolff; de Paula, Flávio Jota; Kalil Filho, Roberto; Ramires, José Antônio Franchini

    2015-01-01

    Background The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used. Objective We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR). Methods A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE. Results During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041). Conclusions In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT. PMID:26351983

  10. Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

    Science.gov (United States)

    Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

    2016-10-01

    Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

  11. Multimodal Semantics Extraction from User-Generated Videos

    Directory of Open Access Journals (Sweden)

    Francesco Cricri

    2012-01-01

    Full Text Available User-generated video content has grown tremendously fast to the point of outpacing professional content creation. In this work we develop methods that analyze contextual information of multiple user-generated videos in order to obtain semantic information about public happenings (e.g., sport and live music events being recorded in these videos. One of the key contributions of this work is a joint utilization of different data modalities, including such captured by auxiliary sensors during the video recording performed by each user. In particular, we analyze GPS data, magnetometer data, accelerometer data, video- and audio-content data. We use these data modalities to infer information about the event being recorded, in terms of layout (e.g., stadium, genre, indoor versus outdoor scene, and the main area of interest of the event. Furthermore we propose a method that automatically identifies the optimal set of cameras to be used in a multicamera video production. Finally, we detect the camera users which fall within the field of view of other cameras recording at the same public happening. We show that the proposed multimodal analysis methods perform well on various recordings obtained in real sport events and live music performances.

  12. Content-based video retrieval by example video clip

    Science.gov (United States)

    Dimitrova, Nevenka; Abdel-Mottaleb, Mohamed

    1997-01-01

    This paper presents a novel approach for video retrieval from a large archive of MPEG or Motion JPEG compressed video clips. We introduce a retrieval algorithm that takes a video clip as a query and searches the database for clips with similar contents. Video clips are characterized by a sequence of representative frame signatures, which are constructed from DC coefficients and motion information (`DC+M' signatures). The similarity between two video clips is determined by using their respective signatures. This method facilitates retrieval of clips for the purpose of video editing, broadcast news retrieval, or copyright violation detection.

  13. Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Mingjian; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-07

    Solar power ramp events (SPREs) are those that significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.

  14. Leveraging KVM Events to Detect Cache-Based Side Channel Attacks in a Virtualization Environment

    Directory of Open Access Journals (Sweden)

    Ady Wahyudi Paundu

    2018-01-01

    Full Text Available Cache-based side channel attack (CSCa techniques in virtualization systems are becoming more advanced, while defense methods against them are still perceived as nonpractical. The most recent CSCa variant called Flush + Flush has showed that the current detection methods can be easily bypassed. Within this work, we introduce a novel monitoring approach to detect CSCa operations inside a virtualization environment. We utilize the Kernel Virtual Machine (KVM event data in the kernel and process this data using a machine learning technique to identify any CSCa operation in the guest Virtual Machine (VM. We evaluate our approach using Receiver Operating Characteristic (ROC diagram of multiple attack and benign operation scenarios. Our method successfully separate the CSCa datasets from the non-CSCa datasets, on both trained and nontrained data scenarios. The successful classification also include the Flush + Flush attack scenario. We are also able to explain the classification results by extracting the set of most important features that separate both classes using their Fisher scores and show that our monitoring approach can work to detect CSCa in general. Finally, we evaluate the overhead impact of our CSCa monitoring method and show that it has a negligible computation overhead on the host and the guest VM.

  15. Review of nuclear power reactor coolant system leakage events and leak detection requirements

    International Nuclear Information System (INIS)

    Chokshi, N.C.; Srinivasan, M.; Kupperman, D.S.; Krishnaswamy, P.

    2005-01-01

    In response to the vessel head event at the Davis-Besse reactor, the U.S. Nuclear Regulatory Commission (NRC) formed a Lessons Learned Task Force (LLTF). Four action plans were formulated to respond to the recommendations of the LLTF. The action plans involved efforts on barrier integrity, stress corrosion cracking (SCC), operating experience, and inspection and program management. One part of the action plan on barrier integrity was an assessment to identify potential safety benefits from changes in requirements pertaining to leakage in the reactor coolant system (RCS). In this effort, experiments and models were reviewed to identify correlations between crack size, crack-tip-opening displacement (CTOD), and leak rate in the RCS. Sensitivity studies using the Seepage Quantification of Upsets In Reactor Tubes (SQUIRT) code were carried out to correlate crack parameters, such as crack size, with leak rate for various types of crack configurations in RCS components. A database that identifies the leakage source, leakage rate, and resulting actions from RCS leaks discovered in U.S. light water reactors was developed. Humidity monitoring systems for detecting leakage and acoustic emission crack monitoring systems for the detection of crack initiation and growth before a leak occurs were also considered. New approaches to the detection of a leak in the reactor head region by monitoring boric-acid aerosols were also considered. (authors)

  16. Ultra-Low Power Sensor System for Disaster Event Detection in Metro Tunnel Systems

    Directory of Open Access Journals (Sweden)

    Jonah VINCKE

    2017-05-01

    Full Text Available In this extended paper, the concept for an ultra-low power wireless sensor network (WSN for underground tunnel systems is presented highlighting the chosen sensors. Its objectives are the detection of emergency events either from natural disasters, such as flooding or fire, or from terrorist attacks using explosives. Earlier works have demonstrated that the power consumption for the communication can be reduced such that the data acquisition (i.e. sensor sub-system becomes the most significant energy consumer. By using ultra-low power components for the smoke detector, a hydrostatic pressure sensor for water ingress detection and a passive acoustic emission sensor for explosion detection, all considered threats are covered while the energy consumption can be kept very low in relation to the data acquisition. In addition to 1 the sensor system is integrated into a sensor board. The total average power consumption for operating the sensor sub-system is measured to be 35.9 µW for lower and 7.8 µW for upper nodes.

  17. Dashboard Videos

    Science.gov (United States)

    Gleue, Alan D.; Depcik, Chris; Peltier, Ted

    2012-01-01

    Last school year, I had a web link emailed to me entitled "A Dashboard Physics Lesson." The link, created and posted by Dale Basier on his "Lab Out Loud" blog, illustrates video of a car's speedometer synchronized with video of the road. These two separate video streams are compiled into one video that students can watch and analyze. After seeing…

  18. Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study.

    Science.gov (United States)

    Cole, Casey A; Anshari, Dien; Lambert, Victoria; Thrasher, James F; Valafar, Homayoun

    2017-12-13

    Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self

  19. First events from the CNGS neutrino beam detected in the OPERA experiment

    CERN Document Server

    Acquafredda, R.; Ambrosio, M.; Anokhina, A.; Aoki, S.; Ariga, A.; Arrabito, L.; Autiero, D.; Badertscher, A.; Bergnoli, A.; Bersani Greggio, F.; Besnier, M.; Beyer, M.; Bondil-Blin, S.; Borer, K.; Boucrot, J.; Boyarkin, V.; Bozza, C.; Brugnera, R.; Buontempo, S.; Caffari, Y.; Campagne, Jean-Eric; Carlus, B.; Carrara, E.; Cazes, A.; Chaussard, L.; Chernyavsky, M.; Chiarella, V.; Chon-Sen, N.; Chukanov, A.; Ciesielski, R.; Consiglio, L.; Cozzi, M.; Dal Corso, F.; D'Ambrosio, N.; Damet, J.; De Lellis, G.; Declais, Y.; Descombes, T.; De Serio, M.; Di Capua, F.; Di Ferdinando, D.; Di Giovanni, A.; Di Marco, N.; Di Troia, C.; Dmitrievski, S.; Dracos, M.; Duchesneau, D.; Dulach, B.; Dusini, S.; Ebert, J.; Enikeev, R.; Ereditato, A.; Esposito, L.S.; Fanin, C.; Favier, J.; Felici, G.; Ferber, T.; Fournier, L.; Franceschi, A.; Frekers, D.; Fukuda, T.; Fukushima, C.; Galkin, V.I.; Galkin, V.A.; Gallet, R.; Garfagnini, A.; Gaudiot, G.; Giacomelli, G.; Giarmana, O.; Giorgini, M.; Girard, L.; Girerd, C.; Goellnitz, C.; Goldberg, J.; Gornoushkin, Y.; Grella, G.; Grianti, F.; Guerin, C.; Guler, M.; Gustavino, C.; Hagner, C.; Hamane, T.; Hara, T.; Hauger, M.; Hess, M.; Hoshino, K.; Ieva, M.; Incurvati, M.; Jakovcic, K.; Janicsko Csathy, J.; Janutta, B.; Jollet, C.; Juget, F.; Kazuyama, M.; Kim, S.H.; Kimura, M.; Knuesel, J.; Kodama, K.; Kolev, D.; Komatsu, M.; Kose, U.; Krasnoperov, A.; Kreslo, I.; Krumstein, Z.; Laktineh, I.; de La Taille, C.; Le Flour, T.; Lieunard, S.; Ljubicic, A.; Longhin, A.; Malgin, A.; Manai, K.; Mandrioli, G.; Mantello, U.; Marotta, A.; Marteau, J.; Martin-Chassard, G.; Matveev, V.; Messina, M.; Meyer, L.; Micanovic, S.; Migliozzi, P.; Miyamoto, S.; Monacelli, Piero; Monteiro, I.; Morishima, K.; Moser, U.; Muciaccia, M.T.; Mugnier, P.; Naganawa, N.; Nakamura, M.; Nakano, T.; Napolitano, T.; Natsume, M.; Niwa, K.; Nonoyama, Y.; Nozdrin, A.; Ogawa, S.; Olchevski, A.; Orlandi, D.; Ossetski, D.; Paoloni, A.; Park, B.D.; Park, I.G.; Pastore, A.; Patrizii, L.; Pellegrino, L.; Pessard, H.; Pilipenko, V.; Pistillo, C.; Polukhina, N.; Pozzato, M.; Pretzl, K.; Publichenko, P.; Raux, L.; Repellin, J.P.; Roganova, T.; Romano, G.; Rosa, G.; Rubbia, A.; Ryasny, V.; Ryazhskaya, O.; Ryzhikov, D.; Sadovski, A.; Sanelli, C.; Sato, O.; Sato, Y.; Saveliev, V.; Savvinov, N.; Sazhina, G.; Schembri, A.; Schmidt Parzefall, W.; Schroeder, H.; Schutz, H.U.; Scotto Lavina, L.; Sewing, J.; Shibuya, H.; Simone, S.; Sioli, M.; Sirignano, C.; Sirri, G.; Song, J.S.; Spaeti, R.; Spinetti, M.; Stanco, L.; Starkov, N.; Stipcevic, M.; Strolin, Paolo Emilio; Sugonyaev, V.; Takahashi, S.; Tereschenko, V.; Terranova, F.; Tezuka, I.; Tioukov, V.; Tikhomirov, I.; Tolun, P.; Toshito, T.; Tsarev, V.; Tsenov, R.; Ugolino, U.; Ushida, N.; Van Beek, G.; Verguilov, V.; Vilain, P.; Votano, L.; Vuilleumier, J.L.; Waelchli, T.; Waldi, R.; Weber, M.; Wilquet, G.; Wonsak, B.; Wurth, R.; Wurtz, J.; Yakushev, V.; Yoon, C.S.; Zaitsev, Y.; Zamboni, I.; Zimmerman, R.

