WorldWideScience

Sample records for intelligent target detection

  1. A Targeted Attack For Enhancing Resiliency of Intelligent Intrusion Detection Modules in Energy Cyber Physical Systems

    Youssef, Tarek [Florida Intl Univ., Miami, FL (United States); El Hariri, Mohammad [Florida Intl Univ., Miami, FL (United States); Habib, Hani [Florida Intl Univ., Miami, FL (United States); Mohammed, Osama [Florida Intl Univ., Miami, FL (United States); Harmon, E [Florida Intl Univ., Miami, FL (United States)

    2017-02-28

    Abstract— Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time- critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware to trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.

  2. Time Sensitive Targeting: Overcoming the Intelligence Gap in Interagency Operations

    Hewitt, Mark

    2003-01-01

    The Central Intelligence Agency's attack on a group of terrorists in Yemen epitomized the agency's short-notice capability to detect, track, and destroy a highly mobile and fleeting target of opportunity. The U.S...

  3. Targeted business intelligence pays off.

    Hennen, James

    2009-03-01

    Application business intelligence can accomplish much of what large-scale, enterprisewide efforts can accomplish: Focus on a variety of data that are interrelated in a meaningful way, Support decision making at multiple levels within a given organization, Leverage data that are already captured but not fully used, Provide actionable information and support quick response via a dashboard or control panel.

  4. EDICAM (Event Detection Intelligent Camera)

    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.

  5. Event detection intelligent camera development

    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. Intelligent detection of microcalcification from digitized mammograms

    Permanent link: https://www.ias.ac.in/article/fulltext/sadh/036/01/0125-0139. Keywords. Breast cancer; digital mammogram; artificial neural networks; microcalcification detection. Abstract. This paper reports the design and implementation of an intelligent system for detection of microcalcification from digital mammograms.

  7. Detection and intelligent systems for homeland security

    Voeller, John G

    2014-01-01

    Detection and Intelligent Systems for Homeland Security features articles from the Wiley Handbook of Science and Technology for Homeland Security covering advanced technology for image and video interpretation systems used for surveillance, which help in solving such problems as identifying faces from live streaming or stored videos. Biometrics for human identification, including eye retinas and irises, and facial patterns are also presented. The book then provides information on sensors for detection of explosive and radioactive materials and methods for sensing chemical

  8. Toward Intelligent Software Defect Detection

    Benson, Markland J.

    2011-01-01

    Source code level software defect detection has gone from state of the art to a software engineering best practice. Automated code analysis tools streamline many of the aspects of formal code inspections but have the drawback of being difficult to construct and either prone to false positives or severely limited in the set of defects that can be detected. Machine learning technology provides the promise of learning software defects by example, easing construction of detectors and broadening the range of defects that can be found. Pinpointing software defects with the same level of granularity as prominent source code analysis tools distinguishes this research from past efforts, which focused on analyzing software engineering metrics data with granularity limited to that of a particular function rather than a line of code.

  9. Artificial Intelligence In Automatic Target Recognizers: Technology And Timelines

    Gilmore, John F.

    1984-12-01

    The recognition of targets in thermal imagery has been a problem exhaustively analyzed in its current localized dimension. This paper discusses the application of artificial intelligence (AI) technology to automatic target recognition, a concept capable of expanding current ATR efforts into a new globalized dimension. Deficiencies of current automatic target recognition systems are reviewed in terms of system shortcomings. Areas of artificial intelligence which show the most promise in improving ATR performance are analyzed, and a timeline is formed in light of how near (as well as far) term artificial intelligence applications may exist. Current research in the area of high level expert vision systems is reviewed and the possible utilization of artificial intelligence architectures to improve low level image processing functions is also discussed. Additional application areas of relevance to solving the problem of automatic target recognition utilizing both high and low level processing are also explored.

  10. A new rechargeable intelligent vehicle detection sensor

    Lin, L; Han, X B; Ding, R; Li, G; Lu, Steven C-Y; Hong, Q

    2005-01-01

    Intelligent Transportation System (ITS) is a valid approach to solve the increasing transportation issue in cities. Vehicle detection is one of the key technologies in ITS. The ITS collects and processes traffic data (vehicle flow, vehicular speed, vehicle density and occupancy ratios) from vehicle detection sensors buried under the road or installed along the road. Inductive loop detector as one type of the vehicle detector is applied extensively, with the characters of stability, high value to cost ratio and feasibility. On the other hand, most of the existing inductive loop vehicle detection sensors have some weak points such as friability of detective loop, huge engineering for setting and traffic interruption during installing the sensor. The design and reality of a new rechargeable intelligent vehicle detection sensor is presented in this paper against these weak points existing now. The sensor consists of the inductive loop detector, the rechargeable batteries, the MCU (microcontroller) and the transmitter. In order to reduce the installing project amount, make the loop durable and easily maintained, the volume of the detective loop is reduced as much as we can. Communication in RF (radio frequency) brings on the advantages of getting rid of the feeder cable completely and reducing the installing project amount enormously. For saving the cable installation, the sensor is supplied by the rechargeable batteries. The purpose of the intelligent management of the energy and transmitter by means of MCU is to minimize the power consumption and prolong the working period of the sensor. In a word, the new sensor is more feasible with smaller volume, wireless communication, rechargeable batteries, low power consumption, low cost, high detector precision and easy maintenance and installation

  11. A new rechargeable intelligent vehicle detection sensor

    Lin, L [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Han, X B [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Ding, R [Tianjin University of Technology and Education, Tianjin 300222 (China); Li, G [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Lu, Steven C-Y [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Hong, Q [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China)

    2005-01-01

    Intelligent Transportation System (ITS) is a valid approach to solve the increasing transportation issue in cities. Vehicle detection is one of the key technologies in ITS. The ITS collects and processes traffic data (vehicle flow, vehicular speed, vehicle density and occupancy ratios) from vehicle detection sensors buried under the road or installed along the road. Inductive loop detector as one type of the vehicle detector is applied extensively, with the characters of stability, high value to cost ratio and feasibility. On the other hand, most of the existing inductive loop vehicle detection sensors have some weak points such as friability of detective loop, huge engineering for setting and traffic interruption during installing the sensor. The design and reality of a new rechargeable intelligent vehicle detection sensor is presented in this paper against these weak points existing now. The sensor consists of the inductive loop detector, the rechargeable batteries, the MCU (microcontroller) and the transmitter. In order to reduce the installing project amount, make the loop durable and easily maintained, the volume of the detective loop is reduced as much as we can. Communication in RF (radio frequency) brings on the advantages of getting rid of the feeder cable completely and reducing the installing project amount enormously. For saving the cable installation, the sensor is supplied by the rechargeable batteries. The purpose of the intelligent management of the energy and transmitter by means of MCU is to minimize the power consumption and prolong the working period of the sensor. In a word, the new sensor is more feasible with smaller volume, wireless communication, rechargeable batteries, low power consumption, low cost, high detector precision and easy maintenance and installation.

  12. Moving Target Detection and Active Tracking with a Multicamera Network

    Long Zhao

    2014-01-01

    Full Text Available We propose a systematic framework for Intelligence Video Surveillance System (IVSS with a multicamera network. The proposed framework consists of low-cost static and PTZ cameras, target detection and tracking algorithms, and a low-cost PTZ camera feedback control algorithm based on target information. The target detection and tracking is realized by fixed cameras using a moving target detection and tracking algorithm; the PTZ camera is manoeuvred to actively track the target from the tracking results of the static camera. The experiments are carried out using practical surveillance system data, and the experimental results show that the systematic framework and algorithms presented in this paper are efficient.

  13. Active Detection for Exposing Intelligent Attacks in Control Systems

    Weerakkody, Sean [Carnegie Mellon Univ., Pittsburgh, PA (United States); Ozel, Omur [Carnegie Mellon Univ., Pittsburgh, PA (United States); Griffioen, Paul [Carnegie Mellon Univ., Pittsburgh, PA (United States); Sinopoli, Bruno [Carnegie Mellon Univ., Pittsburgh, PA (United States)

    2017-07-01

    In this paper, we consider approaches for detecting integrity attacks carried out by intelligent and resourceful adversaries in control systems. Passive detection techniques are often incorporated to identify malicious behavior. Here, the defender utilizes finely-tuned algorithms to process information and make a binary decision, whether the system is healthy or under attack. We demonstrate that passive detection can be ineffective against adversaries with model knowledge and access to a set of input/output channels. We then propose active detection as a tool to detect attacks. In active detection, the defender leverages degrees of freedom he has in the system to detect the adversary. Specifically, the defender will introduce a physical secret kept hidden from the adversary, which can be utilized to authenticate the dynamics. In this regard, we carefully review two approaches for active detection: physical watermarking at the control input, and a moving target approach for generating system dynamics. We examine practical considerations for implementing these technologies and discuss future research directions.

  14. The Bering Autonomous Target Detection

    Jørgensen, John Leif; Denver, Troelz; Betto, Maurizio

    2003-01-01

    An autonomous asteroid target detection and tracking method has been developed. The method features near omnidirectionality and focus on high speed operations and completeness of search of the near space rather than the traditional faint object search methods, employed presently at the larger...... telescopes. The method has proven robust in operation and is well suited for use onboard spacecraft. As development target for the method and the associated instrumentation the asteroid research mission Bering has been used. Onboard a spacecraft, the autonomous detection is centered around the fully...... autonomous star tracker the Advanced Stellar Compass (ASC). One feature of this instrument is that potential targets are registered directly in terms of date, right ascension, declination, and intensity, which greatly facilitates both tracking search and registering. Results from ground and inflight tests...

  15. An intelligent detecting system for permeability prediction of MBR.

    Han, Honggui; Zhang, Shuo; Qiao, Junfei; Wang, Xiaoshuang

    2018-01-01

    The membrane bioreactor (MBR) has been widely used to purify wastewater in wastewater treatment plants. However, a critical difficulty of the MBR is membrane fouling. To reduce membrane fouling, in this work, an intelligent detecting system is developed to evaluate the performance of MBR by predicting the membrane permeability. This intelligent detecting system consists of two main parts. First, a soft computing method, based on the partial least squares method and the recurrent fuzzy neural network, is designed to find the nonlinear relations between the membrane permeability and the other variables. Second, a complete new platform connecting the sensors and the software is built, in order to enable the intelligent detecting system to handle complex algorithms. Finally, the simulation and experimental results demonstrate the reliability and effectiveness of the proposed intelligent detecting system, underlying the potential of this system for the online membrane permeability for detecting membrane fouling of MBR.

  16. Detection of Intelligent Intruders in Wireless Sensor Networks

    Yun Wang

    2016-01-01

    Full Text Available Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors. Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1 the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2 network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3 it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

  17. Automatic food detection in egocentric images using artificial intelligence technology

    Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

  18. Comparison of Intelligent Systems in Detecting a Child's Mathematical Gift

    Pavlekovic, Margita; Zekic-Susac, Marijana; Djurdjevic, Ivana

    2009-01-01

    This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children's mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child's mathematical gift…

  19. Delamination detection using methods of computational intelligence

    Ihesiulor, Obinna K.; Shankar, Krishna; Zhang, Zhifang; Ray, Tapabrata

    2012-11-01

    Abstract Reliable delamination prediction scheme is indispensable in order to prevent potential risks of catastrophic failures in composite structures. The existence of delaminations changes the vibration characteristics of composite laminates and hence such indicators can be used to quantify the health characteristics of laminates. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using numerical models of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used via inverse modeling to determine the position, size and location of delaminations using changes in measured natural frequencies. The results clearly highlight the efficiency and the robustness of the approach.

  20. Magnetic biosensor system to detect biological targets

    Li, Fuquan; Gooneratne, Chinthaka Pasan; Kosel, Jü rgen

    2012-01-01

    magnetic concentration, magnetic as well as mechanical trapping and magnetic sensing. Target detection is based on the size difference between bare magnetic beads and magnetic beads with targets attached. This method remedies the need for a coating layer

  1. Intelligent Bar Chart Plagiarism Detection in Documents

    Mohammed Mumtaz Al-Dabbagh

    2014-01-01

    Full Text Available This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR. By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

  2. Intelligent bar chart plagiarism detection in documents.

    Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Rehman, Amjad; Alkawaz, Mohammed Hazim; Saba, Tanzila; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

    2014-01-01

    This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

  3. Intelligent Bar Chart Plagiarism Detection in Documents

    Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Alkawaz, Mohammed Hazim; Saba, Tanzila; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

    2014-01-01

    This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts. PMID:25309952

  4. Intelligent Image Segment for Material Composition Detection

    Liang Xiaodan

    2017-01-01

    Full Text Available In the process of material composition detection, the image analysis is an inevitable problem. Multilevel thresholding based OTSU method is one of the most popular image segmentation techniques. How, with the increase of the number of thresholds, the computing time increases exponentially. To overcome this problem, this paper proposed an artificial bee colony algorithm with a two-level topology. This improved artificial bee colony algorithm can quickly find out the suitable thresholds and nearly no trap into local optimal. The test results confirm it good performance.

  5. Integrated artificial intelligence algorithm for skin detection

    Bush Idoko John

    2018-01-01

    Full Text Available The detection of skin colour has been a useful and renowned technique due to its wide range of application in both analyses based on diagnostic and human computer interactions. Various problems could be solved by simply providing an appropriate method for pixel-like skin parts. Presented in this study is a colour segmentation algorithm that works directly in RGB colour space without converting the colour space. Genfis function as used in this study formed the Sugeno fuzzy network and utilizing Fuzzy C-Mean (FCM clustering rule, clustered the data and for each cluster/class a rule is generated. Finally, corresponding output from data mapping of pseudo-polynomial is obtained from input dataset to the adaptive neuro fuzzy inference system (ANFIS.

  6. Manifold structure preservative for hyperspectral target detection

    Imani, Maryam

    2018-05-01

    A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

  7. Intelligent-based Structural Damage Detection Model

    Lee, Eric Wai Ming; Yu, K.F.

    2010-01-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  8. Intelligent-based Structural Damage Detection Model

    Lee, Eric Wai Ming; Yu, Kin Fung

    2010-05-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  9. Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence

    Pace, Paul W.; Sutherland, John

    2001-10-01

    This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.

  10. Emotionally Intelligent Leadership: An Analysis of Targeted Interventions for Aspiring School Leaders in Texas

    Kearney, W. Sean; Kelsey, Cheryl; Sinkfield, Carolin

    2014-01-01

    This study measures the impact of targeted interventions on the emotional intelligence of aspiring principals. The interventions utilized were designed by Nelson and Low (2011) to increase emotionally intelligent leadership skills in the following six areas: social awareness/active listening; anxiety management; decision making; appropriate use of…

  11. Multimodal Detection of Music Performances for Intelligent Emotion Based Lighting

    Bonde, Esben Oxholm Skjødt; Hansen, Ellen Kathrine; Triantafyllidis, Georgios

    2016-01-01

    Playing music is about conveying emotions and the lighting at a concert can help do that. However, new and unknown bands that play at smaller venues and bands that don’t have the budget to hire a dedicated light technician have to miss out on lighting that will help them to convey the emotions...... of what they play. In this paper it is investigated whether it is possible or not to develop an intelligent system that through a multimodal input detects the intended emotions of the played music and in realtime adjusts the lighting accordingly. A concept for such an intelligent lighting system...... is developed and described. Through existing research on music and emotion, as well as on musicians’ body movements related to the emotion they want to convey, a row of cues is defined. This includes amount, speed, fluency and regularity for the visual and level, tempo, articulation and timbre for the auditory...

  12. Covariance descriptor fusion for target detection

    Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

    2016-05-01

    Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

  13. Intelligent modular star and target tracker: a new generation of attitude sensors

    Schmidt, Uwe; Strobel, Rainer; Wunder, Dietmar; Graf, Eberhart

    2018-04-01

    This paper, "Intelligent modular star and target tracker: a new generation of attitude sensors," was presented as part of International Conference on Space Optics—ICSO 1997, held in Toulouse, France.

  14. On the detectability of intelligent civilizations in the galaxy

    Đurić N.

    2003-01-01

    Full Text Available In this paper we argue for the possibility that even in the event of a Galaxy teeming with extraterrestrial intelligence (ETI the probability of receiving recognizable signals from the ETIs may be very low. There are two majors factors that may limit our ability to detect other civilizations. (i Evolutionary mismatches may cause difficulties analogous to humans attempting to communicate with lower primates. (ii Independent evolutionary paths resulting from differing planetary/stellar environments may result in life whose cognitive processes and consequent perceptions of the universe are very different from ours. Interpreting signals from such civilizations may prove a very difficult or even futile task. Even on Earth, an example of a cognitive mismatch is that between humans and dolphins, where evolution in very different environments has led to difficulty in establishing communication between these two species. The main effect of the second factor is to limit communication while the effect of the first is to constrain what communication is possible to a "window of opportunity", a finite period of time, τω, when communication may be possible before diverging evolution makes it impossible. For example, if the number of ETIs in the Galaxy is one million and if τω 5 × 10 3 light years so that one star in 10 10 harbors such a civilization. If the above arguments are correct we reach the following conclusions. The absence of detected signals does not translate into an absence of ETI’s. Targeting individual stars in the search for ETI has a low probability of success. The use of radio signals is of limited value because with such large separations between "contactable" civilizations interstellar scintillation strongly limits the propagation of radio signals. Similarly, optical communication would be hindered by interstellar extinction. Possible alternatives to current searches for narrow band signals include listening for modulated broadband

  15. Target Detection Using an AOTF Hyperspectral Imager

    Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.

    1994-01-01

    This paper reports results of a recent field experiment using a prototype system to evaluate the acousto-optic tunable filter polarimetric hyperspectral imaging technology for target detection applications.

  16. Dim point target detection against bright background

    Zhang, Yao; Zhang, Qiheng; Xu, Zhiyong; Xu, Junping

    2010-05-01

    For target detection within a large-field cluttered background from a long distance, several difficulties, involving low contrast between target and background, little occupancy, illumination ununiformity caused by vignetting of lens, and system noise, make it a challenging problem. The existing approaches to dim target detection can be roughly divided into two categories: detection before tracking (DBT) and tracking before detection (TBD). The DBT-based scheme has been widely used in practical applications due to its simplicity, but it often requires working in the situation with a higher signal-to-noise ratio (SNR). In contrast, the TBD-based methods can provide impressive detection results even in the cases of very low SNR; unfortunately, the large memory requirement and high computational load prevents these methods from real-time tasks. In this paper, we propose a new method for dim target detection. We address this problem by combining the advantages of the DBT-based scheme in computational efficiency and of the TBD-based in detection capability. Our method first predicts the local background, and then employs the energy accumulation and median filter to remove background clutter. The dim target is finally located by double window filtering together with an improved high order correlation which speeds up the convergence. The proposed method is implemented on a hardware platform and performs suitably in outside experiments.

  17. Defending a single object against an attacker trying to detect a subset of false targets

    Peng, R.; Zhai, Q.Q.; Levitin, G.

    2016-01-01

    Deployment of false targets can be a very important and effective measure for enhancing the survivability of an object subjected to intentional attacks. Existing papers have assumed that false targets are either perfect or can be detected with a constant probability. In practice, the attacker may allocate part of its budget into intelligence actions trying to detect a subset of false targets. Analogously, the defender can allocate part of its budget into disinformation actions to prevent the false targets from being detected. In this paper, the detection probability of each false target is assumed to be a function of the intelligence and disinformation efforts allocated on the false target. The optimal resource distribution between target identification/disinformation and attack/protection efforts is studied as solutions of a non-cooperative two period min–max game between the two competitors for the case of constrained defense and attack resources. - Highlights: • A defense-attack problem is studied as a two-period min–max game. • Both intelligence contest over false targets and impact contest are considered. • Optimal defense and attack strategies are investigated with different parameters.

  18. Automatic target detection using binary template matching

    Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

    2005-03-01

    This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

  19. A comparison of directed search target detection versus in-scene target detection in Worldview-2 datasets

    Grossman, S.

    2015-05-01

    Since the events of September 11, 2001, the intelligence focus has moved from large order-of-battle targets to small targets of opportunity. Additionally, the business community has discovered the use of remotely sensed data to anticipate demand and derive data on their competition. This requires the finer spectral and spatial fidelity now available to recognize those targets. This work hypothesizes that directed searches using calibrated data perform at least as well as inscene manually intensive target detection searches. It uses calibrated Worldview-2 multispectral images with NEF generated signatures and standard detection algorithms to compare bespoke directed search capabilities against ENVI™ in-scene search capabilities. Multiple execution runs are performed at increasing thresholds to generate detection rates. These rates are plotted and statistically analyzed. While individual head-to-head comparison results vary, 88% of the directed searches performed at least as well as in-scene searches with 50% clearly outperforming in-scene methods. The results strongly support the premise that directed searches perform at least as well as comparable in-scene searches.

  20. An Intelligent Strain Gauge with Debond Detection and Temperature Compensation

    Jensen, Scott L.

    2012-01-01

    The harsh rocket propulsion test environment will expose any inadequacies associated with preexisting instrumentation technologies, and the criticality for collecting reliable test data justifies investigating any encountered data anomalies. Novel concepts for improved systems are often conceived during the high scrutiny investigations by individuals with an in-depth knowledge from maintaining critical test operations. The Intelligent Strain Gauge concept was conceived while performing these kinds of activities. However, the novel concepts are often unexplored even if it has the potential for advancing the current state of the art. Maturing these kinds of concepts is often considered to be a tangential development or a research project which are both normally abandoned within the propulsion-oriented environment. It is also difficult to justify these kinds of projects as a facility enhancement because facility developments are only accepted for mature and proven technologies. Fortunately, the CIF program has provided an avenue for bringing the Intelligent Strain Gauge to fruition. Two types of fully functional smart strain gauges capable of performing reliable and sensitive debond detection have been successfully produced. Ordinary gauges are designed to provide test article data and they lack the ability to supply information concerning the gauge itself. A gauge is considered to be a smart gauge when it provides supplementary data relating other relevant attributes for performing diagnostic function or producing enhanced data. The developed strain gauges provide supplementary signals by measuring strain and temperature through embedded Karma and nickel chromium (NiCr) alloy elements. Intelligently interpreting the supplementary data into valuable information can be performed manually, however, integrating this functionality into an automatic system is considered to be an intelligent gauge. This was achieved while maintaining a very low mass. The low mass enables

  1. Detection Accuracy of Collective Intelligence Assessments for Skin Cancer Diagnosis.

    Kurvers, Ralf H J M; Krause, Jens; Argenziano, Giuseppe; Zalaudek, Iris; Wolf, Max

    2015-12-01

    Incidence rates of skin cancer are increasing globally, and the correct classification of skin lesions (SLs) into benign and malignant tissue remains a continuous challenge. A collective intelligence approach to skin cancer detection may improve accuracy. To evaluate the performance of 2 well-known collective intelligence rules (majority rule and quorum rule) that combine the independent conclusions of multiple decision makers into a single decision. Evaluations were obtained from 2 large and independent data sets. The first data set consisted of 40 experienced dermoscopists, each of whom independently evaluated 108 images of SLs during the Consensus Net Meeting of 2000. The second data set consisted of 82 medical professionals with varying degrees of dermatology experience, each of whom evaluated a minimum of 110 SLs. All SLs were evaluated via the Internet. Image selection of SLs was based on high image quality and the presence of histopathologic information. Data were collected from July through October 2000 for study 1 and from February 2003 through January 2004 for study 2 and evaluated from January 5 through August 7, 2015. For both collective intelligence rules, we determined the true-positive rate (ie, the hit rate or specificity) and the false-positive rate (ie, the false-alarm rate or 1 - sensitivity) and compared these rates with the performance of single decision makers. Furthermore, we evaluated the effect of group size on true- and false-positive rates. One hundred twenty-two medical professionals performed 16 029 evaluations. Use of either collective intelligence rule consistently outperformed single decision makers. The groups achieved an increased true-positive rate and a decreased false-positive rate. For example, individual decision makers in study 1, using the pattern analysis as diagnostic algorithm, achieved a true-positive rate of 0.83 and a false-positive rate of 0.17. Groups of 3 individuals achieved a true-positive rate of 0.91 and a

  2. Small target detection using objectness and saliency

    Zhang, Naiwen; Xiao, Yang; Fang, Zhiwen; Yang, Jian; Wang, Li; Li, Tao

    2017-10-01

    We are motived by the need for generic object detection algorithm which achieves high recall for small targets in complex scenes with acceptable computational efficiency. We propose a novel object detection algorithm, which has high localization quality with acceptable computational cost. Firstly, we obtain the objectness map as in BING[1] and use NMS to get the top N points. Then, k-means algorithm is used to cluster them into K classes according to their location. We set the center points of the K classes as seed points. For each seed point, an object potential region is extracted. Finally, a fast salient object detection algorithm[2] is applied to the object potential regions to highlight objectlike pixels, and a series of efficient post-processing operations are proposed to locate the targets. Our method runs at 5 FPS on 1000*1000 images, and significantly outperforms previous methods on small targets in cluttered background.