    2006-01-01

    The OPERA neutrino detector at the underground Gran Sasso Laboratory (LNGS) was designed to perform the first detection of neutrino oscillations in appearance mode, through the study of nu_mu to nu_tau oscillations. The apparatus consists of a lead/emulsion-film target complemented by electronic detectors. It is placed in the high-energy, long-baseline CERN to LNGS beam (CNGS) 730 km away from the neutrino source. In August 2006 a first run with CNGS neutrinos was successfully conducted. A first sample of neutrino events was collected, statistically consistent with the integrated beam intensity. After a brief description of the beam and of the various sub-detectors, we report on the achievement of this milestone, presenting the first data and some analysis results.

  20. The Event Detection and the Apparent Velocity Estimation Based on Computer Vision

    Science.gov (United States)

    Shimojo, M.

    2012-08-01

    The high spatial and time resolution data obtained by the telescopes aboard Hinode revealed the new interesting dynamics in solar atmosphere. In order to detect such events and estimate the velocity of dynamics automatically, we examined the estimation methods of the optical flow based on the OpenCV that is the computer vision library. We applied the methods to the prominence eruption observed by NoRH, and the polar X-ray jet observed by XRT. As a result, it is clear that the methods work well for solar images if the images are optimized for the methods. It indicates that the optical flow estimation methods in the OpenCV library are very useful to analyze the solar phenomena.

  1. Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor.

    Science.gov (United States)

    Al-Naji, Ali; Chahl, Javaan

    2018-03-20

    Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI). This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea) in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective.

  2. Event detection and localization for small mobile robots using reservoir computing.

    Science.gov (United States)

    Antonelo, E A; Schrauwen, B; Stroobandt, D

    2008-08-01

    Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

  3. Decision support methods for the detection of adverse events in post-marketing data.

    Science.gov (United States)

    Hauben, M; Bate, A

    2009-04-01

    Spontaneous reporting is a crucial component of post-marketing drug safety surveillance despite its significant limitations. The size and complexity of some spontaneous reporting system databases represent a challenge for drug safety professionals who traditionally have relied heavily on the scientific and clinical acumen of the prepared mind. Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous reporting system databases. After an overview of pharmacovigilance and spontaneous reporting systems, we discuss the theory and application of contemporary computer algorithms in regular use, those under development, and the practical considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.

  4. Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor

    Directory of Open Access Journals (Sweden)

    Ali Al-Naji

    2018-03-01

    Full Text Available Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI. This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective.

  5. Video microblogging

    DEFF Research Database (Denmark)

    Bornoe, Nis; Barkhuus, Louise

    2010-01-01

    Microblogging is a recently popular phenomenon and with the increasing trend for video cameras to be built into mobile phones, a new type of microblogging has entered the arena of electronic communication: video microblogging. In this study we examine video microblogging, which is the broadcasting...... of short videos. A series of semi-structured interviews offers an understanding of why and how video microblogging is used and what the users post and broadcast....

  6. Standard and Nonstandard Neutrino-Nucleus Reactions Cross Sections and Event Rates to Neutrino Detection Experiments

    Directory of Open Access Journals (Sweden)

    D. K. Papoulias

    2015-01-01

    Full Text Available In this work, we explore ν-nucleus processes from a nuclear theory point of view and obtain results with high confidence level based on accurate nuclear structure cross sections calculations. Besides cross sections, the present study includes simulated signals expected to be recorded by nuclear detectors and differential event rates as well as total number of events predicted to be measured. Our original cross sections calculations are focused on measurable rates for the standard model process, but we also perform calculations for various channels of the nonstandard neutrino-nucleus reactions and come out with promising results within the current upper limits of the corresponding exotic parameters. We concentrate on the possibility of detecting (i supernova neutrinos by using massive detectors like those of the GERDA and SuperCDMS dark matter experiments and (ii laboratory neutrinos produced near the spallation neutron source facilities (at Oak Ridge National Lab by the COHERENT experiment. Our nuclear calculations take advantage of the relevant experimental sensitivity and employ the severe bounds extracted for the exotic parameters entering the Lagrangians of various particle physics models and specifically those resulting from the charged lepton flavour violating μ-→e- experiments (Mu2e and COMET experiments.

  7. Energy efficient data representation and aggregation with event region detection in wireless sensor networks

    Science.gov (United States)

    Banerjee, Torsha

    Detection (PERD) for WSNs. When a single event occurs, a child of the tree sends a Flagged Polynomial (FP) to its parent, if the readings approximated by it falls outside the data range defining the existing phenomenon. After the aggregation process is over, the root having the two polynomials, P and FP can be queried for FP (approximating the new event region) instead of flooding the whole network. For multiple such events, instead of computing a polynomial corresponding to each new event, areas with same data range are combined by the corresponding tree nodes and the aggregated coefficients are passed on. Results reveal that a new event can be detected by PERD while error in detection remains constant and is less than a threshold of 10%. As the node density increases, accuracy and delay for event detection are found to remain almost constant, making PERD highly scalable. Whenever an event occurs in a WSN, data is generated by closeby sensors and relaying the data to the base station (BS) make sensors closer to the BS run out of energy at a much faster rate than sensors in other parts of the network. This gives rise to an unequal distribution of residual energy in the network and makes those sensors with lower remaining energy level die at much faster rate than others. We propose a scheme for enhancing network Lifetime using mobile cluster heads (CH) in a WSN. To maintain remaining energy more evenly, some energy-rich nodes are designated as CHs which move in a controlled manner towards sensors rich in energy and data. This eliminates multihop transmission required by the static sensors and thus increases the overall lifetime of the WSN. We combine the idea of clustering and mobile CH to first form clusters of static sensor nodes. A collaborative strategy among the CHs further increases the lifetime of the network. Time taken for transmitting data to the BS is reduced further by making the CHs follow a connectivity strategy that always maintain a connected path to the BS

  8. Commercially available video motion detectors

    International Nuclear Information System (INIS)

    1979-01-01

    A market survey of commercially available video motion detection systems was conducted by the Intrusion Detection Systems Technology Division of Sandia Laboratories. The information obtained from this survey is summarized in this report. The cutoff date for this information is May 1978. A list of commercially available video motion detection systems is appended

  9. Convolutional neural networks for event-related potential detection: impact of the architecture.

    Science.gov (United States)

    Cecotti, H

    2017-07-01

    The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

  10. Content-based analysis and indexing of sports video

    Science.gov (United States)

    Luo, Ming; Bai, Xuesheng; Xu, Guang-you

    2001-12-01

    An explosion of on-line image and video data in digital form is already well underway. With the exponential rise in interactive information exploration and dissemination through the World-Wide Web, the major inhibitors of rapid access to on-line video data are the management of capture and storage, and content-based intelligent search and indexing techniques. This paper proposes an approach for content-based analysis and event-based indexing of sports video. It includes a novel method to organize shots - classifying shots as close shots and far shots, an original idea of blur extent-based event detection, and an innovative local mutation-based algorithm for caption detection and retrieval. Results on extensive real TV programs demonstrate the applicability of our approach.

  11. Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features

    Directory of Open Access Journals (Sweden)

    Hyoung‐Gook Kim

    2017-12-01

    Full Text Available Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

  12. Detecting Forest Disturbance Events from MODIS and Landsat Time Series for the Conterminous United States

    Science.gov (United States)

    Zhang, G.; Ganguly, S.; Saatchi, S. S.; Hagen, S. C.; Harris, N.; Yu, Y.; Nemani, R. R.

    2013-12-01

    Spatial and temporal patterns of forest disturbance and regrowth processes are key for understanding aboveground terrestrial vegetation biomass and carbon stocks at regional-to-continental scales. The NASA Carbon Monitoring System (CMS) program seeks key input datasets, especially information related to impacts due to natural/man-made disturbances in forested landscapes of Conterminous U.S. (CONUS), that would reduce uncertainties in current carbon stock estimation and emission models. This study provides a end-to-end forest disturbance detection framework based on pixel time series analysis from MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat surface spectral reflectance data. We applied the BFAST (Breaks for Additive Seasonal and Trend) algorithm to the Normalized Difference Vegetation Index (NDVI) data for the time period from 2000 to 2011. A harmonic seasonal model was implemented in BFAST to decompose the time series to seasonal and interannual trend components in order to detect abrupt changes in magnitude and direction of these components. To apply the BFAST for whole CONUS, we built a parallel computing setup for processing massive time-series data using the high performance computing facility of the NASA Earth Exchange (NEX). In the implementation process, we extracted the dominant deforestation events from the magnitude of abrupt changes in both seasonal and interannual components, and estimated dates for corresponding deforestation events. We estimated the recovery rate for deforested regions through regression models developed between NDVI values and time since disturbance for all pixels. A similar implementation of the BFAST algorithm was performed over selected Landsat scenes (all Landsat cloud free data was used to generate NDVI from atmospherically corrected spectral reflectances) to demonstrate the spatial coherence in retrieval layers between MODIS and Landsat. In future, the application of this largely parallel disturbance

  13. Algorithms for the automatic identification of MARFEs and UFOs in JET database of visible camera videos

    International Nuclear Information System (INIS)

    Murari, A.; Camplani, M.; Cannas, B.; Usai, P.; Mazon, D.; Delaunay, F.

    2010-01-01

    MARFE instabilities and UFOs leave clear signatures in JET fast visible camera videos. Given the potential harmful consequences of these events, particularly as triggers of disruptions, it would be important to have the means of detecting them automatically. In this paper, the results of various algorithms to identify automatically the MARFEs and UFOs in JET visible videos are reported. The objective is to retrieve the videos, which have captured these events, exploring the whole JET database of images, as a preliminary step to the development of real-time identifiers in the future. For the detection of MARFEs, a complete identifier has been finalized, using morphological operators and Hu moments. The final algorithm manages to identify the videos with MARFEs with a success rate exceeding 80%. Due to the lack of a complete statistics of examples, the UFO identifier is less developed, but a preliminary code can detect UFOs quite reliably. (authors)

  14. The power to detect recent fragmentation events using genetic differentiation methods.

    Directory of Open Access Journals (Sweden)

    Michael W Lloyd

    Full Text Available Habitat loss and fragmentation are imminent threats to biological diversity worldwide and thus are fundamental issues in conservation biology. Increased isolation alone has been implicated as a driver of negative impacts in populations associated with fragmented landscapes. Genetic monitoring and the use of measures of genetic divergence have been proposed as means to detect changes in landscape connectivity. Our goal was to evaluate the sensitivity of Wright's F st, Hedrick' G'st , Sherwin's MI, and Jost's D to recent fragmentation events across a range of population sizes and sampling regimes. We constructed an individual-based model, which used a factorial design to compare effects of varying population size, presence or absence of overlapping generations, and presence or absence of population sub-structuring. Increases in population size, overlapping generations, and population sub-structuring each reduced F st, G'st , MI, and D. The signal of fragmentation was detected within two generations for all metrics. However, the magnitude of the change in each was small in all cases, and when N e was >100 individuals it was extremely small. Multi-generational sampling and population estimates are required to differentiate the signal of background divergence from changes in Fst , G'st , MI, and D associated with fragmentation. Finally, the window during which rapid change in Fst , G'st , MI, and D between generations occurs can be small, and if missed would lead to inconclusive results. For these reasons, use of F st, G'st , MI, or D for detecting and monitoring changes in connectivity is likely to prove difficult in real-world scenarios. We advocate use of genetic monitoring only in conjunction with estimates of actual movement among patches such that one could compare current movement with the genetic signature of past movement to determine there has been a change.