  3. Magnetic biosensor system to detect biological targets

    Li, Fuquan

    2012-09-01

    Magneto-resistive sensors in combination with magnetic beads provide sensing platforms, which are small in size and highly sensitive. These platforms can be fully integrated with microchannels and electronics to enable devices capable of performing complex tasks. Commonly, a sandwich method is used that requires a specific coating of the sensor\\'s surface to immobilize magnetic beads and biological targets on top of the sensor. This paper concerns a micro device to detect biological targets using magnetic concentration, magnetic as well as mechanical trapping and magnetic sensing. Target detection is based on the size difference between bare magnetic beads and magnetic beads with targets attached. This method remedies the need for a coating layer and reduces the number of steps required to run an experiment. © 2012 IEEE.

  4. Computerized detection of breast cancer with artificial intelligence and thermograms.

    Ng, E Y-K; Fok, S C; Peh, Y C; Ng, F C; Sim, L S J

    2002-01-01

    This paper shows the concurrent use of thermography and artificial neural networks (ANN) for the diagnosis of breast cancer, a disease that is growing in prominence in women all over the world. It has been reported that breast thermography itself could detect breast cancer up to 10 years earlier than the conventional golden methods such as mammography, in particular in the younger patient. However, the accuracy of thermography is dependent on many factors such as the symmetry of the breasts' temperature and temperature stability. A woman's body temperature is known to be stable in certain periods after menstruation and it was found that the accuracy of thermography in women whose thermal images are taken in a suitable period (5th - 12th and 21st day of menstruation) is higher (80%) than the total population of patients (73%). The stability of the body temperature will depend on physiological state. This paper examines the use of ANN to complement the infrared heat radiating from the surface of the body with other physiological data. Four backpropagation neural networks were developed and trained using the results from the Singapore General Hospital patients' physiological data and thermographs. Owing to the inaccuracies found in thermography and the low population size gathered for this project, the networks developed could only accurately diagnose about 61.54% of the breast cancer cases. Nevertheless, the basic neural network framework has been established and it has great potential for future development of an intelligent breast cancer diagnosis system. This would be especially useful to the teenagers and young adults who are unsuitable for mammography at a young age. An intelligent breast thermography-neural network will be able to give an accurate diagnosis of breast cancer and can make a positive impact on breast disease detection.

  5. International law implications of the detection of extraterrestrial intelligent signals

    Kopal, Vladimir

    This paper first considers whether the present law of outer space, as it has been enshrined in five United Nations treaties and other legal documents concerning outer space, provides a satisfactory basis for SETI/CETI activities. In the author's opinion, these activities may serve "the common interest of all mankind in the progress of the exploration and use of outer space for peaceful purposes," as recognized in the 1967 Outer Space Treaty. The use of the radio frequency spectrum for SETI/CETI purposes should be in conformity with the legal principles governing this valuable natural resource, as expressed in the International Telecommunication Convention and related documents, and with allocations of the relevant segments of the spectrum by the competent bodies of the International Telecommunication Union. In the second part the author examines the impact that the detection of extraterrestrial intelligent signals may have on the present body of space law. A possible role for the United Nations in this respect is also explored and a timely interest of the world body in discussing questions relating to this subject is recommended. Consideration of these questions could become a tool helping to concentrate the attention of the world community on problems of common concern and thus to strengthen international cooperation. However, the author believes that a law-making process that would aim at elaborating a special regulation of activities in this field would be premature at this stage. It should be initiated only when the boundary between possibilities and realities is crossed. Finally, the paper outlines some likely transformation in our space law thinking that would be the consequence of the detection of extraterrestrial intelligent signals. Elaboration of the principles and norms to govern relations between the international community of our own planet and other intelligent communities in the universe would add a new dimension to the present body of outer space

  6. Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection.

    Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

    2017-06-12

    Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.

  7. Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection

    Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

    2017-01-01

    Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities. PMID:28604628

  8. Camouflage, detection and identification of moving targets.

    Hall, Joanna R; Cuthill, Innes C; Baddeley, Roland; Shohet, Adam J; Scott-Samuel, Nicholas E

    2013-05-07

    Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation-detection, identification and capture-in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely 'break' camouflage.

  9. Detection, information fusion, and temporal processing for intelligence in recognition

    Casasent, D. [Carnegie Mellon Univ., Pittsburgh, PA (United States)

    1996-12-31

    The use of intelligence in vision recognition uses many different techniques or tools. This presentation discusses several of these techniques for recognition. The recognition process is generally separated into several steps or stages when implemented in hardware, e.g. detection, segmentation and enhancement, and recognition. Several new distortion-invariant filters, biologically-inspired Gabor wavelet filter techniques, and morphological operations that have been found very useful for detection and clutter rejection are discussed. These are all shift-invariant operations that allow multiple object regions of interest in a scene to be located in parallel. We also discuss new algorithm fusion concepts by which the results from different detection algorithms are combined to reduce detection false alarms; these fusion methods utilize hierarchical processing and fuzzy logic concepts. We have found this to be most necessary, since no single detection algorithm is best for all cases. For the final recognition stage, we describe a new method of representing all distorted versions of different classes of objects and determining the object class and pose that most closely matches that of a given input. Besides being efficient in terms of storage and on-line computations required, it overcomes many of the problems that other classifiers have in terms of the required training set size, poor generalization with many hidden layer neurons, etc. It is also attractive in its ability to reject input regions as clutter (non-objects) and to learn new object descriptions. We also discuss its use in processing a temporal sequence of input images of the contents of each local region of interest. We note how this leads to robust results in which estimation efforts in individual frames can be overcome. This seems very practical, since in many scenarios a decision need not be made after only one frame of data, since subsequent frames of data enter immediately in sequence.

  10. Spectral Target Detection using Schroedinger Eigenmaps

    Dorado-Munoz, Leidy P.

    Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance. On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together. A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and

  11. Automatic detection of mycobacterium tuberculosis using artificial intelligence

    Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen

    2018-01-01

    Background Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. Methods We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Results Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. Conclusions TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified. PMID:29707349

  12. Automatic detection of mycobacterium tuberculosis using artificial intelligence.

    Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen; Li, Ting

    2018-03-01

    Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified.

  13. Automatic food detection in egocentric images using artificial intelligence technology.

    Jia, Wenyan; Li, Yuecheng; Qu, Ruowei; Baranowski, Thomas; Burke, Lora E; Zhang, Hong; Bai, Yicheng; Mancino, Juliet M; Xu, Guizhi; Mao, Zhi-Hong; Sun, Mingui

    2018-03-26

    To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

  14. Public health intelligence and the detection of potential pandemics.

    French, Martin; Mykhalovskiy, Eric

    2013-02-01

    This article considers contemporary developments in public health intelligence (PHI), especially their focus on health events of pandemic potential. It argues that the sociological study of PHI can yield important insights for the sociology of pandemics. PHI aims to detect health events as (or even before) they unfold. Whilst its apparatuses envelope traditional public health activities, such as epidemiological surveillance, they increasingly extend to non-traditional public health activities such as data-mining in electronically mediated social networks. With a focus on non-traditional PHI activities, the article first situates the study of PHI in relation to the sociology of public health. It then discusses the conceptualisation and actualisation of pandemics, reflecting on how public health professionals and organisations must equip themselves with diverse allies in order to realise the claims they make about pandemic phenomena. Finally, using the analytic tools of actor-network theory, sites for future empirical research that can contribute to the sociology of pandemics are suggested. © 2012 The Authors. Sociology of Health & Illness © 2012 Foundation for the Sociology of Health & Illness/Blackwell Publishing Ltd.

  15. Assessment of Schrodinger Eigenmaps for target detection

    Dorado Munoz, Leidy P.; Messinger, David W.; Czaja, Wojtek

    2014-06-01

    Non-linear dimensionality reduction methods have been widely applied to hyperspectral imagery due to its structure as the information can be represented in a lower dimension without losing information, and because the non-linear methods preserve the local geometry of the data while the dimension is reduced. One of these methods is Laplacian Eigenmaps (LE), which assumes that the data lies on a low dimensional manifold embedded in a high dimensional space. LE builds a nearest neighbor graph, computes its Laplacian and performs the eigendecomposition of the Laplacian. These eigenfunctions constitute a basis for the lower dimensional space in which the geometry of the manifold is preserved. In addition to the reduction problem, LE has been widely used in tasks such as segmentation, clustering, and classification. In this regard, a new Schrodinger Eigenmaps (SE) method was developed and presented as a semi-supervised classification scheme in order to improve the classification performance and take advantage of the labeled data. SE is an algorithm built upon LE, where the former Laplacian operator is replaced by the Schrodinger operator. The Schrodinger operator includes a potential term V, that, taking advantage of the additional information such as labeled data, allows clustering of similar points. In this paper, we explore the idea of using SE in target detection. In this way, we present a framework where the potential term V is defined as a barrier potential: a diagonal matrix encoding the spatial position of the target, and the detection performance is evaluated by using different targets and different hyperspectral scenes.

  16. An Intelligent Tutor for Intrusion Detection on Computer Systems

    Rowe, Neil C; Schiavo, Sandra

    1998-01-01

    ... critical. We describe a tutor incorporating two programs. The first program uses artificial-intelligence planning methods to generate realistic audit files reporting actions of a variety of simulated users (including intruders...

  17. Intelligence

    Sternberg, Robert J.

    2012-01-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain—especially with regard to the functioning in the prefrontal cortex—and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret. PMID:22577301

  18. Intelligence.

    Sternberg, Robert J

    2012-03-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex-and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret.

  19. Efficient drilling problem detection. Early fault detection by the combination of physical models and artificial intelligence

    Nyboe, Roar

    2009-09-15

    The drilling of an oil or gas well is an expensive undertaking. Hence, it is not surprising that mistakes and accidents during drilling incur a high cost. Accidents could result in the loss of expensive equipment and subsequent delays setting back the operation for days or weeks and thus running up large bills on rig-time and personnel hours. Some types of accidents also pose a risk to the personnel or the environment. In this dissertation we study alarm systems which could give the driller an early warning of upcoming problems, and thus provide time to avoid these accidents. We explore alarm systems which combine advanced physical models of the well and drilling process with artificial intelligence and time series analysis. Finally, we determine the advantages as well as the challenges of this approach. It is our hope that this dissertation is accessible to both practitioners in machine learning and control engineering, as well as to petroleum engineers with a passing familiarity with machine learning. Hence this dissertation starts with a quick introduction to drilling problems and some terms from time series analysis and machine learning. We then briefly describe the theory of observer-based fault detection and isolation. Theories of supervisory control systems are also introduced, as these concern both the choice of algorithms and how AI-based alarm systems integrate with the rest of the operation. From chapter 6 and onward, the challenges to fault detection in drilling are discussed. We focus on clarifying what restrictions the available training data put on our choice of machine learning methods. In chapter 8 and 9, we propose ways to combine machine learning and observer-based fault detection. Experimental results are presented in chapter 10, before we end with concluding remarks in chapter 11. Our main conclusion, reflected in our experimental results, is that physical models and artificial intelligence can be combined to produce hybrid alarm systems that

  20. Detection of Anomalies in Hydrometric Data Using Artificial Intelligence Techniques

    Lauzon, N.; Lence, B. J.

    2002-12-01

    This work focuses on the detection of anomalies in hydrometric data sequences, such as 1) outliers, which are individual data having statistical properties that differ from those of the overall population; 2) shifts, which are sudden changes over time in the statistical properties of the historical records of data; and 3) trends, which are systematic changes over time in the statistical properties. For the purpose of the design and management of water resources systems, it is important to be aware of these anomalies in hydrometric data, for they can induce a bias in the estimation of water quantity and quality parameters. These anomalies may be viewed as specific patterns affecting the data, and therefore pattern recognition techniques can be used for identifying them. However, the number of possible patterns is very large for each type of anomaly and consequently large computing capacities are required to account for all possibilities using the standard statistical techniques, such as cluster analysis. Artificial intelligence techniques, such as the Kohonen neural network and fuzzy c-means, are clustering techniques commonly used for pattern recognition in several areas of engineering and have recently begun to be used for the analysis of natural systems. They require much less computing capacity than the standard statistical techniques, and therefore are well suited for the identification of outliers, shifts and trends in hydrometric data. This work constitutes a preliminary study, using synthetic data representing hydrometric data that can be found in Canada. The analysis of the results obtained shows that the Kohonen neural network and fuzzy c-means are reasonably successful in identifying anomalies. This work also addresses the problem of uncertainties inherent to the calibration procedures that fit the clusters to the possible patterns for both the Kohonen neural network and fuzzy c-means. Indeed, for the same database, different sets of clusters can be

  1. Design of intelligent proximity detection zones to prevent striking and pinning fatalities around continuous mining machines.

    Bissert, P T; Carr, J L; DuCarme, J P; Smith, A K

    2016-01-01

    The continuous mining machine is a key piece of equipment used in underground coal mining operations. Over the past several decades these machines have been involved in a number of mine worker fatalities. Proximity detection systems have been developed to avert hazards associated with operating continuous mining machines. Incorporating intelligent design into proximity detection systems allows workers greater freedom to position themselves to see visual cues or avoid other hazards such as haulage equipment or unsupported roof or ribs. However, intelligent systems must be as safe as conventional proximity detection systems. An evaluation of the 39 fatal accidents for which the Mine Safety and Health Administration has published fatality investigation reports was conducted to determine whether the accident may have been prevented by conventional or intelligent proximity. Multiple zone configurations for the intelligent systems were studied to determine how system performance might be affected by the zone configuration. Researchers found that 32 of the 39 fatalities, or 82 percent, may have been prevented by both conventional and intelligent proximity systems. These results indicate that, by properly configuring the zones of an intelligent proximity detection system, equivalent protection to a conventional system is possible.

  2. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Ganapathy, S.; Yogesh, P.; Kannan, A.

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

  3. Space moving target detection using time domain feature

    Wang, Min; Chen, Jin-yong; Gao, Feng; Zhao, Jin-yu

    2018-01-01

    The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space moving target detection method based on time domain features. We firstly construct the time spectral data of star map, then analyze the time domain features of the main objects (target, stars and the background) in star maps, finally detect the moving targets using single pulse feature of the time domain signal. The real star map target detection experimental results show that the proposed method can effectively detect the trajectory of moving targets in the star map sequence, and the detection probability achieves 99% when the false alarm rate is about 8×10-5, which outperforms those of compared algorithms.

  4. Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

    Arotaritei, D.; Rotariu, C.

    2015-09-01

    In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

  5. An Overview of Radar Waveform Optimization for Target Detection

    Wang Lulu

    2016-10-01

    Full Text Available An optimal waveform design method that fully employs the knowledge of the target and the environment can further improve target detection performance, thus is of vital importance to research. In this paper, methods of radar waveform optimization for target detection are reviewed and summarized and provide the basis for the research.

  6. Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

    Laghari, J.A.; Mokhlis, H.; Karimi, M.; Bakar, A.H.A.; Mohamad, Hasmaini

    2014-01-01

    Highlights: • Unintentional and intentional islanding, their causes, and solutions are presented. • Remote, passive, active and hybrid islanding detection techniques are discussed. • The limitation of these techniques in accurately detect islanding are discussed. • Computational intelligence techniques ability in detecting islanding is discussed. • Review of ANN, fuzzy logic control, ANFIS, Decision tree techniques is provided. - Abstract: Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system

  7. Real time testing of intelligent relays for synchronous distributed generation islanding detection

    Zhuang, Davy

    As electric power systems continue to grow to meet ever-increasing energy demand, their security, reliability, and sustainability requirements also become more stringent. The deployment of distributed energy resources (DER), including generation and storage, in conventional passive distribution feeders, gives rise to integration problems involving protection and unintentional islanding. Distributed generators need to be islanded for safety reasons when disconnected or isolated from the main feeder as distributed generator islanding may create hazards to utility and third-party personnel, and possibly damage the distribution system infrastructure, including the distributed generators. This thesis compares several key performance indicators of a newly developed intelligent islanding detection relay, against islanding detection devices currently used by the industry. The intelligent relay employs multivariable analysis and data mining methods to arrive at decision trees that contain both the protection handles and the settings. A test methodology is developed to assess the performance of these intelligent relays on a real time simulation environment using a generic model based on a real-life distribution feeder. The methodology demonstrates the applicability and potential advantages of the intelligent relay, by running a large number of tests, reflecting a multitude of system operating conditions. The testing indicates that the intelligent relay often outperforms frequency, voltage and rate of change of frequency relays currently used for islanding detection, while respecting the islanding detection time constraints imposed by standing distributed generator interconnection guidelines.

  8. Intelligent Flamefinder Detection and Alert System (IFDAS), Phase II

    National Aeronautics and Space Administration — Current hydrogen flame detection systems exhibit shortcomings ranging from limited detection range, to localization inaccuracy, limited sensitivity, false alarms,...

  9. Camouflage target detection via hyperspectral imaging plus information divergence measurement

    Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

    2016-01-01

    Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.

  10. An intelligent detection method for high-field asymmetric waveform ion mobility spectrometry.

    Li, Yue; Yu, Jianwen; Ruan, Zhiming; Chen, Chilai; Chen, Ran; Wang, Han; Liu, Youjiang; Wang, Xiaozhi; Li, Shan

    2018-04-01

    In conventional high-field asymmetric waveform ion mobility spectrometry signal acquisition, multi-cycle detection is time consuming and limits somewhat the technique's scope for rapid field detection. In this study, a novel intelligent detection approach has been developed in which a threshold was set on the relative error of α parameters, which can eliminate unnecessary time spent on detection. In this method, two full-spectrum scans were made in advance to obtain the estimated compensation voltage at different dispersion voltages, resulting in a narrowing down of the whole scan area to just the peak area(s) of interest. This intelligent detection method can reduce the detection time to 5-10% of that of the original full-spectrum scan in a single cycle.

  11. Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods

    Smetek, Timothy E

    2007-01-01

    This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors...

  12. Detection technique of targets for missile defense system

    Guo, Hua-ling; Deng, Jia-hao; Cai, Ke-rong

    2009-11-01

    Ballistic missile defense system (BMDS) is a weapon system for intercepting enemy ballistic missiles. It includes ballistic-missile warning system, target discrimination system, anti-ballistic-missile guidance systems, and command-control communication system. Infrared imaging detection and laser imaging detection are widely used in BMDS for surveillance, target detection, target tracking, and target discrimination. Based on a comprehensive review of the application of target-detection techniques in the missile defense system, including infrared focal plane arrays (IRFPA), ground-based radar detection technology, 3-dimensional imaging laser radar with a photon counting avalanche photodiode (APD) arrays and microchip laser, this paper focuses on the infrared and laser imaging detection techniques in missile defense system, as well as the trends for their future development.

  13. Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.

    Ali, Abdulbaset; Hu, Bing; Ramahi, Omar

    2015-05-15

    This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.

  14. Small Surface Target Detection with EO/IR Sensors

    Jong, A.N. de; Kemp, R.A.W.

    1998-01-01

    The detection of small surface targets at sea is an increasing requirement for warships. The present sensors on board do not provide the required detection probabilities for these low observable targets like small rubber boats, floating mines, periscopes, people etc. The reason for the low

  15. Quantum Illumination-Based Target Detection and Discrimination

    2014-06-30

    photodiode with an estimated quantum efficiency of 85% and an ultralow-noise transimpedance amplifier . Compared with to our initial QI measurements...demonstrated high signal-to-noise ratio (SNR) quantum-illumination target detection in a lossy, noisy environment using an optical parametric amplifier ...Research Triangle Park, NC 27709-2211 quantum communication, target detection, entanglement, parametric downconversion, optical parametric amplifiers

  16. Dim target detection method based on salient graph fusion

    Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

    2018-02-01

    Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

  17. Small target pre-detection with an attention mechanism

    Wang, Yuehuan; Zhang, Tianxu; Wang, Guoyou

    2002-04-01

    We introduce the concept of predetection based on an attention mechanism to improve the efficiency of small-target detection by limiting the image region of detection. According to the characteristics of small-target detection, local contrast is taken as the only feature in predetection and a nonlinear sampling model is adopted to make the predetection adaptive to detect small targets with different area sizes. To simplify the predetection itself and decrease the false alarm probability, neighboring nodes in the sampling grid are used to generate a saliency map, and a short-term memory is adopted to accelerate the `pop-out' of targets. We discuss the fact that the proposed approach is simple enough in computational complexity. In addition, even in a cluttered background, attention can be led to targets in a satisfying few iterations, which ensures that the detection efficiency will not be decreased due to false alarms. Experimental results are presented to demonstrate the applicability of the approach.

  18. Detection of dim targets in multiple environments

    Mirsky, Grace M.; Woods, Matthew; Grasso, Robert J.

    2013-10-01

    The proliferation of a wide variety of weapons including Anti-Aircraft Artillery (AAA), rockets, and small arms presents a substantial threat to both military and civilian aircraft. To address this ever-present threat, Northrop Grumman has assessed unguided threat phenomenology to understand the underlying physical principles for detection. These principles, based upon threat transit through the atmosphere, exploit a simple phenomenon universal to all objects moving through an atmosphere comprised of gaseous media to detect and track the threat in the presence of background and clutter. Threat detection has rapidly become a crucial component of aircraft survivability systems that provide situational awareness to the crew. It is particularly important to platforms which may spend a majority of their time at low altitudes and within the effective range of a large variety of weapons. Detection of these threats presents a unique challenge as this class of threat typically has a dim signature coupled with a short duration. Correct identification of each of the threat components (muzzle flash and projectile) is important to determine trajectory and intent while minimizing false alarms and maintaining a high detection probability in all environments.

  19. Sensor fusion: lane marking detection and autonomous intelligent cruise control system

    Baret, Marc; Baillarin, S.; Calesse, C.; Martin, Lionel

    1995-12-01

    In the past few years MATRA and RENAULT have developed an Autonomous Intelligent Cruise Control (AICC) system based on a LIDAR sensor. This sensor incorporating a charge coupled device was designed to acquire pulsed laser diode emission reflected by standard car reflectors. The absence of moving mechanical parts, the large field of view, the high measurement rate and the very good accuracy for distance range and angular position of targets make this sensor very interesting. It provides the equipped car with the distance and the relative speed of other vehicles enabling the safety distance to be controlled by acting on the throttle and the automatic gear box. Experiments in various real traffic situations have shown the limitations of this kind of system especially on bends. All AICC sensors are unable to distinguish between a bend and a change of lane. This is easily understood if we consider a road without lane markings. This fact has led MATRA to improve its AICC system by providing the lane marking information. Also in the scope of the EUREKA PROMETHEUS project, MATRA and RENAULT have developed a lane keeping system in order to warn of the drivers lack of vigilance. Thus, MATRA have spread this system to far field lane marking detection and have coupled it with the AICC system. Experiments will be carried out on roads to estimate the gain in performance and comfort due to this fusion.

  20. Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants

    Reifman, J.