  15. Detection of adverse events in general surgery using the " Trigger Tool" methodology.

    Science.gov (United States)

    Pérez Zapata, Ana Isabel; Gutiérrez Samaniego, María; Rodríguez Cuéllar, Elías; Andrés Esteban, Eva María; Gómez de la Cámara, Agustín; Ruiz López, Pedro

    2015-02-01

    Surgery is one of the high-risk areas for the occurrence of adverse events (AE). The purpose of this study is to know the percentage of hospitalisation-related AE that are detected by the «Global Trigger Tool» methodology in surgical patients, their characteristics and the tool validity. Retrospective, observational study on patients admitted to a general surgery department, who underwent a surgical operation in a third level hospital during the year 2012. The identification of AE was carried out by patient record review using an adaptation of «Global Trigger Tool» methodology. Once an AE was identified, a harm category was assigned, including the grade in which the AE could have been avoided and its relation with the surgical procedure. The prevalence of AE was 36,8%. There were 0,5 AE per patient. 56,2% were deemed preventable. 69,3% were directly related to the surgical procedure. The tool had a sensitivity of 86% and a specificity of 93,6%. The positive predictive value was 89% and the negative predictive value 92%. Prevalence of AE is greater than the estimate of other studies. In most cases the AE detected were related to the surgical procedure and more than half were also preventable. The adapted «Global Trigger Tool» methodology has demonstrated to be highly effective and efficient for detecting AE in surgical patients, identifying all the serious AE with few false negative results. Copyright © 2014 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

  16. Video demystified

    CERN Document Server

    Jack, Keith

    2004-01-01

    This international bestseller and essential reference is the "bible" for digital video engineers and programmers worldwide. This is by far the most informative analog and digital video reference available, includes the hottest new trends and cutting-edge developments in the field. Video Demystified, Fourth Edition is a "one stop" reference guide for the various digital video technologies. The fourth edition is completely updated with all new chapters on MPEG-4, H.264, SDTV/HDTV, ATSC/DVB, and Streaming Video (Video over DSL, Ethernet, etc.), as well as discussions of the latest standards throughout. The accompanying CD-ROM is updated to include a unique set of video test files in the newest formats. *This essential reference is the "bible" for digital video engineers and programmers worldwide *Contains all new chapters on MPEG-4, H.264, SDTV/HDTV, ATSC/DVB, and Streaming Video *Completely revised with all the latest and most up-to-date industry standards.

  17. Video pedagogy

    OpenAIRE

    Länsitie, Janne; Stevenson, Blair; Männistö, Riku; Karjalainen, Tommi; Karjalainen, Asko

    2016-01-01

    The short film is an introduction to the concept of video pedagogy. The five categories of video pedagogy further elaborate how videos can be used as a part of instruction and learning process. Most pedagogical videos represent more than one category. A video itself doesn’t necessarily define the category – the ways in which the video is used as a part of pedagogical script are more defining factors. What five categories did you find? Did you agree with the categories, or are more...

  18. FOREWORD: 3rd Symposium on Large TPCs for Low Energy Event Detection

    Science.gov (United States)

    Irastorza, Igor G.; Colas, Paul; Gorodetzky, Phillippe

    2007-05-01

    The Third International Symposium on large TPCs for low-energy rare-event detection was held at Carré des sciences, Poincaré auditorium, 25 rue de la Montagne Ste Geneviève in Paris on 11 12 December 2006. This prestigious location belonging to the Ministry of Research is hosted in the former Ecole Polytechnique. The meeting, held in Paris every two years, gathers a significant community of physicists involved in rare event detection. Its purpose is an extensive discussion of present and future projects using large TPCs for low energy, low background detection of rare events (low-energy neutrinos, dark matter, solar axions). The use of a new generation of Micro-Pattern Gaseous Detectors (MPGD) appears to be a promising way to reach this goal. The program this year was enriched by a new session devoted to the detection challenge of polarized gamma rays, relevant novel experimental techniques and the impact on particle physics, astrophysics and astronomy. A very particular feature of this conference is the large variety of talks ranging from purely theoretical to purely experimental subjects including novel technological aspects. This allows discussion and exchange of useful information and new ideas that are emerging to address particle physics experimental challenges. The scientific highlights at the Symposium came on many fronts: Status of low-energy neutrino physics and double-beta decay New ideas on double-beta decay experiments Gamma ray polarization measurement combining high-precision TPCs with MPGD read-out Dark Matter challenges in both axion and WIMP search with new emerging ideas for detection improvements Progress in gaseous and liquid TPCs for rare event detection Georges Charpak opened the meeting with a talk on gaseous detectors for applications in the bio-medical field. He also underlined the importance of new MPGD detectors for both physics and applications. There were about 100 registered participants at the symposium. The successful

  19. Bioluminescence Detection of Cells Having Stabilized p53 in Response to a Genotoxic Event

    Directory of Open Access Journals (Sweden)

    Alnawaz Rehemtulla

    2004-01-01

    Full Text Available Inactivation of p53 is one of the most frequent molecular events in neoplastic transformation. Approximately 60% of all human tumors have mutations in both p53 alleles. Wild-type p53 activity is regulated in large part by the proteosome-dependent degradation of p53, resulting in a short p53 half-life in unstressed and untransformed cells. Activation of p53 by a variety of stimuli, including DNA damage induced by genotoxic drugs or radiation, is accomplished by stabilization of wild-type p53. The stabilized and active p53 can result in either cell-cycle arrest or apoptosis. Surprisingly, the majority of tumor-associated, inactivating p53 mutations also result in p53 accumulation. Thus, constitutive elevation of p53 levels in cells is a reliable measure of p53 inactivation, whereas transiently increased p53 levels reflect a recent genotoxic stress. In order to facilitate noninvasive imaging of p53 accumulation, we here describe the construction of a p53-luciferase fusion protein. Induction of DNA damage in cells expressing the fusion protein resulted in a time-dependent accumulation of the fusion that was noninvasively detected using bioluminescence imaging and validated by Western blot analysis. The p53-Luc protein retains p53 function because its expression in HCT116 cells lacking functional p53 resulted in activation of p21 expression as well as induction of apoptosis in response to a DNA damaging event. Employed in a transgenic animal model, the proposed p53-reporter fusion protein will be useful for studying p53 activation in response to exposure to DNA-damaging carcinogenic agents. It could also be used to study p53 stabilization as a result of inactivating p53 mutations. Such studies will further our understanding of p53's role as the “guardian of the genome” and its function in tumorigenesis.

  20. Detection of microsleep events in a car driving simulation study using electrocardiographic features

    Directory of Open Access Journals (Sweden)

    Lenis Gustavo

    2016-09-01

    Full Text Available Microsleep events (MSE are short intrusions of sleep under the demand of sustained attention. They can impose a major threat to safety while driving a car and are considered one of the most significant causes of traffic accidents. Driver’s fatigue and MSE account for up to 20% of all car crashes in Europe and at least 100,000 accidents in the US every year. Unfortunately, there is not a standardized test developed to quantify the degree of vigilance of a driver. To account for this problem, different approaches based on biosignal analysis have been studied in the past. In this paper, we investigate an electrocardiographic-based detection of MSE using morphological and rhythmical features. 14 records from a car driving simulation study with a high incidence of MSE were analyzed and the behavior of the ECG features before and after an MSE in relation to reference baseline values (without drowsiness were investigated. The results show that MSE cannot be detected (or predicted using only the ECG. However, in the presence of MSE, the rhythmical and morphological features were observed to be significantly different than the ones calculated for the reference signal without sleepiness. In particular, when MSE were present, the heart rate diminished while the heart rate variability increased. Time distances between P wave and R peak, and R peak and T wave and their dispersion increased also. This demonstrates a noticeable change of the autonomous regulation of the heart. In future, the ECG parameter could be used as a surrogate measure of fatigue.

  1. Detecting Micro-seismicity and Long-duration Tremor-like Events from the Oklahoma Wavefield Experiment

    Science.gov (United States)

    Li, C.; Li, Z.; Peng, Z.; Zhang, C.; Nakata, N.

    2017-12-01

    Oklahoma has experienced abrupt increase of induced seismicity in the last decade. An important way to fully understand seismic activities in Oklahoma is to obtain more complete earthquake catalogs and detect different types of seismic events. The IRIS Community Wavefield Demonstration Experiment was deployed near Enid, Oklahoma in Summer of 2016. The dataset from this ultra-dense array provides an excellent opportunity for detecting microseismicity in that region with wavefield approaches. Here we examine continuous waveforms recorded by 3 seismic lines using local coherence for ultra-dense arrays (Li et al., 2017), which is a measure of cross-correlation of waveform at each station with its nearby stations. So far we have detected more than 5,000 events from 06/22/2016 to 07/20/2016, and majority of them are not listed on the regional catalog of Oklahoma or global catalogs, indicating that they are local events. We also identify 15-20 long-period long-duration events, some of them lasting for more than 500 s. Such events have been found at major plate-boundary faults (also known as deep tectonic tremor), as well as during hydraulic fracturing, slow-moving landslides and glaciers. Our next step is to locate these possible tremor-like events with their relative arrival times across the array and compare their occurrence times with solid-earth tides and injection histories to better understand their driving mechanisms.

  2. On the feasibility of using satellite gravity observations for detecting large-scale solid mass transfer events

    Science.gov (United States)

    Peidou, Athina C.; Fotopoulos, Georgia; Pagiatakis, Spiros

    2017-10-01

    The main focus of this paper is to assess the feasibility of utilizing dedicated satellite gravity missions in order to detect large-scale solid mass transfer events (e.g. landslides). Specifically, a sensitivity analysis of Gravity Recovery and Climate Experiment (GRACE) gravity field solutions in conjunction with simulated case studies is employed to predict gravity changes due to past subaerial and submarine mass transfer events, namely the Agulhas slump in southeastern Africa and the Heart Mountain Landslide in northwestern Wyoming. The detectability of these events is evaluated by taking into account the expected noise level in the GRACE gravity field solutions and simulating their impact on the gravity field through forward modelling of the mass transfer. The spectral content of the estimated gravity changes induced by a simulated large-scale landslide event is estimated for the known spatial resolution of the GRACE observations using wavelet multiresolution analysis. The results indicate that both the Agulhas slump and the Heart Mountain Landslide could have been detected by GRACE, resulting in {\\vert }0.4{\\vert } and {\\vert }0.18{\\vert } mGal change on GRACE solutions, respectively. The suggested methodology is further extended to the case studies of the submarine landslide in Tohoku, Japan, and the Grand Banks landslide in Newfoundland, Canada. The detectability of these events using GRACE solutions is assessed through their impact on the gravity field.

  3. Snake scales, partial exposure, and the Snake Detection Theory: A human event-related potentials study

    Science.gov (United States)

    Van Strien, Jan W.; Isbell, Lynne A.

    2017-01-01

    Studies of event-related potentials in humans have established larger early posterior negativity (EPN) in response to pictures depicting snakes than to pictures depicting other creatures. Ethological research has recently shown that macaques and wild vervet monkeys respond strongly to partially exposed snake models and scale patterns on the snake skin. Here, we examined whether snake skin patterns and partially exposed snakes elicit a larger EPN in humans. In Task 1, we employed pictures with close-ups of snake skins, lizard skins, and bird plumage. In task 2, we employed pictures of partially exposed snakes, lizards, and birds. Participants watched a random rapid serial visual presentation of these pictures. The EPN was scored as the mean activity (225–300 ms after picture onset) at occipital and parieto-occipital electrodes. Consistent with previous studies, and with the Snake Detection Theory, the EPN was significantly larger for snake skin pictures than for lizard skin and bird plumage pictures, and for lizard skin pictures than for bird plumage pictures. Likewise, the EPN was larger for partially exposed snakes than for partially exposed lizards and birds. The results suggest that the EPN snake effect is partly driven by snake skin scale patterns which are otherwise rare in nature. PMID:28387376

  4. First Satellite-detected Perturbations of Outgoing Longwave Radiation Associated with Blowing Snow Events over Antarctica

    Science.gov (United States)

    Yang, Yuekui; Palm, Stephen P.; Marshak, Alexander; Wu, Dong L.; Yu, Hongbin; Fu, Qiang

    2014-01-01

    We present the first satellite-detected perturbations of the outgoing longwave radiation (OLR) associated with blowing snow events over the Antarctic ice sheet using data from Cloud-Aerosol Lidar with Orthogonal Polarization and Clouds and the Earth's Radiant Energy System. Significant cloud-free OLR differences are observed between the clear and blowing snow sky, with the sign andmagnitude depending on season and time of the day. During nighttime, OLRs are usually larger when blowing snow is present; the average difference in OLRs between without and with blowing snow over the East Antarctic Ice Sheet is about 5.2 W/m2 for the winter months of 2009. During daytime, in contrast, the OLR perturbation is usually smaller or even has the opposite sign. The observed seasonal variations and day-night differences in the OLR perturbation are consistent with theoretical calculations of the influence of blowing snow on OLR. Detailed atmospheric profiles are needed to quantify the radiative effect of blowing snow from the satellite observations.

  5. Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

    Directory of Open Access Journals (Sweden)

    Elmer P. Dadios

    2009-01-01

    Full Text Available This paper presents a new algorithm for real time event detection using Finite State Machines with multiple Fuzzy Logic Probability Evaluators (FLPEs. A machine referee for a robot soccer game is developed and is used as the platform to test the proposed algorithm. A novel technique to detect collisions and other events in microrobot soccer game under inaccurate and insufficient information is presented. The robots' collision is used to determine goalkeeper charging and goal score events which are crucial for the machine referee's decisions. The Main State Machine (MSM handles the schedule of event activation. The FLPE calculates the probabilities of the true occurrence of the events. Final decisions about the occurrences of events are evaluated and compared through threshold crisp probability values. The outputs of FLPEs can be combined to calculate the probability of an event composed of subevents. Using multiple fuzzy logic system, the FLPE utilizes minimal number of rules and can be tuned individually. Experimental results show the accuracy and robustness of the proposed algorithm.

  6. Motion video analysis using planar parallax

    Science.gov (United States)

    Sawhney, Harpreet S.

    1994-04-01

    Motion and structure analysis in video sequences can lead to efficient descriptions of objects and their motions. Interesting events in videos can be detected using such an analysis--for instance independent object motion when the camera itself is moving, figure-ground segregation based on the saliency of a structure compared to its surroundings. In this paper we present a method for 3D motion and structure analysis that uses a planar surface in the environment as a reference coordinate system to describe a video sequence. The motion in the video sequence is described as the motion of the reference plane, and the parallax motion of all the non-planar components of the scene. It is shown how this method simplifies the otherwise hard general 3D motion analysis problem. In addition, a natural coordinate system in the environment is used to describe the scene which can simplify motion based segmentation. This work is a part of an ongoing effort in our group towards video annotation and analysis for indexing and retrieval. Results from a demonstration system being developed are presented.

  7. A new method to detect event-related potentials based on Pearson's correlation.

    Science.gov (United States)

    Giroldini, William; Pederzoli, Luciano; Bilucaglia, Marco; Melloni, Simone; Tressoldi, Patrizio

    2016-12-01

    Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N , where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP's waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson's correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase-in consonance with the stimuli-in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP's inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well

  8. A secure distributed logistic regression protocol for the detection of rare adverse drug events.

    Science.gov (United States)

    El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

    2013-05-01

    There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through

  9. Enriched Encoding: Reward Motivation Organizes Cortical Networks for Hippocampal Detection of Unexpected Events

    OpenAIRE

    Murty, Vishnu P.; Adcock, R. Alison

    2013-01-01

    Learning how to obtain rewards requires learning about their contexts and likely causes. How do long-term memory mechanisms balance the need to represent potential determinants of reward outcomes with the computational burden of an over-inclusive memory? One solution would be to enhance memory for salient events that occur during reward anticipation, because all such events are potential determinants of reward. We tested whether reward motivation enhances encoding of salient events like expec...

  10. The necessity of recognizing all events in X-ray detection.

    Science.gov (United States)

    Papp, T; Maxwell, J A; Papp, A T

    2010-01-01

    In our work in studying properties of inner shell ionization, we are troubled that the experimental data used to determine the basic parameters of X-ray physics have a large and unexplainable scatter. As we looked into the problems we found that many of them contradict simple logic, elemental arithmetic, even parity and angular momentum conservation laws. We have identified that the main source of the problems, other than the human factor, is rooted in the signal processing electronics. To overcome these problems we have developed a fully digital signal processor, which not only has excellent resolution and line shape, but also allows proper accounting of all events. This is achieved by processing all events and separating them into two or more spectra (maximum 16), where the first spectrum is the accepted or good spectrum and the second spectrum is the spectrum of all rejected events. The availability of all the events allows one to see the other part of the spectrum. To our surprise the total information explains many of the shortcomings and contradictions of the X-ray database. The data processing methodology cannot be established on the partial and fractional information offered by other approaches. Comparing Monte Carlo detector modeling results with the partial spectra is ambiguous. It suggests that the metrology of calibration by radioactive sources as well as other X-ray measurements could be improved by the availability of the proper accounting of all events. It is not enough to know that an event was rejected and increment the input counter, it is necessary to know, what was rejected and why it happened, whether it was a noise or a disturbed event, a retarded event or a true event, or any pile up combination of these events. Such information is supplied by our processor reporting the events rejected by each discriminator in separate spectra. Several industrial applications of this quality assurance capable signal processor are presented. Copyright 2009

  11. Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

    International Nuclear Information System (INIS)

    Hou, Dibo; He, Huimei; Huang, Pingjie; Zhang, Guangxin; Loaiciga, Hugo

    2013-01-01

    This study presents a method for detecting contamination events of sources of drinking water based on the Dempster–Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events. (paper)

  12. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

    Science.gov (United States)

    Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

    2015-08-01

    In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

  13. Camera network video summarization

    Science.gov (United States)

    Panda, Rameswar; Roy-Chowdhury, Amit K.

    2017-05-01

    Networks of vision sensors are deployed in many settings, ranging from security needs to disaster response to environmental monitoring. Many of these setups have hundreds of cameras and tens of thousands of hours of video. The difficulty of analyzing such a massive volume of video data is apparent whenever there is an incident that requires foraging through vast video archives to identify events of interest. As a result, video summarization, that automatically extract a brief yet informative summary of these videos, has attracted intense attention in the recent years. Much progress has been made in developing a variety of ways to summarize a single video in form of a key sequence or video skim. However, generating a summary from a set of videos captured in a multi-camera network still remains as a novel and largely under-addressed problem. In this paper, with the aim of summarizing videos in a camera network, we introduce a novel representative selection approach via joint embedding and capped l21-norm minimization. The objective function is two-fold. The first is to capture the structural relationships of data points in a camera network via an embedding, which helps in characterizing the outliers and also in extracting a diverse set of representatives. The second is to use a capped l21-norm to model the sparsity and to suppress the influence of data outliers in representative selection. We propose to jointly optimize both of the objectives, such that embedding can not only characterize the structure, but also indicate the requirements of sparse representative selection. Extensive experiments on standard multi-camera datasets well demonstrate the efficacy of our method over state-of-the-art methods.

  14. Activity-based exploitation of Full Motion Video (FMV)

    Science.gov (United States)

    Kant, Shashi

    2012-06-01

    Video has been a game-changer in how US forces are able to find, track and defeat its adversaries. With millions of minutes of video being generated from an increasing number of sensor platforms, the DOD has stated that the rapid increase in video is overwhelming their analysts. The manpower required to view and garner useable information from the flood of video is unaffordable, especially in light of current fiscal restraints. "Search" within full-motion video has traditionally relied on human tagging of content, and video metadata, to provision filtering and locate segments of interest, in the context of analyst query. Our approach utilizes a novel machine-vision based approach to index FMV, using object recognition & tracking, events and activities detection. This approach enables FMV exploitation in real-time, as well as a forensic look-back within archives. This approach can help get the most information out of video sensor collection, help focus the attention of overburdened analysts form connections in activity over time and conserve national fiscal resources in exploiting FMV.

  15. A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

    Science.gov (United States)

    Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

    2017-02-01

    High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Particle Filter Based Tracking in a Detection Sparse Discrete Event Simulation Environment

    National Research Council Canada - National Science Library

    Borovies, Drew A

    2007-01-01

    .... In actuality, even extremely uncertain or incomplete detections and counter-detections of opposing entities can provide enough data for entities to make reasonably intelligent decisions on the virtual battlefield...

  17. A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

    KAUST Repository

    Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

    2016-01-01

    Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures

  18. Accuracy and precision of equine gait event detection during walking with limb and trunk mounted inertial sensors

    DEFF Research Database (Denmark)

    Olsen, Emil; Andersen, Pia Haubro; Pfau, Thilo

    2012-01-01

    The increased variations of temporal gait events when pathology is present are good candidate features for objective diagnostic tests. We hypothesised that the gait events hoof-on/off and stance can be detected accurately and precisely using features from trunk and distal limb-mounted Inertial....... Accuracy (bias) and precision (SD of bias) was calculated to compare force plate and IMU timings for gait events. Data were collected from seven horses. One hundred and twenty three (123) front limb steps were analysed; hoof-on was detected with a bias (SD) of -7 (23) ms, hoof-off with 0.7 (37) ms...... and front limb stance with -0.02 (37) ms. A total of 119 hind limb steps were analysed; hoof-on was found with a bias (SD) of -4 (25) ms, hoof-off with 6 (21) ms and hind limb stance with 0.2 (28) ms. IMUs mounted on the distal limbs and sacrum can detect gait events accurately and precisely....