    1997-01-01

    A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented. Classification criteria are established that categorize artificial intelligence diagnostic systems according to the types of computing approaches used (e.g., computing tools, computer languages, and shell and simulation programs), the types of methodologies employed (e.g., types of knowledge, reasoning and inference mechanisms, and diagnostic approach), and the scope of the system. The major issues of process diagnostics and computer-based diagnostic systems are identified and cross-correlated with the various categories used for classification. Ninety-five publications are reviewed

  1. Target Detection Based on EBPSK Satellite Passive Radar

    Lu Zeyuan

    2015-05-01

    Full Text Available Passive radar is a topic anti stealth technology with simple structure, and low cost. Radiation source model, signal transmission model, and target detection are the key points of passive radar technology research. The paper analyzes the characteristics of EBPSK signal modulation and target detection method aspect of spaceborne radiant source. By comparison with other satellite navigation and positioning system, the characteristics of EBPSK satellite passive radar system are analyzed. It is proved that the maximum detection range of EBPSK satellite signal can satisfy the needs of the proposed model. In the passive radar model, sparse representation is used to achieve high resolution DOA detection. The comparison with the real target track by simulation demonstrates that effective detection of airborne target using EBPSK satellite passive radar system based on sparse representation is efficient.

  2. Detection and identification of human targets in radar data

    Gürbüz, Sevgi Z.; Melvin, William L.; Williams, Douglas B.

    2007-04-01

    Radar offers unique advantages over other sensors, such as visual or seismic sensors, for human target detection. Many situations, especially military applications, prevent the placement of video cameras or implantment seismic sensors in the area being observed, because of security or other threats. However, radar can operate far away from potential targets, and functions during daytime as well as nighttime, in virtually all weather conditions. In this paper, we examine the problem of human target detection and identification using single-channel, airborne, synthetic aperture radar (SAR). Human targets are differentiated from other detected slow-moving targets by analyzing the spectrogram of each potential target. Human spectrograms are unique, and can be used not just to identify targets as human, but also to determine features about the human target being observed, such as size, gender, action, and speed. A 12-point human model, together with kinematic equations of motion for each body part, is used to calculate the expected target return and spectrogram. A MATLAB simulation environment is developed including ground clutter, human and non-human targets for the testing of spectrogram-based detection and identification algorithms. Simulations show that spectrograms have some ability to detect and identify human targets in low noise. An example gender discrimination system correctly detected 83.97% of males and 91.11% of females. The problems and limitations of spectrogram-based methods in high clutter environments are discussed. The SNR loss inherent to spectrogram-based methods is quantified. An alternate detection and identification method that will be used as a basis for future work is proposed.

  3. Photonics: From target recognition to lesion detection

    Henry, E. Michael

    1994-01-01

    Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

  4. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

    Hamed Komari Alaie

    2018-01-01

    Full Text Available This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method.

  5. Eye Detection and Tracking for Intelligent Human Computer Interaction

    Yin, Lijun

    2006-01-01

    .... In this project, Dr. Lijun Yin has developed a new algorithm for detecting and tracking eyes under an unconstrained environment using a single ordinary camera or webcam. The new algorithm is advantageous in that it works in a non-intrusive way based on a socalled Topographic Context approach.

  6. Application of artificial intelligence for detecting derived viruses

    Asiru, OF

    2017-06-01

    Full Text Available they infect files and systems is still the same. Hence, such viruses cannot be argued to be new. In this paper, the authors refer to such viruses as derived viruses. Just like new viruses, derived viruses are hard to detect with current scanning...

  7. Detection of Moving Targets Using Soliton Resonance Effect

    Kulikov, Igor K.; Zak, Michail

    2013-01-01

    The objective of this research was to develop a fundamentally new method for detecting hidden moving targets within noisy and cluttered data-streams using a novel "soliton resonance" effect in nonlinear dynamical systems. The technique uses an inhomogeneous Korteweg de Vries (KdV) equation containing moving-target information. Solution of the KdV equation will describe a soliton propagating with the same kinematic characteristics as the target. The approach uses the time-dependent data stream obtained with a sensor in form of the "forcing function," which is incorporated in an inhomogeneous KdV equation. When a hidden moving target (which in many ways resembles a soliton) encounters the natural "probe" soliton solution of the KdV equation, a strong resonance phenomenon results that makes the location and motion of the target apparent. Soliton resonance method will amplify the moving target signal, suppressing the noise. The method will be a very effective tool for locating and identifying diverse, highly dynamic targets with ill-defined characteristics in a noisy environment. The soliton resonance method for the detection of moving targets was developed in one and two dimensions. Computer simulations proved that the method could be used for detection of singe point-like targets moving with constant velocities and accelerations in 1D and along straight lines or curved trajectories in 2D. The method also allows estimation of the kinematic characteristics of moving targets, and reconstruction of target trajectories in 2D. The method could be very effective for target detection in the presence of clutter and for the case of target obscurations.

  8. Infrared small target detection with kernel Fukunaga Koontz transform

    Liu, Rui-ming; Liu, Er-qi; Yang, Jie; Zhang, Tian-hao; Wang, Fang-lin

    2007-09-01

    The Fukunaga-Koontz transform (FKT) has been proposed for many years. It can be used to solve two-pattern classification problems successfully. However, there are few researchers who have definitely extended FKT to kernel FKT (KFKT). In this paper, we first complete this task. Then a method based on KFKT is developed to detect infrared small targets. KFKT is a supervised learning algorithm. How to construct training sets is very important. For automatically detecting targets, the synthetic target images and real background images are used to train KFKT. Because KFKT can represent the higher order statistical properties of images, we expect better detection performance of KFKT than that of FKT. The well-devised experiments verify that KFKT outperforms FKT in detecting infrared small targets.

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

    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.

  10. Texture orientation-based algorithm for detecting infrared maritime targets.

    Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai

    2015-05-20

    Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.

  11. Detection of range migrating targets in compound-Gaussian clutter

    Petrov, N.; le Chevalier, F.; Yarovyi, O.

    2018-01-01

    This paper deals with the problem of coherent radar detection of fast moving targets in a high range resolution mode. In particular, we are focusing on the spiky clutter modeled as a compound Gaussian process with rapidly varying power along range. Additionally, a fast moving target of interest has

  12. Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

    Chen, Shuoyang; Xu, Tingfa; Li, Daqun; Zhang, Jizhou; Jiang, Shenwang

    2016-01-01

    During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder. PMID:27775671

  13. Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

    Shuoyang Chen

    2016-10-01

    Full Text Available During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor and FPGA (Field Programmable Gate Array platforms and the high-precision intelligent holder.

  14. Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

    Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

    2013-01-01

    Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

  15. The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

    Glauner, Patrick; Meira, Jorge Augusto; Valtchev, Petko; State, Radu; Bettinger, Franck

    2016-01-01

    Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview ...

  16. Project Cyclops: a Design Study of a System for Detecting Extraterrestrial Intelligent Life

    1972-01-01

    The requirements in hardware, manpower, time and funding to conduct a realistic effort aimed at detecting the existence of extraterrestrial intelligent life are examined. The methods used are limited to present or near term future state-of-the-art techniques. Subjects discussed include: (1) possible methods of contact, (2) communication by electromagnetic waves, (3) antenna array and system facilities, (4) antenna elements, (5) signal processing, (6) search strategy, and (7) radio and radar astronomy.

  17. Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

    Veldin A. Talorete Jr.; Sherwin A Guirnaldo

    2017-01-01

    This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifie...

  18. Novel Rock Detection Intelligence for Space Exploration Based on Non-Symbolic Algorithms and Concepts

    Yildirim, Sule; Beachell, Ronald L.; Veflingstad, Henning

    2007-01-01

    Future space exploration can utilize artificial intelligence as an integral part of next generation space rover technology to make the rovers more autonomous in performing mission objectives. The main advantage of the increased autonomy through a higher degree of intelligence is that it allows for greater utilization of rover resources by reducing the frequency of time consuming communications between rover and earth. In this paper, we propose a space exploration application of our research on a non-symbolic algorithm and concepts model. This model is based on one of the most recent approaches of cognitive science and artificial intelligence research, a parallel distributed processing approach. We use the Mars rovers. Sprit and Opportunity, as a starting point for proposing what rovers in the future could do if the presented model of non-symbolic algorithms and concepts is embedded in a future space rover. The chosen space exploration application for this paper, novel rock detection, is only one of many potential space exploration applications which can be optimized (through reduction of the frequency of rover-earth communications. collection and transmission of only data that is distinctive/novel) through the use of artificial intelligence technology compared to existing approaches.

  19. Artificial intelligence and ultrasonic tests in detection of defects

    Barrera Cardiel, G.; Fabian Alvarez, M. a.; Velez Martinez, M.; Villasenor, L.

    2001-01-01

    One of the most serious problems in the quality control of welded unions is the location, identification and classification of defects. As a solution to this problem, a technique for classification, applicable to welded unions done by electric arc welding as well as by friction, is proposed; it is based on ultrasonic signals. The neuronal networks proposed are Kohonen and Multilayer Percept ron, all in a virtual instrument environment. Currently the techniques most used in this field are: radiological analysis (X-rays) and ultrasonic analysis (ultrasonic waves). The X-ray technique in addition to being dangerous requires highly specialized personnel and equipment, therefore its use is restricted. The ultrasonic technique, in spite of being one of the most used for detection of discontinuities, requires personnel with wide experience in the interpretation of ultrasonic signals, this is a time-consuming process which necessarily increases its operation cost. The classification techniques that we propose turn out to be safe, reliable, inexpensive and easy to implement for the solution of this important problem. (Author) 8 refs

  20. Infrared small target detection technology based on OpenCV

    Liu, Lei; Huang, Zhijian

    2013-09-01

    Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

  1. Intelligently targeted drug delivery and enhanced antitumor effect by gelatinase-responsive nanoparticles.

    Rutian Li

    Full Text Available AIMS: The matrix metalloproteinase (MMP 2/9, also known as collagenases IV and gelatinases A/B, play a key role in cancer invasion and metastasis. However, the clinical trials of the MMP inhibitors (MMPIs ended up with disappointing results. In this paper, we synthesized a gelatinase-responsive copolymer (mPEG-PCL by inserting a gelatinase cleavable peptide (PVGLIG between mPEG and PCL blocks of mPEG-PCL for anticancer drug delivery to make use of MMP2/9 as an intelligent target for drug delivery. MATERIALS AND METHODS: mPEG-pep-PCL copolymer was synthesized via ring-opening copolymerization and double-amidation. To evaluate whether Nanoparticles (NPs prepared from this copolymer are superior to NPs prepared from mPEG-PCL, NPs prepared from mPEG-PCL copolymer were used as positive control. Docetaxel-loading NPs using mPEG-pep-PCL and mPEG-PCL were prepared by nano-precipitation method, mentioned as Gel-NPs and Con-NPs, respectively. The morphologic changes of the NPs after treatment with gelatinases were observed macroscopically by spectrophotometer and microscopically by transmission electron microscopy (TEM and atomic force microscopy (AFM. The cellular uptake amount and cytotoxicity of Gel-NPs and Con-NPs, respectively, in cell lines with different levels of gelatinase expression were studied. Moreover, the cytotoxicity study on the primary cancer cells isolated from pericardial fluids from a patient with late-stage lung cancer was conducted. RESULTS: The Gel-NPs aggregated in response to gelatinases, which was confirmed macroscopically and microscopically. The cellular uptake amount of Gel-NPs was correlated with the level of gelatinases. The in vitro antitumor effect of Gel-NPs was also correlated with the level of gelatinases and was superior to Taxotere (commercially available docetaxel as well as the Con-NPs. The cytotoxicity study on the primary lung cancer cells also confirmed the effectiveness of Gel-NPs. CONCLUSION: The results in

  2. Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme

    Dairi, Abdelkader; Harrou, Fouzi; Sun, Ying; Senouci, Mohamed

    2018-01-01

    Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

  3. Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme

    Dairi, Abdelkader

    2018-04-30

    Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

  4. Directional detection of dark matter with two-dimensional targets

    Hochberg, Yonit; Kahn, Yonatan; Lisanti, Mariangela; Tully, Christopher G.; Zurek, Kathryn M.

    2017-09-01

    We propose two-dimensional materials as targets for direct detection of dark matter. Using graphene as an example, we focus on the case where dark matter scattering deposits sufficient energy on a valence-band electron to eject it from the target. We show that the sensitivity of graphene to dark matter of MeV to GeV mass can be comparable, for similar exposure and background levels, to that of semiconductor targets such as silicon and germanium. Moreover, a two-dimensional target is an excellent directional detector, as the ejected electron retains information about the angular dependence of the incident dark matter particle. This proposal can be implemented by the PTOLEMY experiment, presenting for the first time an opportunity for directional detection of sub-GeV dark matter.

  5. Neuromorphic Modeling of Moving Target Detection in Insects

    2007-12-31

    Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39, 18 Grants FA9550-04-1-0283 and FA9550-04-1-0294 Neuromorphic Modeling of Moving Target Detection...natural for neuromorphic sensory processing. We developed visual motion detection circuitry, including photodetectors, early vision, and models for both...Lincoln Labs 3DM2 run, Tanner Research reserved and utilized space corresponding to two MOSIS ’tiny chips ’ (2mm square each), each with three interconnected

  6. Intelligent Security IT System for Detecting Intruders Based on Received Signal Strength Indicators

    Yunsick Sung

    2016-10-01

    Full Text Available Given that entropy-based IT technology has been applied in homes, office buildings and elsewhere for IT security systems, diverse kinds of intelligent services are currently provided. In particular, IT security systems have become more robust and varied. However, access control systems still depend on tags held by building entrants. Since tags can be obtained by intruders, an approach to counter the disadvantages of tags is required. For example, it is possible to track the movement of tags in intelligent buildings in order to detect intruders. Therefore, each tag owner can be judged by analyzing the movements of their tags. This paper proposes a security approach based on the received signal strength indicators (RSSIs of beacon-based tags to detect intruders. The normal RSSI patterns of moving entrants are obtained and analyzed. Intruders can be detected when abnormal RSSIs are measured in comparison to normal RSSI patterns. In the experiments, one normal and one abnormal scenario are defined for collecting the RSSIs of a Bluetooth-based beacon in order to validate the proposed method. When the RSSIs of both scenarios are compared to pre-collected RSSIs, the RSSIs of the abnormal scenario are about 61% more different compared to the RSSIs of the normal scenario. Therefore, intruders in buildings can be detected by considering RSSI differences.

  7. Detection of target phonemes in spontaneous and read speech.

    Mehta, G; Cutler, A

    1988-01-01

    Although spontaneous speech occurs more frequently in most listeners' experience than read speech, laboratory studies of human speech recognition typically use carefully controlled materials read from a script. The phonological and prosodic characteristics of spontaneous and read speech differ considerably, however, which suggests that laboratory results may not generalise to the recognition of spontaneous speech. In the present study listeners were presented with both spontaneous and read speech materials, and their response time to detect word-initial target phonemes was measured. Responses were, overall, equally fast in each speech mode. However, analysis of effects previously reported in phoneme detection studies revealed significant differences between speech modes. In read speech but not in spontaneous speech, later targets were detected more rapidly than targets preceded by short words. In contrast, in spontaneous speech but not in read speech, targets were detected more rapidly in accented than in unaccented words and in strong than in weak syllables. An explanation for this pattern is offered in terms of characteristic prosodic differences between spontaneous and read speech. The results support claims from previous work that listeners pay great attention to prosodic information in the process of recognising speech.

  8. Performance evaluation of sea surface simulation methods for target detection

    Xia, Renjie; Wu, Xin; Yang, Chen; Han, Yiping; Zhang, Jianqi

    2017-11-01

    With the fast development of sea surface target detection by optoelectronic sensors, machine learning has been adopted to improve the detection performance. Many features can be learned from training images by machines automatically. However, field images of sea surface target are not sufficient as training data. 3D scene simulation is a promising method to address this problem. For ocean scene simulation, sea surface height field generation is the key point to achieve high fidelity. In this paper, two spectra-based height field generation methods are evaluated. Comparison between the linear superposition and linear filter method is made quantitatively with a statistical model. 3D ocean scene simulating results show the different features between the methods, which can give reference for synthesizing sea surface target images with different ocean conditions.

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

    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.

  10. Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques

    Ferreira, F.J.O. [Instituto de Engenharia Nuclear, Cidade Universitaria, Rio de Janeiro, CEP 21945-970, Caixa Postal 68550 (Brazil)], E-mail: fferreira@ien.gov.br; Crispim, V.R.; Silva, A.X. [DNC/Poli, PEN COPPE CT, UFRJ Universidade Federal do Rio de Janeiro, CEP 21941-972, Caixa Postal 68509, Rio de Janeiro (Brazil)

    2010-06-15

    In this study the development of a methodology to detect illicit drugs and plastic explosives is described with the objective of being applied in the realm of public security. For this end, non-destructive assay with neutrons was used and the technique applied was the real time neutron radiography together with computerized tomography. The system is endowed with automatic responses based upon the application of an artificial intelligence technique. In previous tests using real samples, the system proved capable of identifying 97% of the inspected materials.

  11. Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques

    Ferreira, F.J.O.; Crispim, V.R.; Silva, A.X.

    2010-01-01

    In this study the development of a methodology to detect illicit drugs and plastic explosives is described with the objective of being applied in the realm of public security. For this end, non-destructive assay with neutrons was used and the technique applied was the real time neutron radiography together with computerized tomography. The system is endowed with automatic responses based upon the application of an artificial intelligence technique. In previous tests using real samples, the system proved capable of identifying 97% of the inspected materials.

  12. Design and research of intelligent mobile robot environment detection system based on multi-sensor technology

    Chen Yu; Wen Xinling

    2007-01-01

    The intelligent mobile robot environment detection system is researched based on SCM MC68HC908GP3 as core of control system. The four groups of detection systems constituted by ultrasonic sensors and infrared sensors gather information of forward, behind, left and right different directions, solve the problem of blind spot, and make up each other shortage. The distance measurement precision is improved rapidly and the detection precision is less than ±1% through using the way of the pulse shooting, the signal chooses circuit, and the temperature compensation. The system design method and the hardware circuit are introduced in detail. Simultaneity, the system adopts the single chip control technology, it makes the system possess favorable expansibility and gains the practicability in engineering field. (authors)

  13. Controlled and targeted release of antigens by intelligent shell for improving applicability of oral vaccines.

    Zhang, Lei; Zeng, Zhanzhuang; Hu, Chaohua; Bellis, Susan L; Yang, Wendi; Su, Yintao; Zhang, Xinyan; Wu, Yunkun

    2016-01-01

    Conventional oral vaccines with simple architecture face barriers with regard to stimulating effective immunity. Here we describe oral vaccines with an intelligent phase-transitional shielding layer, poly[(methyl methacrylate)-co-(methyl acrylate)-co-(methacrylic acid)]-poly(D,L-lactide-co-glycolide) (PMMMA-PLGA), which can protect antigens in the gastro-intestinal tract and achieve targeted vaccination in the large intestine. With the surface immunogenic protein (SIP) from group B Streptococcus (GBS) entrapped as the antigen, oral administration with PMMMA-PLGA (PTRBL)/Trx-SIP nanoparticles stimulated robust immunity in tilapia, an animal with a relatively simple immune system. The vaccine succeeded in protecting against Streptococcus agalactiae, a pathogen of worldwide importance that threatens human health and is transmitted in water with infected fish. After oral vaccination with PTRBL/Trx-SIP, tilapia produced enhanced levels of SIP specific antibodies and displayed durability of immune protection. 100% of the vaccinated tilapia were protected from GBS infection, whereas the control groups without vaccines or vaccinated with Trx-SIP only exhibited respective infection rates of 100% or >60% within the initial 5 months after primary vaccination. Experiments in vivo demonstrated that the recombinant antigen Trx-SIP labeled with FITC was localized in colon, spleen and kidney, which are critical sites for mounting an immune response. Our results revealed that, rather than the size of the nanoparticles, it is more likely that the negative charge repulsion produced by ionization of the carboxyl groups in PMMMA shielded the nanoparticles from uptake by small intestinal epithelial cells. This system resolves challenges arising from gastrointestinal damage to antigens, and more importantly, offers a new approach applicable for oral vaccination. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Improved Ordinary Measure and Image Entropy Theory based intelligent Copy Detection Method

    Dengpan Ye

    2011-10-01

    Full Text Available Nowadays, more and more multimedia websites appear in social network. It brings some security problems, such as privacy, piracy, disclosure of sensitive contents and so on. Aiming at copyright protection, the copy detection technology of multimedia contents becomes a hot topic. In our previous work, a new computer-based copyright control system used to detect the media has been proposed. Based on this system, this paper proposes an improved media feature matching measure and an entropy based copy detection method. The Levenshtein Distance was used to enhance the matching degree when using for feature matching measure in copy detection. For entropy based copy detection, we make a fusion of the two features of entropy matrix of the entropy feature we extracted. Firstly,we extract the entropy matrix of the image and normalize it. Then, we make a fusion of the eigenvalue feature and the transfer matrix feature of the entropy matrix. The fused features will be used for image copy detection. The experiments show that compared to use these two kinds of features for image detection singly, using feature fusion matching method is apparent robustness and effectiveness. The fused feature has a high detection for copy images which have been received some attacks such as noise, compression, zoom, rotation and so on. Comparing with referred methods, the method proposed is more intelligent and can be achieved good performance.

  15. Emotional Intelligence and Mismatching Expressive and Verbal Messages: A Contribution to Detection of Deception

    Wojciechowski, Jerzy; Stolarski, Maciej; Matthews, Gerald

    2014-01-01

    Processing facial emotion, especially mismatches between facial and verbal messages, is believed to be important in the detection of deception. For example, emotional leakage may accompany lying. Individuals with superior emotion perception abilities may then be more adept in detecting deception by identifying mismatch between facial and verbal messages. Two personal factors that may predict such abilities are female gender and high emotional intelligence (EI). However, evidence on the role of gender and EI in detection of deception is mixed. A key issue is that the facial processing skills required to detect deception may not be the same as those required to identify facial emotion. To test this possibility, we developed a novel facial processing task, the FDT (Face Decoding Test) that requires detection of inconsistencies between facial and verbal cues to emotion. We hypothesized that gender and ability EI would be related to performance when cues were inconsistent. We also hypothesized that gender effects would be mediated by EI, because women tend to score as more emotionally intelligent on ability tests. Data were collected from 210 participants. Analyses of the FDT suggested that EI was correlated with superior face decoding in all conditions. We also confirmed the expected gender difference, the superiority of high EI individuals, and the mediation hypothesis. Also, EI was more strongly associated with facial decoding performance in women than in men, implying there may be gender differences in strategies for processing affective cues. It is concluded that integration of emotional and cognitive cues may be a core attribute of EI that contributes to the detection of deception. PMID:24658500

  16. Emotional intelligence and mismatching expressive and verbal messages: a contribution to detection of deception.

    Jerzy Wojciechowski

    Full Text Available Processing facial emotion, especially mismatches between facial and verbal messages, is believed to be important in the detection of deception. For example, emotional leakage may accompany lying. Individuals with superior emotion perception abilities may then be more adept in detecting deception by identifying mismatch between facial and verbal messages. Two personal factors that may predict such abilities are female gender and high emotional intelligence (EI. However, evidence on the role of gender and EI in detection of deception is mixed. A key issue is that the facial processing skills required to detect deception may not be the same as those required to identify facial emotion. To test this possibility, we developed a novel facial processing task, the FDT (Face Decoding Test that requires detection of inconsistencies between facial and verbal cues to emotion. We hypothesized that gender and ability EI would be related to performance when cues were inconsistent. We also hypothesized that gender effects would be mediated by EI, because women tend to score as more emotionally intelligent on ability tests. Data were collected from 210 participants. Analyses of the FDT suggested that EI was correlated with superior face decoding in all conditions. We also confirmed the expected gender difference, the superiority of high EI individuals, and the mediation hypothesis. Also, EI was more strongly associated with facial decoding performance in women than in men, implying there may be gender differences in strategies for processing affective cues. It is concluded that integration of emotional and cognitive cues may be a core attribute of EI that contributes to the detection of deception.

  17. Intelligent mechatronics; Intelligent mechatronics

    Hashimoto, H. [The University of Tokyo, Tokyo (Japan). Institute of Industrial Science

    1995-10-01

    Intelligent mechatronics (IM) was explained as follows: a study of IM essentially targets realization of a robot namely, but in the present stage the target is a creation of new values by intellectualization of machine, that is, a combination of the information infrastructure and the intelligent machine system. IM is also thought to be constituted of computers positively used and micromechatronics. The paper next introduces examples of IM study, mainly those the author is concerned with as shown below: sensor gloves, robot hands, robot eyes, tele operation, three-dimensional object recognition, mobile robot, magnetic bearing, construction of remote controlled unmanned dam, robot network, sensitivity communication using neuro baby, etc. 27 figs.

  18. A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks

    Mohammad Nurul Afsar Shaon

    2017-05-01

    Full Text Available A wormhole attack is one of the most critical and challenging security threats for wireless sensor networks because of its nature and ability to perform concealed malicious activities. This paper proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN. Most wormhole detection schemes reported in the literature assume the sensors are uniformly distributed in a network, and, furthermore, they use statistical and topological information and special hardware for their detection. However, these schemes may perform poorly in non-uniformly distributed networks, and, moreover, they may fail to defend against “out of band” and “in band” wormhole attacks. The aim of the proposed research is to develop a detection scheme that is able to detect all kinds of wormhole attacks in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the malicious nodes can be identified by the proposed ANN based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed algorithm is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR based detection models. The simulation results show that proposed ANN based algorithm outperforms the SVM or LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.

  19. Detectability counts when assessing populations for biodiversity targets.

    Silviu O Petrovan

    Full Text Available Efficient, practical and accurate estimates of population parameters are a necessary basis for effective conservation action to meet biodiversity targets. The brown hare is representative of many European farmland species: historically widespread and abundant but having undergone rapid declines as a result of agricultural intensification. As a priority species in the UK Biodiversity Action Plan, it has national targets for population increase that are part of wider national environmental indicators. Previous research has indicated that brown hare declines have been greatest in pastural landscapes and that gains might be made by focussing conservation effort there. We therefore used hares in pastural landscapes to examine how basic changes in survey methodology can affect the precision of population density estimates and related these to national targets for biodiversity conservation in the UK. Line transects for hares carried out at night resulted in higher numbers of detections, had better-fitting detection functions and provided more robust density estimates with lower effort than those during the day, due primarily to the increased probability of detection of hares at night and the nature of hare responses to the observer. Hare spring densities varied widely within a single region, with a pooled mean of 20.6 hares km(-2, significantly higher than the reported national average of hares in pastures of 3.3 hares km(-2. The high number of encounters allowed us to resolve hare densities at site, season and year scales. We demonstrate how survey conduct can impact on data quantity and quality with implications for setting and monitoring biodiversity targets. Our case study of the brown hare provides evidence that for wildlife species with low detectability, large scale volunteer-based monitoring programmes, either species specific or generalist, might be more successfully and efficiently carried out by a small number of trained personnel able to

  20. Hierarchical effects on target detection and conflict monitoring

    Cao, Bihua; Gao, Feng; Ren, Maofang; Li, Fuhong

    2016-01-01

    Previous neuroimaging studies have demonstrated a hierarchical functional structure of the frontal cortices of the human brain, but the temporal course and the electrophysiological signature of the hierarchical representation remains unaddressed. In the present study, twenty-one volunteers were asked to perform a nested cue-target task, while their scalp potentials were recorded. The results showed that: (1) in comparison with the lower-level hierarchical targets, the higher-level targets elicited a larger N2 component (220–350 ms) at the frontal sites, and a smaller P3 component (350–500 ms) across the frontal and parietal sites; (2) conflict-related negativity (non-target minus target) was greater for the lower-level hierarchy than the higher-level, reflecting a more intensive process of conflict monitoring at the final step of target detection. These results imply that decision making, context updating, and conflict monitoring differ among different hierarchical levels of abstraction. PMID:27561989

  1. Intelligence and impact contests in systems with redundancy, false targets, and partial protection

    Levitin, Gregory; Hausken, Kjell

    2009-01-01

    The paper considers a system consisting of identical elements that can be intentionally attacked. The cumulative performance of the system elements should meet a demand. To prevent loss of demand the defender provides system redundancy (deploying genuine system elements (GEs) with cumulative performance exceeding the demand); deploys false elements (FEs), and protects the GEs. If the attacker cannot distinguish GEs and FEs, he chooses the number of elements to attack and attacks at random these elements distributing his resource evenly among the attacked elements. In order to get the information about the system the attacker allocates a part of his resource into the intelligence activity. Analogously, the defender allocates a part of his resource into the counter-intelligence activity. The attacker's strategy presumes distribution of his resource among the intelligence and attack effort and choice of the number of attacked elements. If the attacker wins the intelligence contest, he can identify both FEs and unprotected GEs ignoring the former ones and destroying the latter ones with negligible effort. The defender's strategy presumes distribution of his resource among the counter-intelligence and the three defensive actions. The paper considers a three-period non-cooperative minmax game between the defender and the attacker and presents an algorithm for determining the agents' optimal strategies.

  2. Detection of target phonemes in spontaneous and read speech

    Mehta, G.; Cutler, A.

    1988-01-01

    Although spontaneous speech occurs more frequently in most listeners’ experience than read speech, laboratory studies of human speech recognition typically use carefully controlled materials read from a script. The phonological and prosodic characteristics of spontaneous and read speech differ considerably, however, which suggests that laboratory results may not generalize to the recognition of spontaneous and read speech materials, and their response time to detect word-initial target phonem...

  3. Robust data reconciliation and outlier detection with swarm intelligence in a thermal reactor power calculation

    Valdetaro, Eduardo Damianik, E-mail: valdtar@eletronuclear.gov.br [ELETRONUCLEAR - ELETROBRAS, Angra dos Reis, RJ (Brazil). Angra 2 Operating Dept.; Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear; Schirru, Roberto, E-mail: schirru@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear

    2011-07-01

    In Nuclear power plants, Data Reconciliation (DR) and Gross Errors Detection (GED) are techniques of increasing interest and are primarily used to keep mass and energy balance into account, which brings outcomes as a direct and indirect financial benefits. Data reconciliation is formulated by a constrained minimization problem, where the constraints correspond to energy and mass balance model. Statistical methods are used combined with the minimization of quadratic error form. Solving nonlinear optimization problem using conventional methods can be troublesome, because a multimodal function with differentiated solutions introduces some difficulties to search an optimal solution. Many techniques were developed to solve Data Reconciliation and Outlier Detection, some of them use, for example, Quadratic Programming, Lagrange Multipliers, Mixed-Integer Non Linear Programming and others use evolutionary algorithms like Genetic Algorithms (GA) and recently the use of the Particle Swarm Optimization (PSO) showed to be a potential tool as a global optimization algorithm when applied to data reconciliation. Robust Statistics is also increasing in interest and it is being used when measured data are contaminated by random errors and one can not assume the error is normally distributed, situation which reflects real problems situation. The aim of this work is to present a brief comparison between the classical data reconciliation technique and the robust data reconciliation and gross error detection with swarm intelligence procedure in calculating the thermal reactor power for a simplified heat circuit diagram of a steam turbine plant using real data obtained from Angra 2 Nuclear power plant. The main objective is to test the potential of the robust DR and GED method in a integrated framework using swarm intelligence and the three part redescending estimator of Hampel when applied to a real process condition. The results evaluate the potential use of the robust technique in

  4. Robust data reconciliation and outlier detection with swarm intelligence in a thermal reactor power calculation

    Valdetaro, Eduardo Damianik; Coordenacao dos Programas de Pos-Graduacao de Engenharia; Schirru, Roberto

    2011-01-01

    In Nuclear power plants, Data Reconciliation (DR) and Gross Errors Detection (GED) are techniques of increasing interest and are primarily used to keep mass and energy balance into account, which brings outcomes as a direct and indirect financial benefits. Data reconciliation is formulated by a constrained minimization problem, where the constraints correspond to energy and mass balance model. Statistical methods are used combined with the minimization of quadratic error form. Solving nonlinear optimization problem using conventional methods can be troublesome, because a multimodal function with differentiated solutions introduces some difficulties to search an optimal solution. Many techniques were developed to solve Data Reconciliation and Outlier Detection, some of them use, for example, Quadratic Programming, Lagrange Multipliers, Mixed-Integer Non Linear Programming and others use evolutionary algorithms like Genetic Algorithms (GA) and recently the use of the Particle Swarm Optimization (PSO) showed to be a potential tool as a global optimization algorithm when applied to data reconciliation. Robust Statistics is also increasing in interest and it is being used when measured data are contaminated by random errors and one can not assume the error is normally distributed, situation which reflects real problems situation. The aim of this work is to present a brief comparison between the classical data reconciliation technique and the robust data reconciliation and gross error detection with swarm intelligence procedure in calculating the thermal reactor power for a simplified heat circuit diagram of a steam turbine plant using real data obtained from Angra 2 Nuclear power plant. The main objective is to test the potential of the robust DR and GED method in a integrated framework using swarm intelligence and the three part redescending estimator of Hampel when applied to a real process condition. The results evaluate the potential use of the robust technique in

  5. Combining orthogonal polarization for elongated target detection with GPR

    Lualdi, Maurizio; Lombardi, Federico

    2014-01-01

    For an accurate imaging of ground penetrating radar data the polarization characteristics of the propagating electromagnetic (EM) wavefield and wave amplitude variations with antenna pattern orientation must be taken into account. For objects that show some directionality feature and cylindrical shape any misalignment between transmitter and target can strongly modify the polarization state of the backscattered wavefield, thus conditioning the detection capability of the system. Hints on the depolarization can be used to design the optimal GPR antenna survey to avoid omissions and pitfalls during data processing. This research addresses the issue of elongated target detection through a multi azimuth (or multi polarization) approach based on the combination of mutually orthogonal GPR data. Results from the analysis of the formal scattering problem demonstrate how this strategy can reach a scalar formulation of the scattering matrix and achieve a rotational invariant quantity. The effectiveness of the algorithm is then evaluated with a detailed field example showing results closely proximal to those obtained under the optimal alignment condition: detection is significantly improved and the risk of target missing is reduced. (paper)

  6. Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

    Sang-Il Oh

    2017-01-01

    Full Text Available To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN. The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

  7. Camouflaged target detection based on polarized spectral features

    Tan, Jian; Zhang, Junping; Zou, Bin

    2016-05-01

    The polarized hyperspectral images (PHSI) include polarization, spectral, spatial and radiant features, which provide more information about objects and scenes than traditional intensity or spectrum ones. And polarization can suppress the background and highlight the object, leading to the high potential to improve camouflaged target detection. So polarized hyperspectral imaging technique has aroused extensive concern in the last few years. Nowadays, the detection methods are still not very mature, most of which are rooted in the detection of hyperspectral image. And before using these algorithms, Stokes vector is used to process the original four-dimensional polarized hyperspectral data firstly. However, when the data is large and complex, the amount of calculation and error will increase. In this paper, tensor is applied to reconstruct the original four-dimensional data into new three-dimensional data, then, the constraint energy minimization (CEM) is used to process the new data, which adds the polarization information to construct the polarized spectral filter operator and takes full advantages of spectral and polarized information. This way deals with the original data without extracting the Stokes vector, so as to reduce the computation and error greatly. The experimental results also show that the proposed method in this paper is more suitable for the target detection of the PHSI.

  8. Intelligent Machine Vision for Automated Fence Intruder Detection Using Self-organizing Map

    Veldin A. Talorete Jr.

    2017-03-01

    Full Text Available This paper presents an intelligent machine vision for automated fence intruder detection. A series of still captured images that contain fence events using Internet Protocol cameras was used as input data to the system. Two classifiers were used; the first is to classify human posture and the second one will classify intruder location. The system classifiers were implemented using Self-Organizing Map after the implementation of several image segmentation processes. The human posture classifier is in charge of classifying the detected subject’s posture patterns from subject’s silhouette. Moreover, the Intruder Localization Classifier is in charge of classifying the detected pattern’s location classifier will estimate the location of the intruder with respect to the fence using geometric feature from images as inputs. The system is capable of activating the alarm, display the actual image and depict the location of the intruder when an intruder is detected. In detecting intruder posture, the system’s success rate of 88%. Overall system accuracy for day-time intruder localization is 83% and an accuracy of 88% for night-time intruder localization

  9. A Game of Cat and Mouse: Intelligence and Dynamic Targeting in Operation Allied Force

    2017-06-01

    who knows the enemy and knows himself will not be endangered in a hundred engagements. Sun Tzu The Art of War It is getting the collection of...77 BIBLIOGRAPHY .......................................................................... 87 1 Chapter 1 Introduction Air...develop ISR strategies to provide the highest quality intelligence for decision makers across the spectrum of conflict. 87 Bibliography Articles

  10. Proposal of an intelligent wayside monitoring system for detection of critical ice accumulations on railway vehicles

    Michelberger, Frank; Wagner, Adrian; Ostermann, Michael; Maly, Thomas

    2017-09-01

    At railway lines with ballasted tracks, under unfavourable conditions, the so-called flying ballast can occur predominantly for trains driving at high speeds. Especially in wintertime, it is highly likely that the causes are adhered snow or ice deposits, which are falling off the vehicle. Due to the high kinetic energy, the impact can lead to the removal of ballast stones from the structure of the ballasted track. If the stones reach the height of vehicles underside, they may be accelerated significantly due to the collision with the vehicle or may detach further ice blocks. In the worst case, a reinforcing effect occurs, which can lead to considerable damage to railway vehicles (under-floor-area, vehicle exteriors, etc.) and infrastructure (signal masts, noise barriers, etc.). Additionally the flying gravel is a significant danger to people in the nearby area of the tracks. With this feasibility study the applicability and meaningfulness of an intelligent monitoring system for identification of the critical ice accumulation to prevent the ballast fly induced by ice dropping was examined. The key findings of the research are that the detection of ice on railway vehicles and the development of an intelligent monitoring seem to be possible with existing technologies, but a proof of concept in terms of field tests is necessary.

  11. Detection of the Deformation of an Intelligent Textile in a Specific Point

    José Gisbert

    2007-06-01

    Full Text Available An intelligent textile is a textile structure that measures and reacts in front of external agents or stimulus with or without integrated electronic equipment. . The finality of the present textile is to take one more step towards intelligent textile, considering the integration of electronics and textile needs, to be industrially viable and to keep up the necessary competitiveness, raising the final price as little as possible. The finality of these experiments is to develop a textile that varies in conductivity and resistance in relation to the elongation of the textile, detecting changes caused by the alteration of a piece of clothing, from the pressure of a finger on the material, for example. One of the most important characteristics of textile is the capacity of reproducing measures, of varying the response in different tests. Two lines of research were opened: the study of the most adequate structure to achieve a response that can be reproduced and the study of the best way of taking measures without altering the behavior of the textile.

  12. Early detection and identification of anomalies in chemical regime based on computational intelligence techniques

    Figedy, Stefan; Smiesko, Ivan

    2012-01-01

    This article provides brief information about the fundamental features of a newly-developed diagnostic system for early detection and identification of anomalies being generated in water chemistry regime of the primary and secondary circuit of the VVER-440 reactor. This system, which is called SACHER (System of Analysis of CHEmical Regime), was installed within the major modernization project at the NPP-V2 Bohunice in the Slovak Republic. The SACHER system has been fully developed on MATLAB environment. It is based on computational intelligence techniques and inserts various elements of intelligent data processing modules for clustering, diagnosing, future prediction, signal validation, etc, into the overall chemical information system. The application of SACHER would essentially assist chemists to identify the current situation regarding anomalies being generated in the primary and secondary circuit water chemistry. This system is to be used for diagnostics and data handling, however it is not intended to fully replace the presence of experienced chemists to decide upon corrective actions. (author)

  13. Present situation and trend of precision guidance technology and its intelligence

    Shang, Zhengguo; Liu, Tiandong

    2017-11-01

    This paper first introduces the basic concepts of precision guidance technology and artificial intelligence technology. Then gives a brief introduction of intelligent precision guidance technology, and with the help of development of intelligent weapon based on deep learning project in foreign: LRASM missile project, TRACE project, and BLADE project, this paper gives an overview of the current foreign precision guidance technology. Finally, the future development trend of intelligent precision guidance technology is summarized, mainly concentrated in the multi objectives, intelligent classification, weak target detection and recognition, intelligent between complex environment intelligent jamming and multi-source, multi missile cooperative fighting and other aspects.

  14. Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

    Khattab M. Ali Alheeti

    2016-07-01

    Full Text Available Vehicular ad hoc networks (VANETs play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.

  15. A Targeted Swallow Screen for the Detection of Postoperative Dysphagia.

    Gee, Erica; Lancaster, Elizabeth; Meltzer, Jospeh; Mendelsohn, Abie H; Benharash, Peyman

    2015-10-01

    Postoperative dysphagia leads to aspiration pneumonia, prolonged hospital stay, and is associated with increased mortality. A simple and sensitive screening test to identify patients requiring objective dysphagia evaluation is presently lacking. In this study, we evaluated the efficacy of a novel targeted swallow screen evaluation. This was a prospective trial involving all adult patients who underwent elective cardiac surgery with cardiopulmonary bypass at our institution over an 8-week period. Within 24 hours of extubation and before the initiation of oral intake, all postsurgical patients were evaluated using the targeted swallow screen. A fiberoptic endoscopic evaluation of swallowing was requested for failed screenings. During the study, 50 postcardiac surgery patients were screened. Fifteen (30%) failed the targeted swallow screen, and ten of the fifteen (66%) failed the subsequent fiberoptic endoscopic evaluation of swallowing exam and were confirmed to have dysphagia. The screening test had 100 per cent sensitivity for detecting dysphagia in our patient population, and a specificity of 87.5 per cent. The overall incidence of dysphagia was 20 per cent. We have shown that a targeted swallow evaluation can efficiently screen patients during the postcardiac surgery period. Furthermore, we have shown that the true incidence of dysphagia after cardiac surgery is significantly higher than previously recognized in literature.

  16. Energy scavenging using piezoelectric sensors to power in pavement intelligent vehicle detection systems

    Parhad, Ashutosh

    Intelligent transportation systems use in-pavement inductive loop sensors to collect real time traffic data. This method is very expensive in terms of installation and maintenance. Our research is focused on developing advanced algorithms capable of generating high amounts of energy that can charge a battery. This electromechanical energy conversion is an optimal way of energy scavenging that makes use of piezoelectric sensors. The power generated is sufficient to run the vehicle detection module that has several sensors embedded together. To achieve these goals, we have developed a simulation module using software's like LabVIEW and Multisim. The simulation module recreates a practical scenario that takes into consideration vehicle weight, speed, wheel width and frequency of the traffic.

  17. Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

    Bal, A.; Alam, M. S.; Aslan, M. S.

    2006-05-01

    Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

  18. Computational optimisation of targeted DNA sequencing for cancer detection

    Martinez, Pierre; McGranahan, Nicholas; Birkbak, Nicolai Juul

    2013-01-01

    Despite recent progress thanks to next-generation sequencing technologies, personalised cancer medicine is still hampered by intra-tumour heterogeneity and drug resistance. As most patients with advanced metastatic disease face poor survival, there is need to improve early diagnosis. Analysing...... detection. Dividing 4,467 samples into one discovery and two independent validation cohorts, we show that up to 76% of 10 cancer types harbour at least one mutation in a panel of only 25 genes, with high sensitivity across most tumour types. Our analyses demonstrate that targeting "hotspot" regions would...

  19. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.

    Rajalakshmi, Ramachandran; Subashini, Radhakrishnan; Anjana, Ranjit Mohan; Mohan, Viswanathan

    2018-06-01

    To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist's grading. Three hundred and one patients with type 2 diabetes underwent retinal photography with Remidio 'Fundus on phone' (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArt TM ) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists' grading. Retinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9-98.7) sensitivity and 80.2% (95% CI 72.6-87.8) specificity for detecting any DR and 99.1% (95% CI 95.1-99.9) sensitivity and 80.4% (95% CI 73.9-85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively. Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.

  20. A high-resolution and intelligent dead pixel detection scheme for an electrowetting display screen

    Luo, ZhiJie; Luo, JianKun; Zhao, WenWen; Cao, Yang; Lin, WeiJie; Zhou, GuoFu

    2018-02-01

    Electrowetting display technology is realized by tuning the surface energy of a hydrophobic surface by applying a voltage based on electrowetting mechanism. With the rapid development of the electrowetting industry, how to analyze efficiently the quality of an electrowetting display screen has a very important significance. There are two kinds of dead pixels on the electrowetting display screen. One is that the oil of pixel cannot completely cover the display area. The other is that indium tin oxide semiconductor wire connecting pixel and foil was burned. In this paper, we propose a high-resolution and intelligent dead pixel detection scheme for an electrowetting display screen. First, we built an aperture ratio-capacitance model based on the electrical characteristics of electrowetting display. A field-programmable gate array is used as the integrated logic hub of the system for a highly reliable and efficient control of the circuit. Dead pixels can be detected and displayed on a PC-based 2D graphical interface in real time. The proposed dead pixel detection scheme reported in this work has promise in automating electrowetting display experiments.

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

    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.

  2. [Study on spectral detection of green plant target].

    Deng, Wei; Zhao, Chun-jiang; He, Xiong-kui; Chen, Li-ping; Zhang, Lu-da; Wu, Guang-wei; Mueller, J; Zhai, Chang-yuan

    2010-08-01

    Weeds grow scatteredly in fields, where many insentient objects exist, for example, withered grasses, dry twig and barriers. In order to improve the precision level of spraying, it is important to study green plant detecting technology. The present paper discussed detecting method of green plant by using spectral recognizing technology, because of the real-time feature of spectral recognition. By analyzing the reflectivity difference between each of the two sides of the "red edge" of the spectrum from plants and surrounding environment, green plant discriminat index (GPDI) is defined as the value which equals the reflectivity ratio at the wavelength of 850 nm divided by the reflectivity ratio at the wavelength of 650 nm. The original spectral data of green plants and the background were measured by using the handhold FieldSpec 3 Spectroradiometer manufactured by ASD Inc. in USA. The spectral data were processed to get the reflectivity of each measured objects and to work out the GPDI thereof as well. The classification model of green plant and its background was built up using decision tree method in order to obtain the threshold of GPDI to distinguish green plants and the background. The threshold of GPDI was chosen as 5.54. The detected object was recognized as green plant when it is GPDI>GPDITH, and vice versa. Through another test, the accuracy rate was verified which was 100% by using the threshold. The authors designed and developed the green plant detector based on single chip microcomputer (SCM) "AT89S51" and photodiode "OPT101" to realize detecting green plants from the background. After passing through two optical filters, the center wavelengths of which are 650 and 850 nm respectively, the reflected light from measured targets was detected by two photodiodes and converted into electrical signals. These analog signals were then converted to digital signals via an analog-to-digital converter (ADS7813) after being amplified by a signal amplifier (OP400

  3. A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

    Shuxin, Li; Zhilong, Zhang; Biao, Li

    2018-01-01

    Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

  4. Kepler Planet Detection Metrics: Per-Target Detection Contours for Data Release 25

    Burke, Christopher J.; Catanzarite, Joseph

    2017-01-01

    A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).