  19. Video games

    OpenAIRE

    Kolář, Vojtěch

    2012-01-01

    This thesis is based on a detailed analysis of various topics related to the question of whether video games can be art. In the first place it analyzes the current academic discussion on this subject and confronts different opinions of both supporters and objectors of the idea, that video games can be a full-fledged art form. The second point of this paper is to analyze the properties, that are inherent to video games, in order to find the reason, why cultural elite considers video games as i...

  20. Validating administrative data for the detection of adverse events in older hospitalized patients

    Directory of Open Access Journals (Sweden)

    Ackroyd-Stolarz S

    2014-08-01

    Full Text Available Stacy Ackroyd-Stolarz,1,2 Susan K Bowles,3–5 Lorri Giffin6 1Performance Excellence Portfolio, Capital District Health Authority, Halifax, Nova Scotia, Canada; 2Department of Emergency Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; 3Geriatric Medicine, Capital District Health Authority, Halifax, Nova Scotia, Canada; 4College of Pharmacy and Division of Geriatric Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; 5Department of Pharmacy at Capital District Health Authority, Halifax, Nova Scotia, Canada; 6South Shore Family Health, Bridgewater, Nova Scotia, Canada Abstract: Older hospitalized patients are at risk of experiencing adverse events including, but not limited to, hospital-acquired pressure ulcers, fall-related injuries, and adverse drug events. A significant challenge in monitoring and managing adverse events is lack of readily accessible information on their occurrence. Purpose: The objective of this retrospective cross-sectional study was to validate diagnostic codes for pressure ulcers, fall-related injuries, and adverse drug events found in routinely collected administrative hospitalization data. Methods: All patients 65 years of age or older discharged between April 1, 2009 and March 31, 2011 from a provincial academic health sciences center in Canada were eligible for inclusion in the validation study. For each of the three types of adverse events, a random sample of 50 patients whose records were positive and 50 patients whose records were not positive for an adverse event was sought for review in the validation study (n=300 records in total. A structured health record review was performed independently by two health care providers with experience in geriatrics, both of whom were unaware of the patient's status with respect to adverse event coding. A physician reviewed 40 records (20 reviewed by each health care provider to establish interrater agreement. Results: A total of 39 pressure ulcers, 56 fall

  1. [Comparison of the "Trigger" tool with the minimum basic data set for detecting adverse events in general surgery].

    Science.gov (United States)

    Pérez Zapata, A I; Gutiérrez Samaniego, M; Rodríguez Cuéllar, E; Gómez de la Cámara, A; Ruiz López, P

    Surgery is a high risk for the occurrence of adverse events (AE). The main objective of this study is to compare the effectiveness of the Trigger tool with the Hospital National Health System registration of Discharges, the minimum basic data set (MBDS), in detecting adverse events in patients admitted to General Surgery and undergoing surgery. Observational and descriptive retrospective study of patients admitted to general surgery of a tertiary hospital, and undergoing surgery in 2012. The identification of adverse events was made by reviewing the medical records, using an adaptation of "Global Trigger Tool" methodology, as well as the (MBDS) registered on the same patients. Once the AE were identified, they were classified according to damage and to the extent to which these could have been avoided. The area under the curve (ROC) were used to determine the discriminatory power of the tools. The Hanley and Mcneil test was used to compare both tools. AE prevalence was 36.8%. The TT detected 89.9% of all AE, while the MBDS detected 28.48%. The TT provides more information on the nature and characteristics of the AE. The area under the curve was 0.89 for the TT and 0.66 for the MBDS. These differences were statistically significant (P<.001). The Trigger tool detects three times more adverse events than the MBDS registry. The prevalence of adverse events in General Surgery is higher than that estimated in other studies. Copyright © 2017 SECA. Publicado por Elsevier España, S.L.U. All rights reserved.

  2. The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

    Science.gov (United States)

    Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

    2017-12-01

    In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long

  3. Detection of rain events in radiological early warning networks with spectro-dosimetric systems

    Science.gov (United States)

    Dąbrowski, R.; Dombrowski, H.; Kessler, P.; Röttger, A.; Neumaier, S.

    2017-10-01

    Short-term pronounced increases of the ambient dose equivalent rate, due to rainfall are a well-known phenomenon. Increases in the same order of magnitude or even below may also be caused by a nuclear or radiological event, i.e. by artificial radiation. Hence, it is important to be able to identify natural rain events in dosimetric early warning networks and to distinguish them from radiological events. Novel spectrometric systems based on scintillators may be used to differentiate between the two scenarios, because the measured gamma spectra provide significant nuclide-specific information. This paper describes three simple, automatic methods to check whether an dot H*(10) increase is caused by a rain event or by artificial radiation. These methods were applied to measurements of three spectrometric systems based on CeBr3, LaBr3 and SrI2 scintillation crystals, investigated and tested for their practicability at a free-field reference site of PTB.

  4. [Performance and optimisation of a trigger tool for the detection of adverse events in hospitalised adult patients].

    Science.gov (United States)

    Guzmán Ruiz, Óscar; Pérez Lázaro, Juan José; Ruiz López, Pedro

    To characterise the performance of the triggers used in the detection of adverse events (AE) of hospitalised adult patients and to define a simplified panel of triggers to facilitate the detection of AE. Cross-sectional study of charts of patients from a service of internal medicine to detect EA through systematic review of the charts and identification of triggers (clinical event often related to AE), determining if there was AE as the context in which it appeared the trigger. Once the EA was detected, we proceeded to the characterization of the triggers that detected it. Logistic regression was applied to select the triggers with greater AE detection capability. A total of 291 charts were reviewed, with a total of 562 triggers in 103 patients, of which 163 were involved in detecting an AE. The triggers that detected the most AE were "A.1. Pressure ulcer" (9.82%), "B.5. Laxative or enema" (8.59%), "A.8. Agitation" (8.59%), "A.9. Over-sedation" (7.98%), "A.7. Haemorrhage" (6.75%) and "B.4. Antipsychotic" (6.75%). A simplified model was obtained using logistic regression, and included the variable "Number of drugs" and the triggers "Over-sedation", "Urinary catheterisation", "Readmission in 30 days", "Laxative or enema" and "Abrupt medication stop". This model showed a probability of 81% to correctly classify charts with EA or without EA (p <0.001; 95% confidence interval: 0.763-0.871). A high number of triggers were associated with AE. The summary model is capable of detecting a large amount of AE, with a minimum of elements. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  5. Research and development of a high-temperature helium-leak detection system (joint research). Part 1 survey on leakage events and current leak detection technology

    Energy Technology Data Exchange (ETDEWEB)

    Sakaba, Nariaki; Nakazawa, Toshio; Kawasaki, Kozo [Japan Atomic Energy Research Inst., Oarai, Ibaraki (Japan). Oarai Research Establishment; Urakami, Masao; Saisyu, Sadanori [Japan Atomic Power Co., Tokyo (Japan)

    2003-03-01

    In High Temperature Gas-cooled Reactors (HTGR), the detection of leakage of helium at an early stage is very important for the safety and stability of operations. Since helium is a colourless gas, it is generally difficult to identify the location and the amount of leakage when very little leakage has occurred. The purpose of this R and D is to develop a helium leak detection system for the high temperature environment appropriate to the HTGR. As the first step in the development, this paper describes the result of surveying leakage events at nuclear facilities inside and outside Japan and current gas leakage detection technology to adapt optical-fibre detection technology to HTGRs. (author)

  6. A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

    KAUST Repository

    Zerrouki, Nabil

    2016-07-26

    Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.

  7. Akademisk video

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2017-01-01

    Dette kapitel har fokus på metodiske problemstillinger, der opstår i forhold til at bruge (digital) video i forbindelse med forskningskommunikation, ikke mindst online. Video har længe været benyttet i forskningen til dataindsamling og forskningskommunikation. Med digitaliseringen og internettet ...

  8. INTEGRAL Detection of the First Prompt Gamma-Ray Signal Coincident with the Gravitational-wave Event GW170817

    DEFF Research Database (Denmark)

    Savchenko, V.; Ferrigno, C.; Kuulkers, E.

    2017-01-01

    We report the INTernational Gamma-ray Astrophysics Laboratory (INTEGRAL) detection of the short gamma-ray burst GRB 170817A (discovered by Fermi-GBM) with a signal-to-noise ratio of 4.6, and, for the first time, its association with the gravitational waves (GWs) from binary neutron star (BNS......) merging event GW170817 detected by the LIGO and Virgo observatories. The significance of association between the gamma-ray burst observed by INTEGRAL and GW170817 is 3.2σ, while the association between the Fermi-GBM and INTEGRAL detections is 4.2σ. GRB 170817A was detected by the SPI-ACS instrument about...

  9. Evaluation of the DTBird video-system at the Smoela wind-power plant. Detection capabilities for capturing near-turbine avian behaviour

    Energy Technology Data Exchange (ETDEWEB)

    Roel, May; Hamre, Oeyvind; Vang, Roald; Nygaard, Torgeir

    2012-07-01

    Collisions between birds and wind turbines can be a problem at wind-power plants both onshore and offshore, and the presence of endangered bird species or proximity to key functional bird areas can have major impact on the choice of site or location wind turbines. There is international consensus that one of the mail challenges in the development of measures to reduce bird collisions is the lack of good methods for assessment of the efficacy of inventions. In order to be better abe to assess the efficacy of mortality-reducing measures Statkraft wishes to find a system that can be operated under Norwegian conditions and that renders objective and quantitative information on collisions and near-flying birds. DTbird developed by Liquen Consultoria Ambiental S.L. is such a system, which is based on video-recording bird flights near turbines during the daylight period (light levels>200 lux). DTBird is a self-working system developed to detect flying birds and to take programmed actions (i.e. warming, dissuasion, collision registration, and turbine stop control) linked to real-time bird detection. This report evaluates how well the DTBird system is able to detect birds in the vicinity of a wind turbine, and assess to which extent it can be utilized to study near-turbine bird flight behaviour and possible deterrence. The evaluation was based on the video sequence recorded with the DTBird systems installed at turbine 21 and turbine 42 at the Smoela wind-power plant between March 2 2012 and September 30 2012, together with GPS telemetry data on white-tailed eagles and avian radar data. The average number of falsely triggered video sequences (false positive rate) was 1.2 per day, and during daytime the DTBird system recorded between 76% and 96% of all bird flights in the vicinity of the turbines. Visually estimated distances of recorded bird flights in the video sequences were in general assessed to be farther from the turbines com pared to the distance settings used within

  10. One Novel Multiple-Target Plasmid Reference Molecule Targeting Eight Genetically Modified Canola Events for Genetically Modified Canola Detection.