  5. Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

    Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

    2017-05-01

    Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

  6. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

    Hirasawa, Toshiaki; Aoyama, Kazuharu; Tanimoto, Tetsuya; Ishihara, Soichiro; Shichijo, Satoki; Ozawa, Tsuyoshi; Ohnishi, Tatsuya; Fujishiro, Mitsuhiro; Matsuo, Keigo; Fujisaki, Junko; Tada, Tomohiro

    2018-07-01

    Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN. The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface. The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

  7. The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

    Gulshan Kumar

    2012-01-01

    Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

  8. Use of Business Intelligence as a Strategic Information Technology in Banking: Farud Discovery & Detection

    علی محقر

    2009-02-01

    Full Text Available In today?s competitive, complex and rapidly changing business world, based on information, it is very essential to have timely and accurate insight of business conditions and activities to check whether they are healthy and according to business norms. Information obviously plays a critical role to have this insight and it can only be of avail, if it is directional, focused, timely and easily accessible and also if it gives both the macro/strategic and the micro/operational view of the company. Due to the advancement in information technology, state-of-the-art technologies like business intelligence (BI have been developed to provide us the above features. This paper provides reader an insight of BI and its strategic importance. It also explains how BI can be effective technology in industries like banking to overcome critical issue like fraud discovery and detection. The methodology, based on BI, in the form of model named BI model for fraud discovery and detection has been devised and suggested at the end of paper along with it its details for overcoming the above mentioned issue in more effective and efficient manner.

  9. Fusion-based multi-target tracking and localization for intelligent surveillance systems

    Rababaah, Haroun; Shirkhodaie, Amir

    2008-04-01

    In this paper, we have presented two approaches addressing visual target tracking and localization in complex urban environment. The two techniques presented in this paper are: fusion-based multi-target visual tracking, and multi-target localization via camera calibration. For multi-target tracking, the data fusion concepts of hypothesis generation/evaluation/selection, target-to-target registration, and association are employed. An association matrix is implemented using RGB histograms for associated tracking of multi-targets of interests. Motion segmentation of targets of interest (TOI) from the background was achieved by a Gaussian Mixture Model. Foreground segmentation, on other hand, was achieved by the Connected Components Analysis (CCA) technique. The tracking of individual targets was estimated by fusing two sources of information, the centroid with the spatial gating, and the RGB histogram association matrix. The localization problem is addressed through an effective camera calibration technique using edge modeling for grid mapping (EMGM). A two-stage image pixel to world coordinates mapping technique is introduced that performs coarse and fine location estimation of moving TOIs. In coarse estimation, an approximate neighborhood of the target position is estimated based on nearest 4-neighbor method, and in fine estimation, we use Euclidean interpolation to localize the position within the estimated four neighbors. Both techniques were tested and shown reliable results for tracking and localization of Targets of interests in complex urban environment.

  10. Magneto-mechanical trapping systems for biological target detection

    Li, Fuquan; Kodzius, Rimantas; Gooneratne, Chinthaka P.; Foulds, Ian G.; Kosel, Jürgen

    2014-01-01

    We demonstrate a magnetic microsystem capable of detecting nucleic acids via the size difference between bare magnetic beads and bead compounds. The bead compounds are formed through linking nonmagnetic beads and magnetic beads by the target nucleic acids. The system comprises a tunnel magneto-resistive (TMR) sensor, a trapping well, and a bead-concentrator. The TMR sensor detects the stray field of magnetic beads inside the trapping well, while the sensor output depends on the number of beads. The size of the bead compounds is larger than that of bare magnetic beads, and fewer magnetic beads are required to fill the trapping well. The bead-concentrator, in turn, is capable of filling the trap in a controlled fashion and so to shorten the assay time. The bead-concentrator includes conducting loops surrounding the trapping well and a conducting line underneath. The central conducting line serves to attract magnetic beads in the trapping well and provides a magnetic field to magnetize them so to make them detectable by the TMR sensor. This system excels by its simplicity in that the DNA is incubated with magnetic and nonmagnetic beads, and the solution is then applied to the chip and analyzed in a single step. In current experiments, a signal-to-noise ratio of 40.3 dB was obtained for a solution containing 20.8 nM of DNA. The sensitivity and applicability of this method can be controlled by the size or concentration of the nonmagnetic bead, or by the dimension of the trapping well. (author)

  11. Magneto-mechanical trapping systems for biological target detection

    Li, Fuquan

    2014-03-29

    We demonstrate a magnetic microsystem capable of detecting nucleic acids via the size difference between bare magnetic beads and bead compounds. The bead compounds are formed through linking nonmagnetic beads and magnetic beads by the target nucleic acids. The system comprises a tunnel magneto-resistive (TMR) sensor, a trapping well, and a bead-concentrator. The TMR sensor detects the stray field of magnetic beads inside the trapping well, while the sensor output depends on the number of beads. The size of the bead compounds is larger than that of bare magnetic beads, and fewer magnetic beads are required to fill the trapping well. The bead-concentrator, in turn, is capable of filling the trap in a controlled fashion and so to shorten the assay time. The bead-concentrator includes conducting loops surrounding the trapping well and a conducting line underneath. The central conducting line serves to attract magnetic beads in the trapping well and provides a magnetic field to magnetize them so to make them detectable by the TMR sensor. This system excels by its simplicity in that the DNA is incubated with magnetic and nonmagnetic beads, and the solution is then applied to the chip and analyzed in a single step. In current experiments, a signal-to-noise ratio of 40.3 dB was obtained for a solution containing 20.8 nM of DNA. The sensitivity and applicability of this method can be controlled by the size or concentration of the nonmagnetic bead, or by the dimension of the trapping well.

  12. Visual performance on detection tasks with double-targets of the same and different difficulty.

    Chan, Alan H S; Courtney, Alan J; Ma, C W

    2002-10-20

    This paper reports a study of measurement of horizontal visual sensitivity limits for 16 subjects in single-target and double-targets detection tasks. Two phases of tests were conducted in the double-targets task; targets of the same difficulty were tested in phase one while targets of different difficulty were tested in phase two. The range of sensitivity for the double-targets test was found to be smaller than that for single-target in both the same and different target difficulty cases. The presence of another target was found to affect performance to a marked degree. Interference effect of the difficult target on detection of the easy one was greater than that of the easy one on the detection of the difficult one. Performance decrement was noted when correct percentage detection was plotted against eccentricity of target in both the single-target and double-targets tests. Nevertheless, the non-significant correlation found between the performance for the two tasks demonstrated that it was impossible to predict quantitatively ability for detection of double targets from the data for single targets. This indicated probable problems in generalizing data for single target visual lobes to those for multiple targets. Also lobe area values obtained from measurements using a single-target task cannot be applied in a mathematical model for situations with multiple occurrences of targets.

  13. An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation.

    Lin, Chin-Teng; Chang, Kuan-Cheng; Lin, Chun-Ling; Chiang, Chia-Cheng; Lu, Shao-Wei; Chang, Shih-Sheng; Lin, Bor-Shyh; Liang, Hsin-Yueh; Chen, Ray-Jade; Lee, Yuan-Teh; Ko, Li-Wei

    2010-05-01

    This study presents a novel wireless, ambulatory, real-time, and autoalarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world. This system consists of a lightweight and power-saving wireless ECG device equipped with a built-in automatic warning expert system. This device is connected to a mobile and ubiquitous real-time display platform. The acquired ECG signals are instantaneously transmitted to mobile devices, such as netbooks or mobile phones through Bluetooth, and then, processed by the expert system. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel tracking cardiac-rhythm disorders. The proposed system also activates an emergency medical alarm system when problems occur. Clinical testing reveals that the proposed system is approximately 94% accurate, with high sensitivity, specificity, and positive prediction rates for ten normal subjects and 20 AF patients. We believe that in the future a business-card-like ECG device, accompanied with a mobile phone, can make universal cardiac protection service possible.

  14. Federated Access to Cyber Observables for Detection of Targeted Attacks

    2014-10-01

    each manages. The DQNs also utilize an intelligent information ex- traction capability for automatically suggesting mappings from text found in audit ...Harmelen, and others, “OWL web ontology language overview,” W3C Recomm., vol. 10, no. 2004–03, p. 10, 2004. [4] D. Miller and B. Pearson , Security...Online]. Available: http://www.disa.mil/Services/Information- Assurance /HBS/HBSS. [21] S. Zanikolas and R. Sakellariou, “A taxonomy of grid

  15. Information Design for “Weak Signal” detection and processing in Economic Intelligence: A case study on Health resources

    Sahbi Sidhom

    2011-12-01

    Full Text Available The topics of this research cover all phases of “Information Design” applied to detect and profit from weak signals in economic intelligence (EI or business intelligence (BI. The field of the information design (ID applies to the process of translating complex, unorganized or unstructured data into valuable and meaningful information. ID practice requires an interdisciplinary approach, which combines skills in graphic design (writing, analysis processing and editing, human performances technology and human factors. Applied in the context of information system, it allows end-users to easily detect implicit topics known as “weak signals” (WS. In our approach to implement the ID, the processes cover the development of a knowledge management (KM process in the context of EI. A case study concerning information monitoring health resources is presented using ID processes to outline weak signals. Both French and American bibliographic databases were applied to make the connection to multilingual concepts in the health watch process.

  16. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Sungho Kim

    2016-07-01

    Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

  17. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  18. Detection algorithm of infrared small target based on improved SUSAN operator

    Liu, Xingmiao; Wang, Shicheng; Zhao, Jing

    2010-10-01

    The methods of detecting small moving targets in infrared image sequences that contain moving nuisance objects and background noise is analyzed in this paper. A novel infrared small target detection algorithm based on improved SUSAN operator is put forward. The algorithm selects double templates for the infrared small target detection: one size is greater than the small target point size and another size is equal to the small target point size. First, the algorithm uses the big template to calculate the USAN of each pixel in the image and detect the small target, the edge of the image and isolated noise pixels; Then the algorithm uses the another template to calculate the USAN of pixels detected in the first step and improves the principles of SUSAN algorithm based on the characteristics of the small target so that the algorithm can only detect small targets and don't sensitive to the edge pixels of the image and isolated noise pixels. So the interference of the edge of the image and isolate noise points are removed and the candidate target points can be identified; At last, the target is detected by utilizing the continuity and consistency of target movement. The experimental results indicate that the improved SUSAN detection algorithm can quickly and effectively detect the infrared small targets.

  19. A Study of Adaptive Detection of Range-Distributed Targets

    Gerlach, Karl R

    2000-01-01

    ... to be characterized as complex zero-mean correlated Gaussian random variables. The target's or targets' complex amplitudes are assumed to be distributed across the entire input data block (sensor x range...

  20. The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection

    Jauk, Emanuel; Benedek, Mathias; Dunst, Beate; Neubauer, Aljoscha C.

    2013-01-01

    The relationship between intelligence and creativity has been subject to empirical research for decades. Nevertheless, there is yet no consensus on how these constructs are related. One of the most prominent notions concerning the interplay between intelligence and creativity is the threshold hypothesis, which assumes that above-average intelligence represents a necessary condition for high-level creativity. While earlier research mostly supported the threshold hypothesis, it has come under fire in recent investigations. The threshold hypothesis is commonly investigated by splitting a sample at a given threshold (e.g., at 120 IQ points) and estimating separate correlations for lower and upper IQ ranges. However, there is no compelling reason why the threshold should be fixed at an IQ of 120, and to date, no attempts have been made to detect the threshold empirically. Therefore, this study examined the relationship between intelligence and different indicators of creative potential and of creative achievement by means of segmented regression analysis in a sample of 297 participants. Segmented regression allows for the detection of a threshold in continuous data by means of iterative computational algorithms. We found thresholds only for measures of creative potential but not for creative achievement. For the former the thresholds varied as a function of criteria: When investigating a liberal criterion of ideational originality (i.e., two original ideas), a threshold was detected at around 100 IQ points. In contrast, a threshold of 120 IQ points emerged when the criterion was more demanding (i.e., many original ideas). Moreover, an IQ of around 85 IQ points was found to form the threshold for a purely quantitative measure of creative potential (i.e., ideational fluency). These results confirm the threshold hypothesis for qualitative indicators of creative potential and may explain some of the observed discrepancies in previous research. In addition, we obtained

  1. Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2012-01-01

    measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study...... of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi- modal detection...... system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data....

  2. Robust Small Target Co-Detection from Airborne Infrared Image Sequences.

    Gao, Jingli; Wen, Chenglin; Liu, Meiqin

    2017-09-29

    In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.

  3. A new method of small target detection based on neural network

    Hu, Jing; Hu, Yongli; Lu, Xinxin

    2018-02-01

    The detection and tracking of moving dim target in infrared image have been an research hotspot for many years. The target in each frame of images only occupies several pixels without any shape and structure information. Moreover, infrared small target is often submerged in complicated background with low signal-to-clutter ratio, making the detection very difficult. Different backgrounds exhibit different statistical properties, making it becomes extremely complex to detect the target. If the threshold segmentation is not reasonable, there may be more noise points in the final detection, which is unfavorable for the detection of the trajectory of the target. Single-frame target detection may not be able to obtain the desired target and cause high false alarm rate. We believe the combination of suspicious target detection spatially in each frame and temporal association for target tracking will increase reliability of tracking dim target. The detection of dim target is mainly divided into two parts, In the first part, we adopt bilateral filtering method in background suppression, after the threshold segmentation, the suspicious target in each frame are extracted, then we use LSTM(long short term memory) neural network to predict coordinates of target of the next frame. It is a brand-new method base on the movement characteristic of the target in sequence images which could respond to the changes in the relationship between past and future values of the values. Simulation results demonstrate proposed algorithm can effectively predict the trajectory of the moving small target and work efficiently and robustly with low false alarm.

  4. An infrared small target detection method based on multiscale local homogeneity measure

    Nie, Jinyan; Qu, Shaocheng; Wei, Yantao; Zhang, Liming; Deng, Lizhen

    2018-05-01

    Infrared (IR) small target detection plays an important role in the field of image detection area owing to its intrinsic characteristics. This paper presents a multiscale local homogeneity measure (MLHM) for infrared small target detection, which can enhance the performance of IR small target detection system. Firstly, intra-patch homogeneity of the target itself and the inter-patch heterogeneity between target and the local background regions are integrated to enhance the significant of small target. Secondly, a multiscale measure based on local regions is proposed to obtain the most appropriate response. Finally, an adaptive threshold method is applied to small target segmentation. Experimental results on three different scenarios indicate that the MLHM has good performance under the interference of strong noise.

  5. A Study of Adaptive Detection of Range-Distributed Targets

    Gerlach, Karl R

    2000-01-01

    .... The unknown parameters associated with the hypothesis test are the complex amplitudes in range of the desired target and the unknown covariance matrix of the additive interference, which is assumed...

  6. Electromagnetic Induction Spectroscopy for the Detection of Subsurface Targets

    2012-12-01

    is proportional to the product of the transmitted and received magnetic fields and the magnetic polarizability tensor of the target being measured by...the EMI sensor (Appendix A). The magnetic polarizability tensor of several canonical targets can be calculated analytically, and these formulas show...prescreener. A simple voting mechanism is employed to discourage temporary mislabeling of land- mines by taking advantage of the sequential measurements. As

  7. An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems

    El Hariri, Mohamad [Florida Intl Univ., Miami, FL (United States); Faddel, Samy [Florida Intl Univ., Miami, FL (United States); Mohammed, Osama [Florida Intl Univ., Miami, FL (United States)

    2017-11-01

    Decentralized and hierarchical microgrid control strategies have lain the groundwork for shaping the future smart grid. Such control approaches require the cooperation between microgrid operators in control centers, intelligent microcontrollers, and remote terminal units via secure and reliable communication networks. In order to enhance the security and complement the work of network intrusion detection systems, this paper presents an artificially intelligent physical model-checking that detects tampered-with circuit breaker switching control commands whether, due to a cyber-attack or human error. In this technique, distributed agents, which are monitoring sectionalized areas of a given microgrid, will be trained and continuously adapted to verify that incoming control commands do not violate the physical system operational standards and do not put the microgrid in an insecure state. The potential of this approach has been tested by deploying agents that monitor circuit breakers status commands on a 14-bus IEEE benchmark system. The results showed the accuracy of the proposed framework in characterizing the power system and successfully detecting malicious and/or erroneous control commands.

  8. Knowledge-Base Application to Ground Moving Target Detection

    Adve, R

    2001-01-01

    This report summarizes a multi-year in-house effort to apply knowledge-base control techniques and advanced Space-Time Adaptive Processing algorithms to improve detection performance and false alarm...

  9. Intelligence in Artificial Intelligence

    Datta, Shoumen Palit Austin

    2016-01-01

    The elusive quest for intelligence in artificial intelligence prompts us to consider that instituting human-level intelligence in systems may be (still) in the realm of utopia. In about a quarter century, we have witnessed the winter of AI (1990) being transformed and transported to the zenith of tabloid fodder about AI (2015). The discussion at hand is about the elements that constitute the canonical idea of intelligence. The delivery of intelligence as a pay-per-use-service, popping out of ...

  10. Penalty dynamic programming algorithm for dim targets detection in sensor systems.

    Huang, Dayu; Xue, Anke; Guo, Yunfei

    2012-01-01

    In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

  11. An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

    Tian, Hao; Yan, Zhaoli; Yang, Jun

    2018-04-09

    Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

  12. Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

    Binol, Hamidullah; Bal, Abdullah

    2016-05-01

    A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.

  13. Detection and characterization of ship targets using CryoSat-2 altimeter waveforms

    G?mez-Enri, Jesus; Scozzari, Andrea; Soldovieri, Francesco; Coca, Josep; Vignudelli, Stefano

    2016-01-01

    This article describes an investigation of the new possibilities offered by SAR altimetry compared with conventional altimetry in the detection and characterization of non-ocean targets. We explore the capabilities of the first SAR altimeter installed on the European Space Agency satellite CryoSat-2 for the detection and characterization of ships. We propose a methodology for the detection of anomalous targets in the radar signals, based on the advantages of SAR/Doppler processing over conven...

  14. Detection of cyst using image segmentation and building knowledge-based intelligent decision support system as an aid to telemedicine

    Janet, J.; Natesan, T. R.; Santhosh, Ramamurthy; Ibramsha, Mohideen

    2005-02-01

    An intelligent decision support tool to the Radiologist in telemedicine is described. Medical prescriptions are given based on the images of cyst that has been transmitted over computer networks to the remote medical center. The digital image, acquired by sonography, is converted into an intensity image. This image is then subjected to image preprocessing which involves correction methods to eliminate specific artifacts. The image is resized into a 256 x 256 matrix by using bilinear interpolation method. The background area is detected using distinct block operation. The area of the cyst is calculated by removing the background area from the original image. Boundary enhancement and morphological operations are done to remove unrelated pixels. This gives us the cyst volume. This segmented image of the cyst is sent to the remote medical center for analysis by Knowledge based artificial Intelligent Decision Support System (KIDSS). The type of cyst is detected and reported to the control mechanism of KIDSS. Then the inference engine compares this with the knowledge base and gives appropriate medical prescriptions or treatment recommendations by applying reasoning mechanisms at the remote medical center.

  15. Delayed Detection of Tonal Targets in Background Noise in Dyslexia

    Chait, Maria; Eden, Guinevere; Poeppel, David; Simon, Jonathan Z.; Hill, Deborah F.; Flowers, D. Lynn

    2007-01-01

    Individuals with developmental dyslexia are often impaired in their ability to process certain linguistic and even basic non-linguistic auditory signals. Recent investigations report conflicting findings regarding impaired low-level binaural detection mechanisms associated with dyslexia. Binaural impairment has been hypothesized to stem from a…

  16. Detecting proxima b's atmosphere with JWST targeting CO

    Snellen, I. A G; Désert, J. M.; Waters, L. B.F.M.; Robinson, T; Meadows, V.; van Dishoeck, E.F.; Brandl, B.R.; Henning, T.; Bouwman, J.; Lahuis, F.; Min, M.; Lovis, C.; Dominik, C.; Van Eylen, V.; Sing, D.; Anglada-Escudé, G.; Birkby, J. L.; Brogi, M.

    2017-01-01

    Exoplanet Proxima b will be an important laboratory for the search for extraterrestrial life for the decades ahead. Here, we discuss the prospects of detecting carbon dioxide at 15 μm using a spectral filtering technique with the Medium Resolution Spectrograph (MRS) mode of the Mid-Infrared

  17. An assessment of independent component analysis for detection of military targets from hyperspectral images

    Tiwari, K. C.; Arora, M. K.; Singh, D.

    2011-10-01

    Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed-Xiaoli anomaly detector and Uniform Target Detector (RXD-UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.

  18. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.

    Best, Myron G; Sol, Nik; In 't Veld, Sjors G J G; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna-Larissa N; Fejes, Aniko V; Tjon Kon Fat, Lee-Ann; Huis In 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y; Meijer, Laura L; Kooi, Irsan E; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C; Urbanus, Rolf T; Hoefer, Imo E; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P; Vandertop, W Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B; Tannous, Bakhos A; de Langen, Adrianus J; Smit, Egbert F; van den Heuvel, Michel M; Wurdinger, Thomas

    2017-08-14

    Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  19. Use of the Wechsler Adult Intelligence Scale Digit Span subtest for malingering detection: a meta-analytic review.

    Jasinski, Lindsey J; Berry, David T R; Shandera, Anni L; Clark, Jessica A

    2011-03-01

    Twenty-four studies utilizing the Wechsler Adult Intelligence Scale (WAIS) Digit Span subtest--either the Reliable Digit Span (RDS) or Age-Corrected Scaled Score (DS-ACSS) variant--for malingering detection were meta-analytically reviewed to evaluate their effectiveness in detecting malingered neurocognitive dysfunction. RDS and DS-ACSS effectively discriminated between honest responders and dissimulators, with average weighted effect sizes of 1.34 and 1.08, respectively. No significant differences were found between RDS and DS-ACSS. Similarly, no differences were found between the Digit Span subtest from the WAIS or Wechsler Memory Scale (WMS). Strong specificity and moderate sensitivity were observed, and optimal cutting scores are recommended.

  20. Target Detection Routine (TADER). User’s Guide.

    1987-09-01

    o System range capability subset (one record - omitted for standoff SLAR and penetrating system) o System inherent detection probability subset ( IELT ...records, i.e., one per element type) * System capability modifier subset/A=1, E=1 ( IELT records) o System capability modifier subset/A=1, E=2 ( IELT ...records) s System capability modifier subset/A=2, E=1 ( IELT records) o System capability modifier subset/A=2, E=2 ( IELT records) Unit Data Set (one set

  1. Feasibility Study on Passive-radar Detection of Space Targets Using Spaceborne Illuminators of Opportunity

    Jiang Tie-zhen

    2015-01-01

    Full Text Available Space target surveillance generally uses active radars. To take full advantage of passive radars, the idea of using spaceborne illuminators of opportunity for space target detection is presented in this paper. Analysis of the detectable time and direct wave suppression shows that passive radar using spaceborne illuminators of opportunity can effectively detect a Low-Earth-Orbit (LEO target. Meanwhile, Ku and L band bi-static radar cross section of passive radars that use spaceborne illuminators of opportunity are presented by simulation, providing the basis of choosing space target forward scatter. Finally the key parameters, mainly system gain, accumulation time and radiation source selection are studied. Results show that system size using satellite TV signals as illuminators of opportunity is relatively small. These encouraging results should stimulate the development of passive radar detection of space targets using spaceborne illuminators of opportunity.

  2. Harmonizing intelligence terminologies in business: Literature review

    Sivave Mashingaidze

    2014-11-01

    Full Text Available The principal objective of this article is to do a literature review of different intelligence terminology with the aim of establishing the common attributes and differences, and to propose a universal and comprehensive definition of intelligence for common understanding amongst users. The findings showed that Competitive Intelligence has the broadest scope of intelligence activities covering the whole external operating environment of the company and targeting all levels of decision-making for instance; strategic intelligence, tactical intelligence and operative intelligence. Another terminology was found called Cyber IntelligenceTM which encompasses competitor intelligence, strategic intelligence, market intelligence and counterintelligence. In conclusion although CI has the broadest scope of intelligence and umbrella to many intelligence concepts, still Business Intelligence, and Corporate Intelligence are often used interchangeably as CI

  3. Contrast, size, and orientation-invariant target detection in infrared imagery

    Zhou, Yi-Tong; Crawshaw, Richard D.

    1991-08-01

    Automatic target detection in IR imagery is a very difficult task due to variations in target brightness, shape, size, and orientation. In this paper, the authors present a contrast, size, and orientation invariant algorithm based on Gabor functions for detecting targets from a single IR image frame. The algorithms consists of three steps. First, it locates potential targets by using low-resolution Gabor functions which resist noise and background clutter effects, then, it removes false targets and eliminates redundant target points based on a similarity measure. These two steps mimic human vision processing but are different from Zeevi's Foveating Vision System. Finally, it uses both low- and high-resolution Gabor functions to verify target existence. This algorithm has been successfully tested on several IR images that contain multiple examples of military vehicles with different size and brightness in various background scenes and orientations.

  4. A Review of Ground Target Detection and Classification Techniques in Forward Scattering Radars

    M. E. A. Kanona

    2018-06-01

    Full Text Available This paper presents a review of target detection and classification in forward scattering radar (FSR which is a special state of bistatic radars, designed to detect and track moving targets in the narrow region along the transmitter-receiver base line. FSR has advantages and incredible features over other types of radar configurations. All previous studies proved that FSR can be used as an alternative system for ground target detection and classification. The radar and FSR fundamentals were addressed and classification algorithms and techniques were debated. On the other hand, the current and future applications and the limitations of FSR were discussed.

  5. Myocardial ischemia detection by artificial intelligence interpretation of Tl-201 tomograms

    Herbst, M.D.; Garcia, E.V.; Cooke, C.D.; Folks, R.D.; Ezquerra, N.F.

    1989-01-01

    This paper reports on an expert system environment which automatically assigned certainty factors to abnormal regions in stress and delayed myocardial thallium-201 polar bulls-eye plots. MYCIN-type algorithms propagated certainty factors for the presence, location, and character of each coronary lesion. Ninety-four previously validated rules that considered only stress perfusion defects spawned 91 new rules considering tracer redistribution. Fifteen new rules assessed vascular territories for the presence and location of fixed or reversible defects. This artificial intelligence tool can provide novice readers of cardiac T1-201 studies automatic, consistent, objective, and justified interpretations that consider artifacts, coronary territory overlap, and multiple defects

  6. Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control

    Heekang Kim

    2016-07-01

    Full Text Available This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD or Complementary metal-Oxide-Semiconductor (CMOS camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC, the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs, High-intensity discharge (HID, and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM, Spectral Correlation Mapper (SCM, and Euclidean Distance Mapper (EDM. The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen.

  7. Low velocity target detection based on time-frequency image for high frequency ground wave radar

    YAN Songhua; WU Shicai; WEN Biyang

    2007-01-01

    The Doppler spectral broadening resulted from non-stationary movement of target and radio-frequency interference will decrease the veracity of target detection by high frequency ground wave(HEGW)radar.By displaying the change of signal energy on two dimensional time-frequency images based on time-frequency analysis,a new mathematical morphology method to distinguish target from nonlinear time-frequency curves is presented.The analyzed results from the measured data verify that with this new method the target can be detected correctly from wide Doppler spectrum.

  8. A novel framework for intelligent signal detection via artificial neural networks for cyclic voltammetry in pyroprocessing technology

    Rakhshan Pouri, Samaneh; Manic, Milos; Phongikaroon, Supathorn

    2018-01-01

    Highlights: •First time ANN implementation toward pyroprocessing safeguards. •Real time monitoring in terms of intelligent materials detection and accountability. •CV simulation via ANN showing a high accuracy of prediction for the unseen situation. •Elimination of trial and error approach to avoid overfitting in learning. -- Abstract: Electrorefiner (ER) is the heart of pyroprocessing technology which contains different fission, rare-earth, and transuranic chloride compositions during the operation. This is still a developing technology that needs to be advanced for the commercial reprocessing design of used nuclear fuel (UNF) in terms of intelligent materials detection and accountability towards safeguards. A novel signal detection, artificial neural network (ANN), has been proposed in this study to apply on massive ER systemic parameters to simulate cyclic voltammetry (CV) graphs for the unseen situation. ANN could be trained to mimic the system by driving the data sets interrelation between variables to provide current and potential simulated data sets with a high accuracy of prediction. For this purpose, over 230,000 experimental data points reported in literature have been explored—0.5–5 wt% of zirconium chloride (ZrCl 4 ) in LiCl-KCl molten salt with different scan rates at 773 K. This study has illustrated a new framework of ANN implementation to eliminate trial and error approach by comparing the average error of one to three hidden layers with different number of neurons. In addition, this framework results in finding a preferable balance between underfitting and overfitting in deep learning. Furthermore, simulated CV graphs were compared with the experimental data and illustrated a reasonable prediction. The results reveal two structures with three hidden layers providing a good prediction with a low average error. The outcomes indicate that ANN has a strong potential in applying toward safeguards for pyroprocessing technology.

  9. Improved OAM-Based Radar Targets Detection Using Uniform Concentric Circular Arrays

    Mingtuan Lin

    2016-01-01

    Full Text Available Without any relative moves or beam scanning, the novel Orbital-Angular-Momentum- (OAM- based radar targets detection technique using uniform concentric circular arrays (UCCAs shows the azimuthal estimation ability, which provides new perspective for radar system design. However, the main estimation method, that is, Fast Fourier Transform (FFT, under this scheme suffers from low resolution. As a solution, this paper rebuilds the OAM-based radar targets detection model and introduces the multiple signal classification (MUSIC algorithm to improve the resolution for detecting targets within the main lobes. The spatial smoothing technique is proposed to tackle the coherent problem brought by the proposed model. Analytical study and simulation demonstrate the superresolution estimation capacity the MUSIC algorithm can achieve for detecting targets within the main lobes. The performance of the MUSIC algorithm to detect targets not illuminated by the main lobes is further evaluated. Despite the fact that MUSIC algorithm loses the resolution advantage under this case, its estimation is more robust than that of the FFT method. Overall, the proposed MUSIC algorithm for the OAM-based radar system demonstrates the superresolution ability for detecting targets within the main lobes and good robustness for targets out of the main lobes.

  10. Dim small targets detection based on self-adaptive caliber temporal-spatial filtering

    Fan, Xiangsuo; Xu, Zhiyong; Zhang, Jianlin; Huang, Yongmei; Peng, Zhenming

    2017-09-01

    To boost the detect ability of dim small targets, this paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HQS). Finally, on the basis of image pre-processing, to address the problem of missed and wrong detection caused by fixed caliber of traditional pipeline filtering, this paper used targets' multi-frame movement correlation in the time-space domain, combined with the scale-space theory, to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets' scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the targets. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HQS significantly increased the signal-noise ratio of images; when the signal-noise ratio was lower than 2.6 dB, this detection algorithm could effectively eliminate noise and detect targets. For the algorithm, the lowest signal-to-noise ratio of the detectable target is 0.37.

  11. Moving Target Detection With Compact Laser Doppler Radar

    Sepp, G.; Breining, A.; Eisfeld, W.; Knopp, R.; Lill, E.; Wagner, D.

    1989-12-01

    This paper describes an experimental integrated optronic system for detection and tracking of moving objects. The system is based on a CO2 waveguide laser Doppler ra-dar with homodyne receiver and galvanometer mirror beam scanner. A "hot spot" seeker consisting of a thermal imager with image processor transmits the coordinates of IR-emitting, i.e. potentially powered, objects to the laser radar scanner. The scanner addresses these "hot" locations operating in a large field-of-view (FOV) random ac-cess mode. Hot spots exhibiting a Doppler shifted laser signal are indicated in the thermal image by velocity-to-colour encoded markers. After switching to a small FOV scanning mode, the laser Doppler radar is used to track fast moving objects. Labora-tory and field experiments with moving objects including rotating discs, automobiles and missiles are described.

  12. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

    Panagiotis Bountris

    2014-01-01

    Full Text Available Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV, including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS, composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%, high specificity (97.1%, high positive predictive value (89.4%, and high negative predictive value (97.1%, for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+. In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

  13. An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection.

    Bountris, Panagiotis; Haritou, Maria; Pouliakis, Abraham; Margari, Niki; Kyrgiou, Maria; Spathis, Aris; Pappas, Asimakis; Panayiotides, Ioannis; Paraskevaidis, Evangelos A; Karakitsos, Petros; Koutsouris, Dimitrios-Dionyssios

    2014-01-01

    Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

  14. Development and application of deep convolutional neural network in target detection

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  15. Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images.

    Zou, Zhengxia; Shi, Zhenwei

    2018-03-01

    We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)." In this paradigm, "Memories" can be interpreted as any model distribution learned from training data and "random access" means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set "LEarning, VIsion and Remote sensing laboratory (LEVIR)", which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

  16. Polarization Calculation and Underwater Target Detection Inspired by Biological Visual Imaging

    Jie Shen

    2014-04-01

    Full Text Available In challenging underwater environments, the polarization parameter maps calculated by the Stokes model are characterized by the high noise and error, harassing the underwater target detection tasks. In order to solve this problem, this paper proposes a novel bionic polarization calculation and underwater target detection method by modeling the visual system of mantis shrimps. This system includes many operators including a polarization-opposition calculation, a factor optimization and a visual neural network model. A calibration learning method is proposed to search the optimal value of the factors in the linear subtraction model. Finally, a six-channel visual neural network model is proposed to detect the underwater targets. Experimental results proved that the maps produced by the polarization-opposition parameter is more accurate and have lower noise than that produced by the Stokes parameter, achieving better performance in underwater target detection tasks.

  17. Remote sensing image ship target detection method based on visual attention model

    Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

    2017-11-01

    The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

  18. Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

    Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

    2017-04-01

    Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

  19. Effect of Various Environmental Stressors on Target Detection, Identification, and Marksmanship

    Tikuisis, Peter; Keefe, Allan A

    2007-01-01

    .... Using a small arms trainer (SAT), the detection, identification, and engagement of targets were tested under a variety of environmentally stressful conditions including heat and cold exposure, noise, fatiguing exercise, and sleep...

  20. Target Detection, Identification, and Marksmanship Under Various Types of Physiological Strain

    Tikuisis, Peter

    2006-01-01

    .... Using a small arms trainer (SAT), target detection, identification, and engagement were tested under a variety of conditions including heat and cold exposure, fatiguing exercise, and sleep deprivation, with caffeine intervention...

  1. Signal Detection, Target Tracking and Differential Geometry Applications to Statistical Inference

    Rao, C

    1997-01-01

    Signal detection and target tracking. A novel method known as polynomial rooting approach is proposed to obtain estimates of frequencies, amplitudes and noise variance of two-dimensional exponential signals...

  2. Detection and localization of multiple short range targets using FMCW radar signal

    Jardak, Seifallah; Kiuru, Tero; Metso, Mikko; Pursula, Pekka; Hakli, Janne; Hirvonen, Mervi; Ahmed, Sajid; Alouini, Mohamed-Slim

    2016-01-01

    In this paper, a 24 GHz frequency-modulated continuous wave radar is used to detect and localize both stationary and moving targets. Depending on the application, the implemented software offers different modes of operation. For example, it can

  3. Limitations and Strengths of the Fourier Transform Method to Detect Accelerating Targets

    Thayaparan, Thayananthan

    2000-01-01

    .... In using a Pulse Doppler Radar to detect a non-accelerating target in additive white Gaussian noise and to estimate its radial velocity, the Fourier method provides an output signal-to-noise ratio (SNR...

  4. Targeted screening strategies to detect Trypanosoma cruzi infection in children.

    Michael Z Levy

    2007-12-01

    Full Text Available Millions of people are infected with Trypanosoma cruzi, the causative agent of Chagas disease in Latin America. Anti-trypanosomal drug therapy can cure infected individuals, but treatment efficacy is highest early in infection. Vector control campaigns disrupt transmission of T. cruzi, but without timely diagnosis, children infected prior to vector control often miss the window of opportunity for effective chemotherapy.We performed a serological survey in children 2-18 years old living in a peri-urban community of Arequipa, Peru, and linked the results to entomologic, spatial and census data gathered during a vector control campaign. 23 of 433 (5.3% [95% CI 3.4-7.9] children were confirmed seropositive for T. cruzi infection by two methods. Spatial analysis revealed that households with infected children were very tightly clustered within looser clusters of households with parasite-infected vectors. Bayesian hierarchical mixed models, which controlled for clustering of infection, showed that a child's risk of being seropositive increased by 20% per year of age and 4% per vector captured within the child's house. Receiver operator characteristic (ROC plots of best-fit models suggest that more than 83% of infected children could be identified while testing only 22% of eligible children.We found evidence of spatially-focal vector-borne T. cruzi transmission in peri-urban Arequipa. Ongoing vector control campaigns, in addition to preventing further parasite transmission, facilitate the collection of data essential to identifying children at high risk of T. cruzi infection. Targeted screening strategies could make integration of diagnosis and treatment of children into Chagas disease control programs feasible in lower-resource settings.

  5. 'Intelligent' triggering methodology for improved detectability of wavelength modulation diode laser absorption spectrometry applied to window-equipped graphite furnaces

    Gustafsson, Joergen; Axner, Ove

    2003-01-01

    The wavelength modulation-diode laser absorption spectrometry (WM-DLAS) technique experiences a limited detectability when window-equipped sample compartments are used because of multiple reflections between components in the optical system (so-called etalon effects). The problem is particularly severe when the technique is used with a window-equipped graphite furnace (GF) as atomizer since the heating of the furnace induces drifts of the thickness of the windows and thereby also of the background signals. This paper presents a new detection methodology for WM-DLAS applied to a window-equipped GF in which the influence of the background signals from the windows is significantly reduced. The new technique, which is based upon a finding that the WM-DLAS background signals from a window-equipped GF are reproducible over a considerable period of time, consists of a novel 'intelligent' triggering procedure in which the GF is triggered at a user-chosen 'position' in the reproducible drift-cycle of the WM-DLAS background signal. The new methodology makes also use of 'higher-than-normal' detection harmonics, i.e. 4f or 6f, since these previously have shown to have a higher signal-to-background ratio than 2f-detection when the background signals originates from thin etalons. The results show that this new combined background-drift-reducing methodology improves the limit of detection of the WM-DLAS technique used with a window-equipped GF by several orders of magnitude as compared to ordinary 2f-detection, resulting in a limit of detection for a window-equipped GF that is similar to that of an open GF

  6. Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar

    Raja Syamsul Azmir Raja Abdullah; Noor Hafizah Abdul Aziz; Nur Emileen Abdul Rashid; Asem Ahmad Salah; Fazirulhisyam Hashim

    2016-01-01

    The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of th...

  7. Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection

    Zhimin Lin

    2017-01-01

    Full Text Available Target image detection based on a rapid serial visual presentation (RSVP paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction. In this paper, we propose a triple-RSVP paradigm with three images being presented simultaneously and a target image appearing three times. Thus, multitrial P300 classification methods can be used to improve detection accuracy. In this study, these mechanisms were extended and validated, and the characteristics of the multi-RSVP framework were further explored. Two different P300 detection algorithms were also utilized in multi-RSVP to demonstrate that the scheme is universally applicable. Results revealed that the detection accuracy of the multi-RSVP paradigm was higher than that of the standard RSVP paradigm. The results validate the effectiveness of the proposed method, and this method can provide a whole new idea in the field of EEG-based target detection.

  8. Detection of Target ssDNA Using a Microfabricated Hall Magnetometer with Correlated Optical Readout

    Steven M. Hira

    2012-01-01

    Full Text Available Sensing biological agents at the genomic level, while enhancing the response time for biodetection over commonly used, optics-based techniques such as nucleic acid microarrays or enzyme-linked immunosorbent assays (ELISAs, is an important criterion for new biosensors. Here, we describe the successful detection of a 35-base, single-strand nucleic acid target by Hall-based magnetic transduction as a mimic for pathogenic DNA target detection. The detection platform has low background, large signal amplification following target binding and can discriminate a single, 350 nm superparamagnetic bead labeled with DNA. Detection of the target sequence was demonstrated at 364 pM (<2 target DNA strands per bead target DNA in the presence of 36 μM nontarget (noncomplementary DNA (<10 ppm target DNA using optical microscopy detection on a GaAs Hall mimic. The use of Hall magnetometers as magnetic transduction biosensors holds promise for multiplexing applications that can greatly improve point-of-care (POC diagnostics and subsequent medical care.

  9. Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

    Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

    2007-11-01

    Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

  10. Improved target detection and bearing estimation utilizing fast orthogonal search for real-time spectral analysis

    Osman, Abdalla; El-Sheimy, Naser; Nourledin, Aboelamgd; Theriault, Jim; Campbell, Scott

    2009-01-01

    The problem of target detection and tracking in the ocean environment has attracted considerable attention due to its importance in military and civilian applications. Sonobuoys are one of the capable passive sonar systems used in underwater target detection. Target detection and bearing estimation are mainly obtained through spectral analysis of received signals. The frequency resolution introduced by current techniques is limited which affects the accuracy of target detection and bearing estimation at a relatively low signal-to-noise ratio (SNR). This research investigates the development of a bearing estimation method using fast orthogonal search (FOS) for enhanced spectral estimation. FOS is employed in this research in order to improve both target detection and bearing estimation in the case of low SNR inputs. The proposed methods were tested using simulated data developed for two different scenarios under different underwater environmental conditions. The results show that the proposed method is capable of enhancing the accuracy for target detection as well as bearing estimation especially in cases of a very low SNR

  11. Multi-modal Intelligent Seizure Acquisition (MISA) system - A new approach towards seizure detection based on full body motion measures

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2009-01-01

    Many epilepsy patients cannot call for help during a seizure, because they are unconscious or because of the affection of their motor system or speech function. This can lead to injuries, medical complications and at worst death. An alarm system setting off at seizure onset could help to avoid...... hazards. Today no reliable alarm systems are available. A Multi-modal Intelligent Seizure Acquisition (MISA) system based on full body motion data seems as a good approach towards detection of epileptic seizures. The system is the first to provide a full body description for epilepsy applications. Three...... test subjects were used for this pilot project. Each subject simulated 15 seizures and in addition performed some predefined normal activities, during a 4-hour monitoring with electromyography (EMG), accelerometer, magnetometer and gyroscope (AMG), electrocardiography (ECG), electroencephalography (EEG...

  12. Results from the search-lidar demonstrator project for detection of small Sea-Surface targets

    Heuvel, J.C. van den; Putten, F.J.M. van; Cohen, L.H.; Kemp, R.A.W.; Franssen, G.C.

    2009-01-01

    Coastal surveillance and naval operations in the littoral both have to deal with the threat of small sea-surface targets. These targets have a low radar cross-section and a low velocity that makes them hard to detect by radar. Typical threats include jet skis, FIAC's, and speedboats. Previous lidar

  13. Search-Lidar Demonstrator for Detection of Small Sea-Surface Targets

    Heuvel, J.C. van den; Bekman, H.H.P.T.; Putten, F.J.M. van; Cohen, L.H.; Schleijpen, H.M.A.

    2008-01-01

    Coastal surveillance and naval operations in the littoral both have to deal with the threat of small sea-surface targets. These targets have a low radar cross-section and a low velocity that makes them hard to detect by radar. Typical threats include jet skis, FIAC’s, and speedboats. Lidar

  14. Detection of Small Sea-Surface Targets with a Search Lidar

    Heuvel, J.C. van den; Bekman, H.H.P.T.; Putten, F.J.M.; Cohen, L.A.

    2007-01-01

    Naval operations in the littoral have to deal with the threat of small sea-surface targets. These targets have a low radar cross-section and low velocity, which makes them hard to detect by radar in the presence of sea clutter. Typical threats include periscopes, jet skies, FIAC’s, and speedboats.

  15. Risk maps for targeting exotic plant pest detection programs in the United States

    R.D. Magarey; D.M. Borchert; J.S. Engle; M Garcia-Colunga; Frank H. Koch; et al

    2011-01-01

    In the United States, pest risk maps are used by the Cooperative Agricultural Pest Survey for spatial and temporal targeting of exotic plant pest detection programs. Methods are described to create standardized host distribution, climate and pathway risk maps for the top nationally ranked exotic pest targets. Two examples are provided to illustrate the risk mapping...

  16. Moving target detection based on temporal-spatial information fusion for infrared image sequences

    Toing, Wu-qin; Xiong, Jin-yu; Zeng, An-jun; Wu, Xiao-ping; Xu, Hao-peng

    2009-07-01

    Moving target detection and localization is one of the most fundamental tasks in visual surveillance. In this paper, through analyzing the advantages and disadvantages of the traditional approaches about moving target detection, a novel approach based on temporal-spatial information fusion is proposed for moving target detection. The proposed method combines the spatial feature in single frame and the temporal properties within multiple frames of an image sequence of moving target. First, the method uses the spatial image segmentation for target separation from background and uses the local temporal variance for extracting targets and wiping off the trail artifact. Second, the logical "and" operator is used to fuse the temporal and spatial information. In the end, to the fusion image sequence, the morphological filtering and blob analysis are used to acquire exact moving target. The algorithm not only requires minimal computation and memory but also quickly adapts to the change of background and environment. Comparing with other methods, such as the KDE, the Mixture of K Gaussians, etc., the simulation results show the proposed method has better validity and higher adaptive for moving target detection, especially in infrared image sequences with complex illumination change, noise change, and so on.

  17. Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

    Xin, L.

    2018-04-01

    Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

  18. Detection and localization of multiple short range targets using FMCW radar signal

    Jardak, Seifallah

    2016-07-26

    In this paper, a 24 GHz frequency-modulated continuous wave radar is used to detect and localize both stationary and moving targets. Depending on the application, the implemented software offers different modes of operation. For example, it can simply output raw data samples for advanced offline processing or directly carry out a two dimensional fast Fourier transform to estimate the location and velocity of multiple targets. To suppress clutter and detect only moving targets, two methods based on the background reduction and the slow time processing techniques are implemented. A trade-off between the two methods is presented based on their performance and the required processing time. © 2016 IEEE.

  19. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    Sukarno, Pudjo; Sidarto, Kuntjoro Adji; Trisnobudi, Amoranto; Setyoadi, Delint Ira; Rohani, Nancy; Darmadi, Darmadi

    2007-01-01

    Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained ...

  20. Vision based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems

    Møgelmose, Andreas; Trivedi, Mohan M.; Moeslund, Thomas B.

    2012-01-01

    In this paper, we provide a survey of the traffic sign detection literature, detailing detection systems for Traffic Sign Recognition (TSR) for driver assistance. We separately describe the contributions of recent works to the various stages inherent in traffic sign detection: segmentation, feature...... extraction, and final sign detection. While TSR is a well-established research area, we highlight open research issues in the literature, including a dearth of use of publicly-available image databases, and the over-representation of European traffic signs. Further, we discuss future directions for TSR...