    Science.gov (United States)

    Li, Zhuqing; Li, Xiang; Wang, Canhua; Song, Guiwen; Pi, Liqun; Zheng, Lan; Zhang, Dabing; Yang, Litao

    2017-09-27

    Multiple-target plasmid DNA reference materials have been generated and utilized as good substitutes of matrix-based reference materials in the analysis of genetically modified organisms (GMOs). Herein, we report the construction of one multiple-target plasmid reference molecule, pCAN, which harbors eight GM canola event-specific sequences (RF1, RF2, MS1, MS8, Topas 19/2, Oxy235, RT73, and T45) and a partial sequence of the canola endogenous reference gene PEP. The applicability of this plasmid reference material in qualitative and quantitative PCR assays of the eight GM canola events was evaluated, including the analysis of specificity, limit of detection (LOD), limit of quantification (LOQ), and performance of pCAN in the analysis of various canola samples, etc. The LODs are 15 copies for RF2, MS1, and RT73 assays using pCAN as the calibrator and 10 genome copies for the other events. The LOQ in each event-specific real-time PCR assay is 20 copies. In quantitative real-time PCR analysis, the PCR efficiencies of all event-specific and PEP assays are between 91% and 97%, and the squared regression coefficients (R 2 ) are all higher than 0.99. The quantification bias values varied from 0.47% to 20.68% with relative standard deviation (RSD) from 1.06% to 24.61% in the quantification of simulated samples. Furthermore, 10 practical canola samples sampled from imported shipments in the port of Shanghai, China, were analyzed employing pCAN as the calibrator, and the results were comparable with those assays using commercial certified materials as the calibrator. Concluding from these results, we believe that this newly developed pCAN plasmid is one good candidate for being a plasmid DNA reference material in the detection and quantification of the eight GM canola events in routine analysis.

  11. INTEGRAL Detection of the First Prompt Gamma-Ray Signal Coincident with the Gravitational-wave Event GW170817

    Energy Technology Data Exchange (ETDEWEB)

    Savchenko, V.; Ferrigno, C.; Bozzo, E.; Courvoisier, T. J.-L. [ISDC, Department of Astronomy, University of Geneva, Chemin d’Écogia, 16 CH-1290 Versoix (Switzerland); Kuulkers, E. [European Space Research and Technology Centre (ESA/ESTEC), Keplerlaan 1, 2201 AZ Noordwijk (Netherlands); Bazzano, A.; Natalucci, L.; Rodi, J. [INAF-Institute for Space Astrophysics and Planetology, Via Fosso del Cavaliere 100, I-00133-Rome (Italy); Brandt, S.; Chenevez, J. [DTU Space, National Space Institute Elektrovej, Building 327 DK-2800 Kongens Lyngby (Denmark); Diehl, R.; Von Kienlin, A. [Max-Planck-Institut für Extraterrestrische Physik, Garching (Germany); Domingo, A. [Centro de Astrobiología (CAB-CSIC/INTA, ESAC Campus), Camino bajo del Castillo S/N, E-28692 Villanueva de la Cañada, Madrid (Spain); Hanlon, L.; Martin-Carrillo, A. [Space Science Group, School of Physics, University College Dublin, Belfield, Dublin 4 (Ireland); Jourdain, E. [IRAP, Université de Toulouse, CNRS, UPS, CNES, 9 Av. Roche, F-31028 Toulouse (France); Laurent, P.; Lebrun, F. [APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris Sorbonne Paris Cité, 10 rue Alice Domont et Léonie Duquet, F-75205 Paris Cedex 13 (France); Lutovinov, A. [Space Research Institute of Russian Academy of Sciences, Profsoyuznaya 84/32, 117997 Moscow (Russian Federation); Mereghetti, S. [INAF, IASF-Milano, via E.Bassini 15, I-20133 Milano (Italy); and others

    2017-10-20

    We report the INTernational Gamma-ray Astrophysics Laboratory ( INTEGRAL ) detection of the short gamma-ray burst GRB 170817A (discovered by Fermi -GBM) with a signal-to-noise ratio of 4.6, and, for the first time, its association with the gravitational waves (GWs) from binary neutron star (BNS) merging event GW170817 detected by the LIGO and Virgo observatories. The significance of association between the gamma-ray burst observed by INTEGRAL and GW170817 is 3.2σ, while the association between the Fermi -GBM and INTEGRAL detections is 4.2σ. GRB 170817A was detected by the SPI-ACS instrument about 2 s after the end of the GW event. We measure a fluence of (1.4 ± 0.4 ± 0.6) × 10{sup −7} erg cm{sup −2} (75–2000 keV), where, respectively, the statistical error is given at the 1σ confidence level, and the systematic error corresponds to the uncertainty in the spectral model and instrument response. We also report on the pointed follow-up observations carried out by INTEGRAL , starting 19.5 hr after the event, and lasting for 5.4 days. We provide a stringent upper limit on any electromagnetic signal in a very broad energy range, from 3 keV to 8 MeV, constraining the soft gamma-ray afterglow flux to <7.1 × 10{sup −11} erg cm{sup −2} s{sup −1} (80–300 keV). Exploiting the unique capabilities of INTEGRAL , we constrained the gamma-ray line emission from radioactive decays that are expected to be the principal source of the energy behind a kilonova event following a BNS coalescence. Finally, we put a stringent upper limit on any delayed bursting activity, for example, from a newly formed magnetar.

  12. Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective.

    Science.gov (United States)

    Schwind, Jessica S; Norman, Stephanie A; Karmacharya, Dibesh; Wolking, David J; Dixit, Sameer M; Rajbhandari, Rajesh M; Mekaru, Sumiko R; Brownstein, John S

    2017-09-21

    Traditional media and the internet are crucial sources of health information. Media can significantly shape public opinion, knowledge and understanding of emerging and endemic health threats. As digital communication rapidly progresses, local access and dissemination of health information contribute significantly to global disease detection and reporting. Health event reports in Nepal (October 2013-December 2014) were used to characterize Nepal's media environment from a One Health perspective using HealthMap - a global online disease surveillance and mapping tool. Event variables (location, media source type, disease or risk factor of interest, and affected species) were extracted from HealthMap. A total of 179 health reports were captured from various sources including newspapers, inter-government agency bulletins, individual reports, and trade websites, yielding 108 (60%) unique articles. Human health events were reported most often (n = 85; 79%), followed by animal health events (n = 23; 21%), with no reports focused solely on environmental health. By expanding event coverage across all of the health sectors, media in developing countries could play a crucial role in national risk communication efforts and could enhance early warning systems for disasters and disease outbreaks.

  13. Snake scales, partial exposure, and the Snake Detection Theory: A human event-related potentials study

    NARCIS (Netherlands)

    J.W. van Strien (Jan); L.A. Isbell (Lynne A.)

    2017-01-01

    textabstractStudies of event-related potentials in humans have established larger early posterior negativity (EPN) in response to pictures depicting snakes than to pictures depicting other creatures. Ethological research has recently shown that macaques and wild vervet monkeys respond strongly to

  14. Detection and monitoring of two dust storm events by multispectral modis images.

    Digital Repository Service at National Institute of Oceanography (India)

    Mehta P.S.; Kunte, P.D.

    of Oman, over Arabian Sea to the coast of Pakistan. The dust storm lasted over the Arabian Sea till 30th March. MODIS sensors on both Terra and Aqua Satellites captured images of both events. From the difference in emissive/transmissive nature...

  15. Oil spill detection and remote sensing : an overview with focus on recent events

    International Nuclear Information System (INIS)

    Coolbaugh, T.S.

    2008-01-01

    Several offshore oil spills occurred during the period from November to December 2007 in various parts of the world, each highlighting the need of quickly detect oil spills in marine settings. Several factors must be considered in order to determine the best technical approach for successful detection and oil spill monitoring. These include the reason for detection or monitoring; the location of the spill; the scale of spatial coverage; availability of detection equipment and time to deploy; high specificity for petroleum oil; weather conditions at and above the spill site; and cost of the detection approach. This paper outlined some of the key attributes of several remote sensing options that are available today or being considered. The approaches used to enhance visualization or detection of spills include traditional electromagnetic spectrum-based approaches such as ultra violet (UV), visible, infra-red (IR), radar, and fluorescence-based systems. Analytical approaches such as chemical analysis for the detection of volatile organic compounds (VOCs) or monitoring of electrical conductivity of the water surface may also provide a warning that hydrocarbons have been released. 12 refs., 3 tabs., 11 figs

  16. Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2013-01-01

    desired levels of false alarms and detection probabilities. Self-tuning residual generators are employed for diagnosis and are combined with statistical change detection to form a setup for robust fault diagnosis. On-line estimation of test statistics is used to obtain a detection threshold and a desired...... false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft....

  17. Detecting Specific Health-Related Events Using an Integrated Sensor System for Vital Sign Monitoring

    Directory of Open Access Journals (Sweden)

    Mourad Adnane

    2009-09-01

    Full Text Available In this paper, a new method for the detection of apnea/hypopnea periods in physiological data is presented. The method is based on the intelligent combination of an integrated sensor system for long-time cardiorespiratory signal monitoring and dedicated signal-processing packages. Integrated sensors are a PVDF film and conductive fabric sheets. The signal processing package includes dedicated respiratory cycle (RC and QRS complex detection algorithms and a new method using the respiratory cycle variability (RCV for detecting apnea/hypopnea periods in physiological data. Results show that our method is suitable for online analysis of long time series data.

  18. Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

    Science.gov (United States)

    Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

    2017-05-01

    The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T 2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

  19. Video Podcasts

    DEFF Research Database (Denmark)

    Nortvig, Anne Mette; Sørensen, Birgitte Holm

    2016-01-01

    This project’s aim was to support and facilitate master’s students’ preparation and collaboration by making video podcasts of short lectures available on YouTube prior to students’ first face-to-face seminar. The empirical material stems from group interviews, from statistical data created through...... YouTube analytics and from surveys answered by students after the seminar. The project sought to explore how video podcasts support learning and reflection online and how students use and reflect on the integration of online activities in the videos. Findings showed that students engaged actively...

  20. Video games.

    Science.gov (United States)

    Funk, Jeanne B

    2005-06-01

    The video game industry insists that it is doing everything possible to provide information about the content of games so that parents can make informed choices; however, surveys indicate that ratings may not reflect consumer views of the nature of the content. This article describes some of the currently popular video games, as well as developments that are on the horizon, and discusses the status of research on the positive and negative impacts of playing video games. Recommendations are made to help parents ensure that children play games that are consistent with their values.

  1. The association of incidentally detected heart valve calcification with future cardiovascular events

    OpenAIRE

    Gondrie, Martijn J. A.; van der Graaf, Yolanda; Jacobs, Peter C.; Oen, Ay L.; Mali, Willem P. Th. M.