  1. Diagnostic imaging strategy for MDCT- or MRI-detected breast lesions: use of targeted sonography

    Nakano, Satoko; Ohtsuka, Masahiko; Mibu, Akemi; Karikomi, Masato; Sakata, Hitomi; Yamamoto, Masahiro

    2012-01-01

    Leading-edge technology such as magnetic resonance imaging (MRI) or computed tomography (CT) often reveals mammographically and ultrasonographically occult lesions. MRI is a well-documented, effective tool to evaluate these lesions; however, the detection rate of targeted sonography varies for MRI detected lesions, and its significance is not well established in diagnostic strategy of MRI detected lesions. We assessed the utility of targeted sonography for multidetector-row CT (MDCT)- or MRI-detected lesions in practice. We retrospectively reviewed 695 patients with newly diagnosed breast cancer who were candidates for breast conserving surgery and underwent MDCT or MRI in our hospital between January 2004 and March 2011. Targeted sonography was performed in all MDCT- or MRI-detected lesions followed by imaging-guided biopsy. Patient background, histopathology features and the sizes of the lesions were compared among benign, malignant and follow-up groups. Of the 695 patients, 61 lesions in 56 patients were detected by MDCT or MRI. The MDCT- or MRI-detected lesions were identified by targeted sonography in 58 out of 61 lesions (95.1%). Patients with pathological diagnoses were significantly older and more likely to be postmenopausal than the follow-up patients. Pathological diagnosis proved to be benign in 20 cases and malignant in 25. The remaining 16 lesions have been followed up. Lesion size and shape were not significantly different among the benign, malignant and follow-up groups. Approximately 95% of MDCT- or MRI-detected lesions were identified by targeted sonography, and nearly half of these lesions were pathologically proven malignancies in this study. Targeted sonography is a useful modality for MDCT- or MRI-detected breast lesions

  2. Abnormal Ventral and Dorsal Attention Network Activity During Single and Dual Target Detection in Schizophrenia

    Amy M. Jimenez

    2016-03-01

    Full Text Available Early visual perception and attention are impaired in schizophrenia, and these deficits can be observed on target detection tasks. These tasks activate distinct ventral and dorsal brain networks which support stimulus-driven and goal-directed attention, respectively. We used single and dual target rapid serial visual presentation (RSVP tasks during fMRI with an ROI approach to examine regions within these networks associated with target detection and the attentional blink (AB in 21 schizophrenia outpatients and 25 healthy controls. In both tasks, letters were targets and numbers were distractors. For the dual target task, the second target (T2 was presented at 3 different lags after the first target (T1 (lag1=100ms, lag3=300ms, lag7=700ms. For both single and dual target tasks, patients identified fewer targets than controls. For the dual target task, both groups showed the expected AB effect with poorer performance at lag 3 than at lags 1 or 7, and there was no group by lag interaction. During the single target task, patients showed abnormally increased deactivation of the temporo-parietal junction (TPJ, a key region of the ventral network. When attention demands were increased during the dual target task, patients showed overactivation of the posterior intraparietal cortex, a key dorsal network region, along with failure to deactivate TPJ. Results suggest inefficient and faulty suppression of salience-oriented processing regions, resulting in increased sensitivity to stimuli in general, and difficulty distinguishing targets from non-targets.

  3. Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

    Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

    2017-06-01

    The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

  4. A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments.

    Al-Nawashi, Malek; Al-Hazaimeh, Obaida M; Saraee, Mohamad

    2017-01-01

    Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

  5. MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

    Chen, Shifu; Huang, Tanxiao; Wen, Tiexiang; Li, Hong; Xu, Mingyan; Gu, Jia

    2018-01-22

    Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data. We developed MutScan to address problems of sensitive detection and efficient validation for target mutations. MutScan involves highly optimized string-searching algorithms, which can scan input FASTQ files to grab all reads that support target mutations. The collected supporting reads for each target mutation will be piled up and visualized using web technologies such as HTML and JavaScript. Algorithms such as rolling hash and bloom filter are applied to accelerate scanning and make MutScan applicable to detect or visualize target mutations in a very fast way. MutScan is a tool for the detection and visualization of target mutations by only scanning FASTQ raw data directly. Compared to conventional pipelines, this offers a very high performance, executing about 20 times faster, and offering maximal sensitivity since it can grab mutations with even one single supporting read. MutScan visualizes detected mutations by generating interactive pile-ups using web technologies. These can serve to validate target mutations, thus avoiding false positives. Furthermore, MutScan can visualize all mutation records in a VCF file to HTML pages for cloud-friendly VCF validation. MutScan is an open source tool available at GitHub: https://github.com/OpenGene/MutScan.

  6. Comparing detection and disclosure of traffic incidents in social networks: an intelligent approach based on Twitter vs. Waze

    Sebastián Vallejos

    2018-03-01

    Full Text Available Nowadays, social networks have become  in a  communication  medium widely  used to disseminate any type  of  information. In  particular,  the  shared  information  in  social  networks  usually  includes  a  considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of  topics  are  communicated.  In this  context,  Twitter  is  a  case  in  point  of  a  generic  social  network  in  which  its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos  Aires  (CABA,  Argentina,  as  the  region  of  interest.  The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents.

  7. Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

    Wei-Chien Wang

    2018-05-01

    Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

  8. New diagnostics for melanoma detection: from artificial intelligence to RNA microarrays.

    Ahlgrimm-Siess, Verena; Laimer, Martin; Arzberger, Edith; Hofmann-Wellenhof, Rainer

    2012-07-01

    Early detection of melanoma remains crucial to ensuring a favorable prognosis. Dermoscopy and total body photography are well-established noninvasive aids that increase the diagnostic accuracy of dermatologists in their daily routine, beyond that of a naked-eye examination. New noninvasive diagnostic techniques, such as reflectance confocal microscopy, multispectral digital imaging and RNA microarrays, are currently being investigated to determine their utility for melanoma detection. This review presents emerging technologies for noninvasive melanoma diagnosis, and discusses their advantages and limitations.

  9. Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

    Yunfei Guo

    2012-04-01

    Full Text Available In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD called penalty DP-TBD (PDP-TBD is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

  10. Golay Complementary Waveforms in Reed–Müller Sequences for Radar Detection of Nonzero Doppler Targets

    Wang, Xuezhi; Huang, Xiaotao; Suvorova, Sofia; Moran, Bill

    2018-01-01

    Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods. PMID:29324708

  11. Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models

    Byung‐Kwan Kim

    2018-04-01

    Full Text Available This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

  12. High-resolution remotely sensed small target detection by imitating fly visual perception mechanism.

    Huang, Fengchen; Xu, Lizhong; Li, Min; Tang, Min

    2012-01-01

    The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

  13. Design and implement of infrared small target real-time detection system based on pipeline technology

    Sun, Lihui; Wang, Yongzhong; He, Yongqiang

    2007-01-01

    The detection for motive small target in infrared image sequence has become a hot topic nowadays. Background suppress algorithm based on minim gradient median filter and temporal recursion target detection algorithm are introduced. On the basis of contents previously mentioned, a four stages pipeline structure infrared small target detection process system, which aims at characters of algorithm complexity, large amounts of data to process, high frame frequency and exigent real-time character in this kind of application, is designed and implemented. The logical structure of the system was introduced and the function and signals flows are programmed. The system is composed of two FPGA chips and two DSP chips of TI. According to the function of each part, the system is divided into image preprocess stage, target detection stage, track relation stage and image output stage. The experiment of running algorithms on the system presented in this paper proved that the system could meet acquisition and process of 50Hz 240x320 digital image and the system could real time detect small target with a signal-noise ratio more than 3 reliably. The system achieves the characters of large amount of memory, high real-time processing, excellent extension and favorable interactive interface.

  14. High-Resolution Remotely Sensed Small Target Detection by Imitating Fly Visual Perception Mechanism

    Fengchen Huang

    2012-01-01

    Full Text Available The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

  15. A strategy to objectively evaluate the necessity of correcting detected target deviations in image guided radiotherapy

    Yue, Ning J.; Kim, Sung; Jabbour, Salma; Narra, Venkat; Haffty, Bruce G.

    2007-01-01

    Image guided radiotherapy technologies are being increasingly utilized in the treatment of various cancers. These technologies have enhanced the ability to detect temporal and spatial deviations of the target volume relative to planned radiation beams. Correcting these detected deviations may, in principle, improve the accuracy of dose delivery to the target. However, in many situations, a clinical decision has to be made as to whether it is necessary to correct some of the deviations since the relevant dosimetric impact may or may not be significant, and the corresponding corrective action may be either impractical or time consuming. Ideally this decision should be based on objective and reproducible criteria rather than subjective judgment. In this study, a strategy is proposed for the objective evaluation of the necessity of deviation correction during the treatment verification process. At the treatment stage, without any alteration from the planned beams, the treatment beams should provide the desired dose coverage to the geometric volume identical to the planning target volume (PTV). Given this fact, the planned dose distribution and PTV geometry were used to compute the dose coverage and PTV enclosure of the clinical target volume (CTV) that was detected from imaging during the treatment setup verification. The spatial differences between the detected CTV and the planning CTV are essentially the target deviations. The extent of the PTV enclosure of the detected CTV as well as its dose coverage were used as criteria to evaluate the necessity of correcting any of the target deviations. This strategy, in principle, should be applicable to any type of target deviations, including both target deformable and positional changes and should be independent of how the deviations are detected. The proposed strategy was used on two clinical prostate cancer cases. In both cases, gold markers were implanted inside the prostate for the purpose of treatment setup

  16. Automatic Detection of Microcalcifications in a Digital Mammography Using Artificial Intelligence Techniques

    Carlos A. Madrigal-González

    2013-11-01

    Full Text Available Breast cancer is one of the cancers that has a higher mortality rate among women and early detection increases the possibilities of cure, so its early detection is one of the best treatments for this serious disease. Microcalcifications are a type of lesion in the breast and its presence is highly correlated with the presence of cancer. In this paper we present a method for automatic detection of microcalcifications using digital image processing using a Gaussian filtering approach, which can enhance the contrast between microcalcifications and normal tissue present in a mammography, then apply a local thresholding algorithm witch allow the identification of suspicious microcalcifications. The classifier used to determine the degree of benign or malignant microcalcifications is the K-Nearest Neighbours (KNN and the validation of the results was done using ROC curves.

  17. Study on moving target detection to passive radar based on FM broadcast transmitter

    2007-01-01

    Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field.First of all,direct-path interference(DPI)suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation(FM) broadcast transmitter is analyzed in this article;Secondly,a space-time-frequency domain synthetic solution to this problem is introduced:Adaptive nulling array processing is considered in the space domain,DPI cancellation based on adaptive fractional delay interpolation(AFDI)technique is used in planned time domain,and long-time coherent integration is utilized in the frequency domain;Finally,an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator,Simulation results by real collected data show that the proposed method has a better performance of moving target detection.

  18. Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

    Xuwang Zhang

    2018-01-01

    Full Text Available This paper proposes a spectrum zoom processing based target detection algorithm for detecting the weak echo of low-altitude and slow-speed small (LSS targets in heavy ground clutter environments, which can be used to retrofit the existing radar systems. With the existing range-Doppler frequency images, the proposed method firstly concatenates the data from the same Doppler frequency slot of different images and then applies the spectrum zoom processing. After performing the clutter suppression, the target detection can be finally implemented. Through the theoretical analysis and real data verification, it is shown that the proposed algorithm can obtain a preferable spectrum zoom result and improve the signal-to-clutter ratio (SCR with a very low computational load.

  19. Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification

    Wan, Weibing; Yuan, Lingfeng; Zhao, Qunfei; Fang, Tao

    2018-01-01

    Saliency detection has been applied to the target acquisition case. This paper proposes a two-dimensional hidden Markov model (2D-HMM) that exploits the hidden semantic information of an image to detect its salient regions. A spatial pyramid histogram of oriented gradient descriptors is used to extract features. After encoding the image by a learned dictionary, the 2D-Viterbi algorithm is applied to infer the saliency map. This model can predict fixation of the targets and further creates robust and effective depictions of the targets' change in posture and viewpoint. To validate the model with a human visual search mechanism, two eyetrack experiments are employed to train our model directly from eye movement data. The results show that our model achieves better performance than visual attention. Moreover, it indicates the plausibility of utilizing visual track data to identify targets.

  20. Implementation Of Vision-Based Landing Target Detection For VTOL UAV Using Raspberry Pi

    Ei Ei Nyein

    2017-04-01

    Full Text Available This paper presents development and implementation of a real-time vision-based landing system for VTOL UAV. We use vision for precise target detection and recognition. A UAV is equipped with the onboard raspberry pi camera to take images and raspberry pi platform to operate the image processing techniques. Today image processing is used for various applications in this paper it is used for landing target extraction. And vision system is also used for take-off and landing function in VTOL UAV. Our landing target design is used as the helipad H shape. Firstly the image is captured to detect the target by the onboard camera. Next the capture image is operated in the onboard processor. Finally the alert sound signal is sent to the remote control RC for landing VTOL UAV. The information obtained from vision system is used to navigate a safe landing. The experimental results from real tests are presented.

  1. Location detection and tracking of moving targets by a 2D IR-UWB radar system.

    Nguyen, Van-Han; Pyun, Jae-Young

    2015-03-19

    In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

  2. Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System

    Van-Han Nguyen

    2015-03-01

    Full Text Available In indoor environments, the Global Positioning System (GPS and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

  3. Stochastic pattern recognition techniques and artificial intelligence for nuclear power plant surveillance and anomaly detection

    Kemeny, L.G

    1998-12-31

    In this paper a theoretical and system conceptual model is outlined for the instrumentation, core assessment and surveillance and anomaly detection of a nuclear power plant. The system specified is based on the statistical on-line analysis of optimally placed instrumentation sensed fluctuating signals in terms of such variates as coherence, correlation function, zero-crossing and spectral density

  4. Stochastic pattern recognition techniques and artificial intelligence for nuclear power plant surveillance and anomaly detection

    Kemeny, L.G.

    1998-01-01

    In this paper a theoretical and system conceptual model is outlined for the instrumentation, core assessment and surveillance and anomaly detection of a nuclear power plant. The system specified is based on the statistical on-line analysis of optimally placed instrumentation sensed fluctuating signals in terms of such variates as coherence, correlation function, zero-crossing and spectral density

  5. Infrared maritime target detection using the high order statistic filtering in fractional Fourier domain

    Zhou, Anran; Xie, Weixin; Pei, Jihong

    2018-06-01

    Accurate detection of maritime targets in infrared imagery under various sea clutter conditions is always a challenging task. The fractional Fourier transform (FRFT) is the extension of the Fourier transform in the fractional order, and has richer spatial-frequency information. By combining it with the high order statistic filtering, a new ship detection method is proposed. First, the proper range of angle parameter is determined to make it easier for the ship components and background to be separated. Second, a new high order statistic curve (HOSC) at each fractional frequency point is designed. It is proved that maximal peak interval in HOSC reflects the target information, while the points outside the interval reflect the background. And the value of HOSC relative to the ship is much bigger than that to the sea clutter. Then, search the curve's maximal target peak interval and extract the interval by bandpass filtering in fractional Fourier domain. The value outside the peak interval of HOSC decreases rapidly to 0, so the background is effectively suppressed. Finally, the detection result is obtained by the double threshold segmenting and the target region selection method. The results show the proposed method is excellent for maritime targets detection with high clutters.

  6. Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

    Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing

    2018-03-01

    In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

  7. Vision models for target detection and recognition in memory of Arthur Menendez

    Peli, Eli

    1995-01-01

    This book is an international collection of contributions from academia, industry and the armed forces. It addresses current and emerging Spatial Vision Models and their application to the understanding, prediction and evaluation of the tasks of target detection and recognition. The discussion in many of the chapters is framed in terms of military targets and military vision aids. However, the techniques analyses and problems are by no means limited to this area of application. The detection and recognition of an armored vehicle from a reconnaissance image are performed by the same visual syst

  8. Directional support value of Gaussian transformation for infrared small target detection.

    Yang, Changcai; Ma, Jiayi; Qi, Shengxiang; Tian, Jinwen; Zheng, Sheng; Tian, Xin

    2015-03-20

    Robust small target detection is one of the key techniques in IR search and tracking systems for self-defense or attacks. In this paper we present a robust solution for small target detection in a single IR image. The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate. The original image is decomposed into sub-bands in different orientations by convolving the image with the directional support value filters, which are deduced from the weighted mapped least-squares-support vector machines (LS-SVMs). Based on the sub-band images, a support value of Gaussian matrix is constructed, and the trace of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed for enhancing target signal while suppressing the background clutter. We demonstrate the advantages of the proposed method on real IR images and compare the results against those obtained from standard detection approaches, including the top-hat filter, max-mean filter, max-median filter, min-local-Laplacian of Gaussian (LoG) filter, as well as LS-SVM. The experimental results on various cluttered background images show that the proposed method outperforms other detectors.

  9. Simulation Research Framework with Embedded Intelligent Algorithms for Analysis of Multi-Target, Multi-Sensor, High-Cluttered Environments

    Hanlon, Nicholas P.

    nearly identical performance metrics at orders of magnitude faster in execution. Second, a fuzzy inference system is presented that alleviates air traffic controllers from information overload by utilizing flight plan data and radar/GPS correlation values to highlight aircraft that deviate from their intended routes. Third, a genetic algorithm optimizes sensor placement that is robust and capable of handling unexpected routes in the environment. Fourth, a fuzzy CUSUM algorithm more accurately detects and corrects aircraft mode changes. Finally, all the work is packaged in a holistic simulation research framework that provides evaluation and analysis of various multi-sensor, multi-target scenarios.

  10. Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection

    Naveen Verma

    2011-04-01

    Full Text Available Intelligent biomedical devices implies systems that are able to detect specific physiological processes in patients so that particular responses can be generated. This closed-loop capability can have enormous clinical value when we consider the unprecedented modalities that are beginning to emerge for sensing and stimulating patient physiology. Both delivering therapy (e.g., deep-brain stimulation, vagus nerve stimulation, etc. and treating impairments (e.g., neural prosthesis requires computational devices that can make clinically relevant inferences, especially using minimally-intrusive patient signals. The key to such devices is algorithms that are based on data-driven signal modeling as well as hardware structures that are specialized to these. This paper discusses the primary application-domain challenges that must be overcome and analyzes the most promising methods for this that are emerging. We then look at how these methods are being incorporated in ultra-low-energy computational platforms and systems. The case study for this is a seizure-detection SoC that includes instrumentation and computation blocks in support of a system that exploits patient-specific modeling to achieve accurate performance for chronic detection. The SoC samples each EEG channel at a rate of 600 Hz and performs processing to derive signal features on every two second epoch, consuming 9 μJ/epoch/channel. Signal feature extraction reduces the data rate by a factor of over 40×, permitting wireless communication from the patient’s head while reducing the total power on the head by 14×.

  11. Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar

    Raja Syamsul Azmir Raja Abdullah

    2016-09-01

    Full Text Available The passive bistatic radar (PBR system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR. The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system’s capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.

  12. Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar.

    Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

    2016-09-29

    The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.

  13. In situ targeted MRI detection of Helicobacter pylori with stable magnetic graphitic nanocapsules

    Li, Yunjie; Hu, Xiaoxiao; Ding, Ding; Zou, Yuxiu; Xu, Yiting; Wang, Xuewei; Zhang, Yin; Chen, Long; Chen, Zhuo; Tan, Weihong

    2017-06-01

    Helicobacter pylori infection is implicated in the aetiology of many diseases. Despite numerous studies, a painless, fast and direct method for the in situ detection of H. pylori remains a challenge, mainly due to the strong acidic/enzymatic environment of the gastric mucosa. Herein, we report the use of stable magnetic graphitic nanocapsules (MGNs), for in situ targeted magnetic resonance imaging (MRI) detection of H. pylori. Several layers of graphene as the shell effectively protect the magnetic core from corrosion while retaining the superior contrast effect for MRI in the gastric environment. Boronic-polyethylene glycol molecules were synthesized and modified on the MGN surface for targeted MRI detection. In a mouse model of H. pylori-induced infection, H. pylori was specifically detected through both T2-weighted MR imaging and Raman gastric mucosa imaging using functionalized MGNs. These results indicated that enhancement of MRI using MGNs may be a promising diagnostic and bioimaging platform for very harsh conditions.

  14. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

    Philippe Barboza

    Full Text Available The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI Early Alerting and Reporting (EAR project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8. This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

  15. Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

    Barboza, Philippe; Vaillant, Laetitia; Mawudeku, Abla; Nelson, Noele P.; Hartley, David M.; Madoff, Lawrence C.; Linge, Jens P.; Collier, Nigel; Brownstein, John S.; Yangarber, Roman; Astagneau, Pascal; on behalf of the Early Alerting, Reporting Project of the Global Health Security Initiative

    2013-01-01

    The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users. PMID:23472077

  16. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

    Barboza, Philippe; Vaillant, Laetitia; Mawudeku, Abla; Nelson, Noele P; Hartley, David M; Madoff, Lawrence C; Linge, Jens P; Collier, Nigel; Brownstein, John S; Yangarber, Roman; Astagneau, Pascal

    2013-01-01

    The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

  17. Usefulness of thin slice target CT scan in detecting mediastinal and hilar lymphadenopathy

    Yoshida, Shoji; Maeda, Tomoho; Nishioka, Masatoshi

    1986-01-01

    Comparative study of target scan with the different slice thickness and scan modes was performed to evaluate the mediastinal and hilar lymphadenopathy. 20 cases in controls and 35 cases in lymphadenopathy were examined. To delineate mediastinal and hilar lymphadenopathy, the scan mode of standard target was most useful in contrast and sharpness. Thin slice thickness with 5 mm was necessary in detecting small lymphnode or contour and internal structure of enlarged lymphnode. Valuable estimation of 5 mm contiguous target scan was obtained in the subaortic node (no. 5), tracheobronchial node (no. 4), precarinal and subcarinal node (no. 7) and right hilar node (no. 12). (author)

  18. NMR parallel Q-meter with double-balanced-mixer detection for polarized target experiments

    Boissevain, J.; Tippens, W.B.

    1983-01-01

    A constant-voltage, parallel-tuned nuclear magnetic resonance (NMR) circuit, patterned after a Liverpool design, has been developed for polarized target experiments. Measuring the admittance of the resonance circuit allows advantageous use of double-balanced mixer detection. The resonant circuit is tolerant of stray capacitance between the NMR coil and the target cavity, thus easing target-cell-design constraints. The reference leg of the circuit includes a voltage-controlled attenuator and phase shifter for ease of tuning. The NMR output features a flat background and has good linearity and stability

  19. Transductive and matched-pair machine learning for difficult target detection problems

    Theiler, James

    2014-06-01

    This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

  20. Detection of genetically modified organisms (GMOs) using isothermal amplification of target DNA sequences.

    Lee, David; La Mura, Maurizio; Allnutt, Theo R; Powell, Wayne

    2009-02-02

    The most common method of GMO detection is based upon the amplification of GMO-specific DNA amplicons using the polymerase chain reaction (PCR). Here we have applied the loop-mediated isothermal amplification (LAMP) method to amplify GMO-related DNA sequences, 'internal' commonly-used motifs for controlling transgene expression and event-specific (plant-transgene) junctions. We have tested the specificity and sensitivity of the technique for use in GMO studies. Results show that detection of 0.01% GMO in equivalent background DNA was possible and dilutions of template suggest that detection from single copies of the template may be possible using LAMP. This work shows that GMO detection can be carried out using LAMP for routine screening as well as for specific events detection. Moreover, the sensitivity and ability to amplify targets, even with a high background of DNA, here demonstrated, highlights the advantages of this isothermal amplification when applied for GMO detection.

  1. Spatial statistical analysis of organs for intelligent CAD and its application to disease detection

    Takizawa, Hotaka

    2009-01-01

    The present article reports our research that was performed in a research project supported by a Grantin-Aid for Scientific Research on Priority Area from the Ministry of Education, Culture Sports, Science and Technology, JAPAN, from 2003 to 2006. Our method developed in the research acquired the trend of variation of spatial relations between true diseases, false positives and image features through statistical analysis of a set of medical images and improved the accuracy of disease detection by predicting their occurrence positions in an image based on the trend. This article describes the formulation of the method in general form and shows the results obtained by applying the method to chest X-ray CT images for detection of pulmonary nodules. (author)

  2. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    Pudjo Sukarno

    2007-05-01

    Full Text Available Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields.

  3. Quantifying Human Performance of a Dynamic Military Target Detection Task: An Application of the Theory of Signal Detection.