    2010-01-01

    Objectives This study aims to investigate the prognostic value of incidental aortic valve calcification (AVC), mitral valve calcification (MVC) and mitral annular calcification (MAC) for cardiovascular events and non-rheumatic valve disease in particular on routine diagnostic chest CT. Methods The study followed a case-cohort design. 10410 patients undergoing chest CT were followed for a median period of 17 months. Patients referred for cardiovascular disease were excluded. A random sample of...

  2. Passive Visual Analytics of Social Media Data for Detection of Unusual Events

    OpenAIRE

    Rustagi, Kush; Chae, Junghoon

    2016-01-01

    Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders...

  3. Enriched encoding: reward motivation organizes cortical networks for hippocampal detection of unexpected events.

    Science.gov (United States)

    Murty, Vishnu P; Adcock, R Alison

    2014-08-01

    Learning how to obtain rewards requires learning about their contexts and likely causes. How do long-term memory mechanisms balance the need to represent potential determinants of reward outcomes with the computational burden of an over-inclusive memory? One solution would be to enhance memory for salient events that occur during reward anticipation, because all such events are potential determinants of reward. We tested whether reward motivation enhances encoding of salient events like expectancy violations. During functional magnetic resonance imaging, participants performed a reaction-time task in which goal-irrelevant expectancy violations were encountered during states of high- or low-reward motivation. Motivation amplified hippocampal activation to and declarative memory for expectancy violations. Connectivity of the ventral tegmental area (VTA) with medial prefrontal, ventrolateral prefrontal, and visual cortices preceded and predicted this increase in hippocampal sensitivity. These findings elucidate a novel mechanism whereby reward motivation can enhance hippocampus-dependent memory: anticipatory VTA-cortical-hippocampal interactions. Further, the findings integrate literatures on dopaminergic neuromodulation of prefrontal function and hippocampus-dependent memory. We conclude that during reward motivation, VTA modulation induces distributed neural changes that amplify hippocampal signals and records of expectancy violations to improve predictions-a potentially unique contribution of the hippocampus to reward learning. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Detection of explosive cough events in audio recordings by internal sound analysis.

    Science.gov (United States)

    Rocha, B M; Mendes, L; Couceiro, R; Henriques, J; Carvalho, P; Paiva, R P

    2017-07-01

    We present a new method for the discrimination of explosive cough events, which is based on a combination of spectral content descriptors and pitch-related features. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 9 features is extracted for each event. Two data sets, recorded using electronic stethoscopes and comprising a total of 46 healthy subjects and 13 patients, were employed to evaluate the method. The proposed feature set is compared to three other sets of descriptors: a baseline, a combination of both sets, and an automatic selection of the best 10 features from both sets. The combined feature set yields good results on the cross-validated database, attaining a sensitivity of 92.3±2.3% and a specificity of 84.7±3.3%. Besides, this feature set seems to generalize well when it is trained on a small data set of patients, with a variety of respiratory and cardiovascular diseases, and tested on a bigger data set of mostly healthy subjects: a sensitivity of 93.4% and a specificity of 83.4% are achieved in those conditions. These results demonstrate that complementing the proposed feature set with a baseline set is a promising approach.

  5. {sup 123}I-MIBG imaging detects cardiac involvement and predicts cardiac events in Churg-Strauss syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Horiguchi, Yoriko; Morita, Yukiko [National Hospital Organization Sagamihara National Hospital, Department of Cardiology, Sagamihara City, Kanagawa (Japan); Tsurikisawa, Naomi; Akiyama, Kazuo [National Hospital Organization Sagamihara National Hospital, Clinical Research Centre for Allergy and Rheumatology, Sagamihara City, Kanagawa (Japan)

    2011-02-15

    In Churg-Strauss syndrome (CSS) it is important to detect cardiac involvement, which predicts poor prognosis. This study evaluated whether {sup 123}I-metaiodobenzylguanidine (MIBG) scintigraphy could detect cardiac damage and predict cardiac events in CSS. {sup 123}I-MIBG scintigraphy was performed in 28 patients with CSS, 12 of whom had cardiac involvement. The early and delayed heart to mediastinum ratio (early H/M and delayed H/M) and washout rate were calculated by using {sup 123}I-MIBG scintigraphy and compared with those in control subjects. Early H/M and delayed H/M were significantly lower and the washout rate was significantly higher in patients with cardiac involvement than in those without and in controls (early H/M, p = 0.0024, p = 0.0001; delayed H/M, p = 0.0002, p = 0.0001; washout rate, p = 0.0012, p = 0.0052 vs those without and vs controls, respectively). Accuracy for detecting cardiac involvement was 86% for delayed H/M and washout rate and 79% for early H/M and B-type natriuretic peptide (BNP). Kaplan-Meier analysis showed significantly lower cardiac event-free rates in patients with early H/M {<=} 2.18 and BNP > 21.8 pg/ml than those with early H/M > 2.18 and BNP {<=} 21.8 pg/ml (log-rank test p = 0.006). Cardiac sympathetic nerve function was damaged in CSS patients with cardiac involvement. {sup 123}I-MIBG scintigraphy was useful in detecting cardiac involvement and in predicting cardiac events. (orig.)

  6. 123I-MIBG imaging detects cardiac involvement and predicts cardiac events in Churg-Strauss syndrome

    International Nuclear Information System (INIS)

    Horiguchi, Yoriko; Morita, Yukiko; Tsurikisawa, Naomi; Akiyama, Kazuo

    2011-01-01

    In Churg-Strauss syndrome (CSS) it is important to detect cardiac involvement, which predicts poor prognosis. This study evaluated whether 123 I-metaiodobenzylguanidine (MIBG) scintigraphy could detect cardiac damage and predict cardiac events in CSS. 123 I-MIBG scintigraphy was performed in 28 patients with CSS, 12 of whom had cardiac involvement. The early and delayed heart to mediastinum ratio (early H/M and delayed H/M) and washout rate were calculated by using 123 I-MIBG scintigraphy and compared with those in control subjects. Early H/M and delayed H/M were significantly lower and the washout rate was significantly higher in patients with cardiac involvement than in those without and in controls (early H/M, p = 0.0024, p = 0.0001; delayed H/M, p = 0.0002, p = 0.0001; washout rate, p = 0.0012, p = 0.0052 vs those without and vs controls, respectively). Accuracy for detecting cardiac involvement was 86% for delayed H/M and washout rate and 79% for early H/M and B-type natriuretic peptide (BNP). Kaplan-Meier analysis showed significantly lower cardiac event-free rates in patients with early H/M ≤ 2.18 and BNP > 21.8 pg/ml than those with early H/M > 2.18 and BNP ≤ 21.8 pg/ml (log-rank test p = 0.006). Cardiac sympathetic nerve function was damaged in CSS patients with cardiac involvement. 123 I-MIBG scintigraphy was useful in detecting cardiac involvement and in predicting cardiac events. (orig.)

  7. The Diurnal Variation of the Wimp Detection Event Rates in Directional Experiments

    CERN Document Server

    Vergados, J D

    2009-01-01

    The recent WMAP data have confirmed that exotic dark matter together with the vacuum energy (cosmological constant) dominate in the flat Universe. Modern particle theories naturally provide viable cold dark matter candidates with masses in the GeV-TeV region. Supersymmetry provides the lightest supersymmetric particle (LSP), theories in extra dimensions supply the lightest Kaluza-Klein particle (LKP) etc. The nature of dark matter can only be unraveled only by its direct detection in the laboratory. All such candidates will be called WIMPs (Weakly Interacting Massive Particles). In any case the direct dark matter search, which amounts to detecting the recoiling nucleus, following its collision with WIMP, is central to particle physics and cosmology. In this work we briefly review the theoretical elements relevant to the direct dark matter detection experiments, paying particular attention to directional experiments. i.e experiments in which, not only the energy but the direction of the recoiling nucleus is ob...

  8. Video Comparator

    International Nuclear Information System (INIS)

    Rose, R.P.

    1978-01-01

    The Video Comparator is a comparative gage that uses electronic images from two sources, a standard and an unknown. Two matched video cameras are used to obtain the electronic images. The video signals are mixed and displayed on a single video receiver (CRT). The video system is manufactured by ITP of Chatsworth, CA and is a Tele-Microscope II, Model 148. One of the cameras is mounted on a toolmaker's microscope stand and produces a 250X image of a cast. The other camera is mounted on a stand and produces an image of a 250X template. The two video images are mixed in a control box provided by ITP and displayed on a CRT. The template or the cast can be moved to align the desired features. Vertical reference lines are provided on the CRT, and a feature on the cast can be aligned with a line on the CRT screen. The stage containing the casts can be moved using a Boeckleler micrometer equipped with a digital readout, and a second feature aligned with the reference line and the distance moved obtained from the digital display

  9. Short Personality and Life Event scale for detection of suicide attempters.

    Science.gov (United States)

    Artieda-Urrutia, Paula; Delgado-Gómez, David; Ruiz-Hernández, Diego; García-Vega, Juan Manuel; Berenguer, Nuria; Oquendo, Maria A; Blasco-Fontecilla, Hilario

    2015-01-01

    To develop a brief and reliable psychometric scale to identify individuals at risk for suicidal behaviour. Case-control study. 182 individuals (61 suicide attempters, 57 psychiatric controls, and 64 psychiatrically healthy controls) aged 18 or older, admitted to the Emergency Department at Puerta de Hierro University Hospital in Madrid, Spain. All participants completed a form including their socio-demographic and clinical characteristics, and the Personality and Life Events scale (27 items). To assess Axis I diagnoses, all psychiatric patients (including suicide attempters) were administered the Mini International Neuropsychiatric Interview. Descriptive statistics were computed for the socio-demographic factors. Additionally, χ(2) independence tests were applied to evaluate differences in socio-demographic and clinical variables, and the Personality and Life Events scale between groups. A stepwise linear regression with backward variable selection was conducted to build the Short Personality Life Event (S-PLE) scale. In order to evaluate the accuracy, a ROC analysis was conducted. The internal reliability was assessed using Cronbach's α, and the external reliability was evaluated using a test-retest procedure. The S-PLE scale, composed of just 6 items, showed good performance in discriminating between medical controls, psychiatric controls and suicide attempters in an independent sample. For instance, the S-PLE scale discriminated between past suicide and past non-suicide attempters with sensitivity of 80% and specificity of 75%. The area under the ROC curve was 88%. A factor analysis extracted only one factor, revealing a single dimension of the S-PLE scale. Furthermore, the S-PLE scale provides values of internal and external reliability between poor (test-retest: 0.55) and acceptable (Cronbach's α: 0.65) ranges. Administration time is about one minute. The S-PLE scale is a useful and accurate instrument for estimating the risk of suicidal behaviour in

  10. MEDUSA: Mining Events to Detect Undesirable uSer Actions in SCADA

    NARCIS (Netherlands)

    Hadziosmanovic, D.; Bolzoni, D.; Hartel, Pieter H.; Jha, Somesh; Sommer, Robin; Kreibich, Christian

    2010-01-01

    Standard approaches for detecting malicious behaviors, e.g. monitoring network traffic, cannot address process-related threats in SCADA(Supervisory Control And Data Acquisition) systems. These threats take place when an attacker gains user access rights and performs actions which look legitimate,

  11. Extranuclear detection of histones and nucleosomes in activated human lymphoblasts as an early event in apoptosis.

    NARCIS (Netherlands)

    Gabler, C.; Blank, N.; Hieronymus, T.; Schiller, M.; Berden, J.H.M.; Kalden, J.R.; Lorenz, H.M.

    2004-01-01

    OBJECTIVE: To evaluate the presence of histones and nucleosomes in cell lysates of freshly isolated peripheral blood mononuclear cells (PBMC), fully activated lymphoblasts, or lymphoblasts after induction of apoptosis. METHODS: Each histone class (H1, H2A, H2B, H3, and H4) was detected by western

  12. USBeSafe: Applying One Class SVM for Effective USB Event Anomaly Detection

    Science.gov (United States)

    2016-04-25

    2012. [5] Phil Muncaster. Indian navy computers stormed by malware-ridden USBs. 2012. [6] Ponemon. 2011 Second Annual Cost of Cyber Crime Study...Zhang, and Shanshan Sun. “A mixed unsu- pervised clustering-based intrusion detection model”. In: Genetic and Evolutionary Computing, 2009. WGEC’09

  13. myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection

    Directory of Open Access Journals (Sweden)

    Junho Ahn

    2016-05-01

    Full Text Available We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users’ daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period.

  14. Exploring design tradeoffs of a distributed algorithm for cosmic ray event detection

    Science.gov (United States)

    Yousaf, S.; Bakhshi, R.; van Steen, M.; Voulgaris, S.; Kelley, J. L.

    2013-03-01

    Many sensor networks, including large particle detector arrays measuring high-energy cosmic-ray air showers, traditionally rely on centralised trigger algorithms to find spatial and temporal coincidences of individual nodes. Such schemes suffer from scalability problems, especially if the nodes communicate wirelessly or have bandwidth limitations. However, nodes which instead communicate with each other can, in principle, use a distributed algorithm to find coincident events themselves without communication with a central node. We present such an algorithm and consider various design tradeoffs involved, in the context of a potential trigger for the Auger Engineering Radio Array (AERA).

  15. Analyzing direct dark matter detection data with unrejected background events by the AMIDAS website

    International Nuclear Information System (INIS)

    Shan, Chung-Lin

    2012-01-01

    In this talk I have presented the data analysis results of extracting properties of halo WIMPs: the mass and the (ratios between the) spin-independent and spin-dependent couplings/cross sections on nucleons by the AMIDAS website by taking into account possible unrejected background events in the analyzed data sets. Although non-standard astronomical setup has been used to generate pseudodata sets for our analyses, it has been found that, without prior information/assumption about the local density and velocity distribution of halo Dark Matter, these WIMP properties have been reconstructed with ∼ 2% to ∼< 30% deviations from the input values.

  16. Reduction in Fall Rate in Dementia Managed Care Through Video Incident Review: Pilot Study.

    Science.gov (United States)

    Bayen, Eleonore; Jacquemot, Julien; Netscher, George; Agrawal, Pulkit; Tabb Noyce, Lynn; Bayen, Alexandre

    2017-10-17

    Falls of individuals with dementia are frequent, dangerous, and costly. Early detection and access to the history of a fall is crucial for efficient care and secondary prevention in cognitively impaired individuals. However, most falls remain unwitnessed events. Furthermore, understanding why and how a fall occurred is a challenge. Video capture and secure transmission of real-world falls thus stands as a promising assistive tool. The objective of this study was to analyze how continuous video monitoring and review of falls of individuals with dementia can support better quality of care. A pilot observational study (July-September 2016) was carried out in a Californian memory care facility. Falls were video-captured (24×7), thanks to 43 wall-mounted cameras (deployed in all common areas and in 10 out of 40 private bedrooms of consenting residents and families). Video review was provided to facility staff, thanks to a customized mobile device app. The outcome measures were the count of residents' falls happening in the video-covered areas, the acceptability of video recording, the analysis of video review, and video replay possibilities for care practice. Over 3 months, 16 falls were video-captured. A drop in fall rate was observed in the last month of the study. Acceptability was good. Video review enabled screening for the severity of falls and fall-related injuries. Video replay enabled identifying cognitive-behavioral deficiencies and environmental circumstances contributing to the fall. This allowed for secondary prevention in high-risk multi-faller individuals and for updated facility care policies regarding a safer living environment for all residents. Video monitoring offers high potential to support conventional care in memory care facilities. ©Eleonore Bayen, Julien Jacquemot, George Netscher, Pulkit Agrawal, Lynn Tabb Noyce, Alexandre Bayen. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.10.2017.

  17. The Tendency of the Crest Factor Helps Detect Nascent Events; Electronic Circuit, Software and Applications to Signals from Diverse Fields

    Directory of Open Access Journals (Sweden)

    Núñez-Pérez Ricardo Francisco

    2014-04-01

    Full Text Available Within the signal analysis techniques in the time domain, the crest factor (CF is undoubtedly one of the most simple and fast to implement using electronic circuits and/or software. That's why it has been used reliably to care for machinery and to evaluate the quality of supply. One of the major manufacturers of instruments for these purposes is Bruel and Kjaer and defines the crest factor of voltage or repetitive current signal as the ratio of the peak level and its rms value during a certain period of time. In this paper, we try to find out experimentally the potential of CF and their tendency to detect the nascent and evolution of events in various fields of knowledge, either by generating it with a developed electronic circuit, or with calculations, through routines that are performed with the programs DADISP and LabVIEW. The results are validated and checked for all the above factors and trends through a comparison between them and the proposed features and specifications. The results were acceptable so that the tools were applied to detect early faults in electrical machines, to identify chaosity differences between the circuits with these dynamics, to detect abnormal respiratory distress or rales in patients and to detect harmful distortions in the electrical current, all this based on simulations and measurements for each of the 4 cases studied. Other CF original applications proposed are: a control of chaos in electronic circuits that stir/ mix industrial processes and b correct the power factor of non-linear and inductive loads. A medium-term study and use a CF that considers the maximum signal peak to peak is contemplated, and it is thought that it can improve event detection

  18. ON THE DETECTABILITY OF A PREDICTED MESOLENSING EVENT ASSOCIATED WITH THE HIGH PROPER MOTION STAR VB 10

    International Nuclear Information System (INIS)

    Lépine, Sébastien; DiStefano, Rosanne

    2012-01-01

    Extrapolation of the astrometric motion of the nearby low-mass star VB 10 indicates that sometime in late 2011 December or during the first 2-3 months of 2012, the star will make a close approach to a background point source. Based on astrometric uncertainties, we estimate a 1 in 2 chance that the distance of closest approach ρ min will be less than 100 mas, a 1 in 5 chance that ρ min min J planet on a moderately wide (≈0.18 AU–0.84 AU) orbit, there is a chance (1% to more than 10%, depending on the distance of closest approach and orbital period and inclination) that a passage of the planet closer to the background source will result in a secondary event of higher magnification. The detection of secondary events could be made possible with a several-times-per-night multi-site monitoring campaign.

  19. DETECTION OF SUPERSONIC DOWNFLOWS AND ASSOCIATED HEATING EVENTS IN THE TRANSITION REGION ABOVE SUNSPOTS

    Energy Technology Data Exchange (ETDEWEB)

    Kleint, L.; Martínez-Sykora, J. [Bay Area Environmental Research Institute, 625 2nd Street, Ste. 209, Petaluma, CA (United States); Antolin, P. [National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588 (Japan); Tian, H.; Testa, P.; Reeves, K. K.; McKillop, S.; Saar, S.; Golub, L. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Judge, P. [High Altitude Observatory/NCAR, P.O. Box 3000, Boulder, CO 80307 (United States); De Pontieu, B.; Wuelser, J. P.; Boerner, P.; Hurlburt, N.; Lemen, J.; Tarbell, T. D.; Title, A. [Lockheed Martin Solar and Astrophysics Laboratory, 3251 Hanover St., Org. ADBS, Bldg. 252, Palo Alto, CA 94304 (United States); Carlsson, M.; Hansteen, V. [Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029, Blindern, NO-0315 Oslo (Norway); Jaeggli, S., E-mail: lucia.kleint@fhnw.ch [Department of Physics, Montana State University, Bozeman, P.O. Box 173840, Bozeman, MT 59717 (United States); and others

    2014-07-10

    Interface Region Imaging Spectrograph data allow us to study the solar transition region (TR) with an unprecedented spatial resolution of 0.''33. On 2013 August 30, we observed bursts of high Doppler shifts suggesting strong supersonic downflows of up to 200 km s{sup –1} and weaker, slightly slower upflows in the spectral lines Mg II h and k, C II 1336, Si IV 1394 Å, and 1403 Å, that are correlated with brightenings in the slitjaw images (SJIs). The bursty behavior lasts throughout the 2 hr observation, with average burst durations of about 20 s. The locations of these short-lived events appear to be the umbral and penumbral footpoints of EUV loops. Fast apparent downflows are observed along these loops in the SJIs and in the Atmospheric Imaging Assembly, suggesting that the loops are thermally unstable. We interpret the observations as cool material falling from coronal heights, and especially coronal rain produced along the thermally unstable loops, which leads to an increase of intensity at the loop footpoints, probably indicating an increase of density and temperature in the TR. The rain speeds are on the higher end of previously reported speeds for this phenomenon, and possibly higher than the free-fall velocity along the loops. On other observing days, similar bright dots are sometimes aligned into ribbons, resembling small flare ribbons. These observations provide a first insight into small-scale heating events in sunspots in the TR.

  20. NuSTAR Detection of X-Ray Heating Events in the Quiet Sun

    Science.gov (United States)

    Kuhar, Matej; Krucker, Säm; Glesener, Lindsay; Hannah, Iain G.; Grefenstette, Brian W.; Smith, David M.; Hudson, Hugh S.; White, Stephen M.

    2018-04-01

    The explanation of the coronal heating problem potentially lies in the existence of nanoflares, numerous small-scale heating events occurring across the whole solar disk. In this Letter, we present the first imaging spectroscopy X-ray observations of three quiet Sun flares during the Nuclear Spectroscopic Telescope ARray (NuSTAR) solar campaigns on 2016 July 26 and 2017 March 21, concurrent with the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) observations. Two of the three events showed time lags of a few minutes between peak X-ray and extreme ultraviolet emissions. Isothermal fits with rather low temperatures in the range 3.2–4.1 MK and emission measures of (0.6–15) × 1044 cm‑3 describe their spectra well, resulting in thermal energies in the range (2–6) × 1026 erg. NuSTAR spectra did not show any signs of a nonthermal or higher temperature component. However, as the estimated upper limits of (hidden) nonthermal energy are comparable to the thermal energy estimates, the lack of a nonthermal component in the observed spectra is not a constraining result. The estimated Geostationary Operational Environmental Satellite (GOES) classes from the fitted values of temperature and emission measure fall between 1/1000 and 1/100 A class level, making them eight orders of magnitude fainter in soft X-ray flux than the largest solar flares.