    1995-06-01

    applied to analyze numerous experimental tasks (Macmillan and Creelman , 1991). One of these tasks, target detection, is the subject research. In...between each associated pair of false alarm rate and hit rate z-scores is d’ for the bias level associated with the pairing (Macmillan and Creelman , 1991...unequal variance in normal distributions (Macmillan and Creelman , 1991). 61 1966). It is described in detail for the interested reader by Green and

  4. Detection of atherosclerotic lesions and intimal macrophages using CD36-targeted nanovesicles.

    Nie, Shufang; Zhang, Jia; Martinez-Zaguilan, Raul; Sennoune, Souad; Hossen, Md Nazir; Lichtenstein, Alice H; Cao, Jun; Meyerrose, Gary E; Paone, Ralph; Soontrapa, Suthipong; Fan, Zhaoyang; Wang, Shu

    2015-12-28

    Current approaches to the diagnosis and therapy of atherosclerosis cannot target lesion-determinant cells in the artery wall. Intimal macrophage infiltration promotes atherosclerotic lesion development by facilitating the accumulation of oxidized low-density lipoproteins (oxLDL) and increasing inflammatory responses. The presence of these cells is positively associated with lesion progression, severity and destabilization. Hence, they are an important diagnostic and therapeutic target. The objective of this study was to noninvasively assess the distribution and accumulation of intimal macrophages using CD36-targeted nanovesicles. Soy phosphatidylcholine was used to synthesize liposome-like nanovesicles. 1-(Palmitoyl)-2-(5-keto-6-octene-dioyl) phosphatidylcholine was incorporated on their surface to target the CD36 receptor. All in vitro data demonstrate that these targeted nanovesicles had a high binding affinity for the oxLDL binding site of the CD36 receptor and participated in CD36-mediated recognition and uptake of nanovesicles by macrophages. Intravenous administration into LDL receptor null mice of targeted compared to non-targeted nanovesicles resulted in higher uptake in aortic lesions. The nanovesicles co-localized with macrophages and their CD36 receptors in aortic lesions. This molecular target approach may facilitate the in vivo noninvasive imaging of atherosclerotic lesions in terms of intimal macrophage accumulation and distribution and disclose lesion features related to inflammation and possibly vulnerability thereby facilitate early lesion detection and targeted delivery of therapeutic compounds to intimal macrophages. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

    Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

    2008-03-01

    In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

  6. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

    P. Amudha

    2015-01-01

    Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

  7. Myth of the Master Detective: Reliability of Interpretations for Kaufman's "Intelligent Testing" Approach to the WISC-III.

    Macmann, Gregg M.; Barnett, David W.

    1997-01-01

    Used computer simulation to examine the reliability of interpretations for Kaufman's "intelligent testing" approach to the Wechsler Intelligence Scale for Children (3rd ed.) (WISC-III). Findings indicate that factor index-score differences and other measures could not be interpreted with confidence. Argues that limitations of IQ testing…

  8. Detecting ship targets in spaceborne infrared image based on modeling radiation anomalies

    Wang, Haibo; Zou, Zhengxia; Shi, Zhenwei; Li, Bo

    2017-09-01

    Using infrared imaging sensors to detect ship target in the ocean environment has many advantages compared to other sensor modalities, such as better thermal sensitivity and all-weather detection capability. We propose a new ship detection method by modeling radiation anomalies for spaceborne infrared image. The proposed method can be decomposed into two stages, where in the first stage, a test infrared image is densely divided into a set of image patches and the radiation anomaly of each patch is estimated by a Gaussian Mixture Model (GMM), and thereby target candidates are obtained from anomaly image patches. In the second stage, target candidates are further checked by a more discriminative criterion to obtain the final detection result. The main innovation of the proposed method is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous patches among complex background. The experimental result on short wavelength infrared band (1.560 - 2.300 μm) and long wavelength infrared band (10.30 - 12.50 μm) of Landsat-8 satellite shows the proposed method achieves a desired ship detection accuracy with higher recall than other classical ship detection methods.

  9. Added value of second biopsy target in screen-detected widespread suspicious breast calcifications.

    Falkner, Nathalie M; Hince, Dana; Porter, Gareth; Dessauvagie, Ben; Jeganathan, Sanjay; Bulsara, Max; Lo, Glen

    2018-06-01

    There is controversy on the optimal work-up of screen-detected widespread breast calcifications: whether to biopsy a single target or multiple targets. This study evaluates agreement between multiple biopsy targets within the same screen-detected widespread (≥25 mm) breast calcification to determine if the second biopsy adds value. Retrospective observational study of women screened in a statewide general population risk breast cancer mammographic screening program from 2009 to 2016. Screening episodes recalled for widespread calcifications where further views indicated biopsy, and two or more separate target areas were sampled within the same lesion were included. Percentage agreement and Cohen's Kappa were calculated. A total of 293317 women were screened during 761124 separate episodes with recalls for widespread calcifications in 2355 episodes. In 171 women, a second target was biopsied within the same lesion. In 149 (86%) cases, the second target biopsy result agreed with the first biopsy (κ = 0.6768). Agreement increased with increasing mammography score (85%, 86% and 92% for score 3, 4 and 5 lesions). Same day multiple biopsied lesions were three times more likely to yield concordant results compared to post-hoc second target biopsy cases. While a single target biopsy is sufficient to discriminate a benign vs. malignant diagnosis in most cases, in 14% there is added value in performing a second target biopsy. Biopsies performed prospectively are more likely to yield concordant results compared to post-hoc second target biopsy cases, suggesting a single prospective biopsy may be sufficient when results are radiological-pathological concordant; discordance still requires repeat sampling. © 2018 The Royal Australian and New Zealand College of Radiologists.

  10. Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

    Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

    2014-05-27

    It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

  11. Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

    Zheng-Zhou Li

    2014-05-01

    Full Text Available It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn’t be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

  12. Discrepancy analysis between crystallized and fluid intelligence tests: a novel method to detect mild cognitive impairment in patients with asymptomatic carotid artery stenosis.

    Takaiwa, A; Kuwayama, N; Akioka, N; Kashiwazaki, D; Kuroda, S

    2018-02-01

    The present study was conducted to accurately determine the presence of mild cognitive impairment, which is often difficult to evaluate using only simple tests. Our approach focused on discrepancy analysis of fluid intelligence relative to crystallized intelligence using internationally recognized neuropsychological tests. One-hundred and five patients diagnosed with asymptomatic carotid artery stenosis were assessed. The neuropsychological tests included the two subtests (information and picture completion) of Wechsler Adult Intelligence Scale-Revised (WAIS-R-two-subtests): crystallized intelligence tests and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (immediate memory, visuospatial/constructional, language, attention, delayed memory and total score) as fluid intelligence tests. Discrepancy analysis was used to assess cognitive impairment. The score for RBANS was subtracted from the score for WAIS-R-two-subtests, and if the score difference was greater than the 5% confidence limit for statistical significance, it was defined as a decline in cognitive function. The WAIS-R-two-subsets was within normal limits when compared with the standardized values. However, all RBANS domains showed significant declines. Frequencies of decline in each RBANS domain were as follows: 69 patients (66%) in immediate memory, 26 (25%) in visuospatial/constructional, 54 (51%) in language, 63 (60%) in attention, 54 (51%) in delayed memory and 78 (74%) in the total score. Moreover, 99 patients (94%) showed decline in at least one RBANS domain. Cognitive function is only preserved in a few patients with asymptomatic carotid artery stenosis. Mild cognitive impairment can be precisely detected by performing the discrepancy analysis between crystallized and fluid intelligence tests. © 2017 EAN.

  13. The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health.

    Mykhalovskiy, Eric; Weir, Lorna

    2006-01-01

    The recent SARS epidemic has renewed widespread concerns about the global transmission of infectious diseases. In this commentary, we explore novel approaches to global infectious disease surveillance through a focus on an important Canadian contribution to the area--the Global Public Health Intelligence Network (GPHIN). GPHIN is a cutting-edge initiative that draws on the capacity of the Internet and newly available 24/7 global news coverage of health events to create a unique form of early warning outbreak detection. This commentary outlines the operation and development of GPHIN and compares it to ProMED-mail, another Internet-based approach to global health surveillance. We argue that GPHIN has created an important shift in the relationship of public health and news information. By exiting the pyramid of official reporting, GPHIN has created a new monitoring technique that has disrupted national boundaries of outbreak notification, while creating new possibilities for global outbreak response. By incorporating news within the emerging apparatus of global infectious disease surveillance, GPHIN has effectively responded to the global media's challenge to official country reporting of outbreak and enhanced the effectiveness and credibility of international public health.

  14. Design of hygrothermal detection and control intelligent system based on AVR-MCU in radon chamber

    Zheng Yongming; Fang Fang; Zhou Wei; Zheng Meiyang; Xu Jianyi

    2006-01-01

    The design of a new hygrothermal detection and control system based on AVR-MCU, which is used in minitype and medium-sized radon chamber, is introduced. The kernel of the interface among ATmega128 MCU, hygrothermal sensor, refrigeration and desiccation components is described. In addition, with the calculation of the control capability in theory, it comes to the conclusion that the design is feasible, and this control system not only can work in independence, but also can cooperate with PC by RS232 communication. (authors)

  15. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    Antonio Plaza

    2010-01-01

    Full Text Available Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  16. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    Paz Abel

    2010-01-01

    Full Text Available Abstract Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  17. Tensor Fukunaga-Koontz transform for small target detection in infrared images

    Liu, Ruiming; Wang, Jingzhuo; Yang, Huizhen; Gong, Chenglong; Zhou, Yuanshen; Liu, Lipeng; Zhang, Zhen; Shen, Shuli

    2016-09-01

    Infrared small targets detection plays a crucial role in warning and tracking systems. Some novel methods based on pattern recognition technology catch much attention from researchers. However, those classic methods must reshape images into vectors with the high dimensionality. Moreover, vectorizing breaks the natural structure and correlations in the image data. Image representation based on tensor treats images as matrices and can hold the natural structure and correlation information. So tensor algorithms have better classification performance than vector algorithms. Fukunaga-Koontz transform is one of classification algorithms and it is a vector version method with the disadvantage of all vector algorithms. In this paper, we first extended the Fukunaga-Koontz transform into its tensor version, tensor Fukunaga-Koontz transform. Then we designed a method based on tensor Fukunaga-Koontz transform for detecting targets and used it to detect small targets in infrared images. The experimental results, comparison through signal-to-clutter, signal-to-clutter gain and background suppression factor, have validated the advantage of the target detection based on the tensor Fukunaga-Koontz transform over that based on the Fukunaga-Koontz transform.

  18. Fluorescence turn-on detection of target sequence DNA based on silicon nanodot-mediated quenching.

    Zhang, Yanan; Ning, Xinping; Mao, Guobin; Ji, Xinghu; He, Zhike

    2018-05-01

    We have developed a new enzyme-free method for target sequence DNA detection based on the dynamic quenching of fluorescent silicon nanodots (SiNDs) toward Cy5-tagged DNA probe. Fascinatingly, the water-soluble SiNDs can quench the fluorescence of cyanine (Cy5) in Cy5-tagged DNA probe in homogeneous solution, and the fluorescence of Cy5-tagged DNA probe can be restored in the presence of target sequence DNA (the synthetic target miRNA-27a). Based on this phenomenon, a SiND-featured fluorescent sensor has been constructed for "turn-on" detection of the synthetic target miRNA-27a for the first time. This newly developed approach possesses the merits of low cost, simple design, and convenient operation since no enzymatic reaction, toxic reagents, or separation procedures are involved. The established method achieves a detection limit of 0.16 nM, and the relative standard deviation of this method is 9% (1 nM, n = 5). The linear range is 0.5-20 nM, and the recoveries in spiked human fluids are in the range of 90-122%. This protocol provides a new tactic in the development of the nonenzymic miRNA biosensors and opens a promising avenue for early diagnosis of miRNA-associated disease. Graphical abstract The SiND-based fluorescent sensor for detection of S-miR-27a.

  19. Targets Mask U-Net for Wind Turbines Detection in Remote Sensing Images

    Han, M.; Wang, H.; Wang, G.; Liu, Y.

    2018-04-01

    To detect wind turbines precisely and quickly in very high resolution remote sensing images (VHRRSI) we propose target mask U-Net. This convolution neural network (CNN), which is carefully designed to be a wide-field detector, models the pixel class assignment to wind turbines and their context information. The shadow, which is the context information of the target in this study, has been regarded as part of a wind turbine instance. We have trained the target mask U-Net on training dataset, which is composed of down sampled image blocks and instance mask blocks. Some post-processes have been integrated to eliminate wrong spots and produce bounding boxes of wind turbine instances. The evaluation metrics prove the reliability and effectiveness of our method for the average F1-score of our detection method is up to 0.97. The comparison of detection accuracy and time consuming with the weakly supervised targets detection method based on CNN illustrates the superiority of our method.

  20. Target detection and driving behaviour measurements in a driving simulator at mesopic light levels

    Alferdinck, J.W.A.M.

    2006-01-01

    During night-time driving hazardous objects often appear at mesopic light levels, which are typically measured using light meters with a spectral sensitivity that is only valid for photopic light levels. In order to develop suitable mesopic models a target detection experiment was performed in a

  1. Detection of Subpixel Submerged Mine-Like Targets in WorldView-2 Multispectral Imagery

    2012-09-01

    exploit the capabilities of the technology already in use, such as the WorldView-2. This is a complicated issue, as any capability has to be able to...Applied Science and Analysis Inc. 91 Mayer, R., & Bucholtz, F. (2003). Object detection by using “ whitening / dewhitening” to transform target

  2. A speeded-up saliency region-based contrast detection method for small targets

    Li, Zhengjie; Zhang, Haiying; Bai, Jiaojiao; Zhou, Zhongjun; Zheng, Huihuang

    2018-04-01

    To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain "integrity" property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of "clustering segmentation", the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.

  3. Aerial surveillance based on hierarchical object classification for ground target detection

    Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

    2015-03-01

    Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

  4. Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

    Minjie Wan

    2017-06-01

    Full Text Available In order to detect both bright and dark small moving targets effectively in infrared (IR video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.

  5. Detection of small targets in a marine environment using laser radar

    Kunz, G.J.; Bekman, H.H.P.T.; Benoist, K.W.; Cohen, L.H.; Heuvel, J.C. van den; Putten, F.J.M.

    2005-01-01

    Small maritime targets, e.g., periscope tubes, jet skies, swimmers and small boats, are potential threats for naval ships under many conditions, but are difficult to detect with current radar systems due to their limited radar cross section and the presence of sea clutter. On the other hand,

  6. Detection of Moving Targets Based on Doppler Spectrum Analysis Technique for Passive Coherent Radar

    Zhao Yao-dong

    2013-06-01

    Full Text Available A novel method of moving targets detection taking Doppler spectrum analysis technique for Passive Coherent Radar (PCR is provided. After dividing the receiving signals into segments as pulse series, it utilizes the technique of pulse compress and Doppler processing to detect and locate the targets. Based on the algorithm for Pulse-Doppler (PD radar, the equipollence between continuous and pulsed wave in match filtering is proved and details of this method are introduced. To compare it with the traditional method of Cross-Ambiguity Function (CAF calculation, the relationship and mathematical modes of them are analyzed, with some suggestions on parameters choosing. With little influence to the gain of targets, the method can greatly promote the processing efficiency. The validity of the proposed method is demonstrated by offline processing real collected data sets and simulation results.

  7. Infrared images target detection based on background modeling in the discrete cosine domain

    Ye, Han; Pei, Jihong

    2018-02-01

    Background modeling is the critical technology to detect the moving target for video surveillance. Most background modeling techniques are aimed at land monitoring and operated in the spatial domain. A background establishment becomes difficult when the scene is a complex fluctuating sea surface. In this paper, the background stability and separability between target are analyzed deeply in the discrete cosine transform (DCT) domain, on this basis, we propose a background modeling method. The proposed method models each frequency point as a single Gaussian model to represent background, and the target is extracted by suppressing the background coefficients. Experimental results show that our approach can establish an accurate background model for seawater, and the detection results outperform other background modeling methods in the spatial domain.

  8. Indoor detection of passive targets recast as an inverse scattering problem

    Gottardi, G.; Moriyama, T.

    2017-10-01

    The wireless local area networks represent an alternative to custom sensors and dedicated surveillance systems for target indoor detection. The availability of the channel state information has opened the exploitation of the spatial and frequency diversity given by the orthogonal frequency division multiplexing. Such a fine-grained information can be used to solve the detection problem as an inverse scattering problem. The goal of the detection is to reconstruct the properties of the investigation domain, namely to estimate if the domain is empty or occupied by targets, starting from the measurement of the electromagnetic perturbation of the wireless channel. An innovative inversion strategy exploiting both the frequency and the spatial diversity of the channel state information is proposed. The target-dependent features are identified combining the Kruskal-Wallis test and the principal component analysis. The experimental validation points out the detection performance of the proposed method when applied to an existing wireless link of a WiFi architecture deployed in a real indoor scenario. False detection rates lower than 2 [%] have been obtained.

  9. Analysis of the Chirplet Transform-Based Algorithm for Radar Detection of Accelerated Targets

    Galushko, V. G.; Vavriv, D. M.

    2017-06-01

    Purpose: Efficiency analysis of an optimal algorithm of chirp signal processing based on the chirplet transform as applied to detection of radar targets in uniformly accelerated motion. Design/methodology/approach: Standard methods of the optimal filtration theory are used to investigate the ambiguity function of chirp signals. Findings: An analytical expression has been derived for the ambiguity function of chirp signals that is analyzed with respect to detection of radar targets moving at a constant acceleration. Sidelobe level and characteristic width of the ambiguity function with respect to the coordinates frequency and rate of its change have been estimated. The gain in the signal-to-noise ratio has been assessed that is provided by the algorithm under consideration as compared with application of the standard Fourier transform to detection of chirp signals against a “white” noise background. It is shown that already with a comparatively small (processing channels (elementary filters with respect to the frequency change rate) the gain in the signal-tonoise ratio exceeds 10 dB. A block diagram of implementation of the algorithm under consideration is suggested on the basis of a multichannel weighted Fourier transform. Recommendations as for selection of the detection algorithm parameters have been developed. Conclusions: The obtained results testify to efficiency of application of the algorithm under consideration to detection of radar targets moving at a constant acceleration. Nevertheless, it seems expedient to perform computer simulations of its operability with account for the noise impact along with trial measurements in real conditions.

  10. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

    Kim, D H; MacKinnon, T

    2018-05-01

    To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs. The top layer of the Inception v3 network was re-trained using lateral wrist radiographs to produce a model for the classification of new studies as either "fracture" or "no fracture". The model was trained on a total of 11,112 images, after an eightfold data augmentation technique, from an initial set of 1,389 radiographs (695 "fracture" and 694 "no fracture"). The training data set was split 80:10:10 into training, validation, and test groups, respectively. An additional 100 wrist radiographs, comprising 50 "fracture" and 50 "no fracture" images, were used for final testing and statistical analysis. The area under the receiver operator characteristic curve (AUC) for this test was 0.954. Setting the diagnostic cut-off at a threshold designed to maximise both sensitivity and specificity resulted in values of 0.9 and 0.88, respectively. The AUC scores for this test were comparable to state-of-the-art providing proof of concept for transfer learning from CNNs in fracture detection on plain radiographs. This was achieved using only a moderate sample size. This technique is largely transferable, and therefore, has many potential applications in medical imaging, which may lead to significant improvements in workflow productivity and in clinical risk reduction. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  11. Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics

    Yi, Weisong; Zhang, Jian; Jiang, Houmin; Zhang, Niya

    2014-09-01

    Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.

  12. Specific and selective target detection of supra-genome 21 Mers Salmonella via silicon nanowires biosensor

    Mustafa, Mohammad Razif Bin; Dhahi, Th S.; Ehfaed, Nuri. A. K. H.; Adam, Tijjani; Hashim, U.; Azizah, N.; Mohammed, Mohammed; Noriman, N. Z.

    2017-09-01

    The nano structure based on silicon can be surface modified to be used as label-free biosensors that allow real-time measurements. The silicon nanowire surface was functionalized using 3-aminopropyltrimethoxysilane (APTES), which functions as a facilitator to immobilize biomolecules on the silicon nanowire surface. The process is simple, economical; this will pave the way for point-of-care applications. However, the surface modification and subsequent detection mechanism still not clear. Thus, study proposed step by step process of silicon nano surface modification and its possible in specific and selective target detection of Supra-genome 21 Mers Salmonella. The device captured the molecule with precisely; the approach took the advantages of strong binding chemistry created between APTES and biomolecule. The results indicated how modifications of the nanowires provide sensing capability with strong surface chemistries that can lead to specific and selective target detection.

  13. Intelligible Artificial Intelligence

    Weld, Daniel S.; Bansal, Gagan

    2018-01-01

    Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. In order to trust their behavior, we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwh...

  14. Self-focusing therapeutic gene delivery with intelligent gene vector swarms: intra-swarm signalling through receptor transgene expression in targeted cells.

    Tolmachov, Oleg E

    2015-01-01

    Gene delivery in vivo that is tightly focused on the intended target cells is essential to maximize the benefits of gene therapy and to reduce unwanted side-effects. Cell surface markers are immediately available for probing by therapeutic gene vectors and are often used to direct gene transfer with these vectors to specific target cell populations. However, it is not unusual for the choice of available extra-cellular markers to be too scarce to provide a reliable definition of the desired therapeutically relevant set of target cells. Therefore, interrogation of intra-cellular determinants of cell-specificity, such as tissue-specific transcription factors, can be vital in order to provide detailed cell-guiding information to gene vector particles. An important improvement in cell-specific gene delivery can be achieved through auto-buildup in vector homing efficiency using intelligent 'self-focusing' of swarms of vector particles on target cells. Vector self-focusing was previously suggested to rely on the release of diffusible chemo-attractants after a successful target-specific hit by 'scout' vector particles. I hypothesize that intelligent self-focusing behaviour of swarms of cell-targeted therapeutic gene vectors can be accomplished without the employment of difficult-to-use diffusible chemo-attractants, instead relying on the intra-swarm signalling through cells expressing a non-diffusible extra-cellular receptor for the gene vectors. In the proposed model, cell-guiding information is gathered by the 'scout' gene vector particles, which: (1) attach to a variety of cells via a weakly binding (low affinity) receptor; (2) successfully facilitate gene transfer into these cells; (3) query intra-cellular determinants of cell-specificity with their transgene expression control elements and (4) direct the cell-specific biosynthesis of a vector-encoded strongly binding (high affinity) cell-surface receptor. Free members of the vector swarm loaded with therapeutic cargo

  15. A new frozen-spin target for 4π particle detection

    Bradtke, Ch.; Dutz, H.; Peschel, H.; Goertz, S.; Harmsen, J.; Hasegawa, S.; Horikawa, N.; Iwata, T.; Kageya, T.; Matsuda, T.; Meier, A.; Meyer, W.; Radtke, E.; Reicherz, G.; Rohlof, Ch.; Thomas, A.; Wakai, A.

    1999-01-01

    A new frozen-spin target has been developed, that allows the detection of emitted particles in an angular acceptance of almost 4π in the laboratory frame. The central part of this new target represents a 3 He/ 4 He dilution refrigerator that is installed horizontally along the beam axis. The refrigerator includes an internal superconducting holding coil to maintain the nucleon polarization in the frozen-spin mode longitudinally to the beam. The design of the dilution refrigerator and the use of an internal holding coil enabled for the first time the measurement of a spin-dependent total cross section in combination with a polarized solid state target. This new frozen-spin target was used successfully to measure the helicity asymmetry of the total photoabsorption cross-section at the Mainz accelerator facility MAMI. This experiment has been performed in order to verify for the first time the GDH sum rule

  16. A new frozen-spin target for 4 pi particle detection

    Bradtke, C; Peschel, H; Görtz, S; Harmsen, J; Hasegawa, S; Horikawa, N; Iwata, T; Kageya, T; Matsuda, T; Meier, A; Meyer, Werner T; Radtke, E; Reicherz, G; Rohlof, C; Thomas, A; Wakai, A

    1999-01-01

    A new frozen-spin target has been developed, that allows the detection of emitted particles in an angular acceptance of almost 4 pi in the laboratory frame. The central part of this new target represents a sup 3 He/ sup 4 He dilution refrigerator that is installed horizontally along the beam axis. The refrigerator includes an internal superconducting holding coil to maintain the nucleon polarization in the frozen-spin mode longitudinally to the beam. The design of the dilution refrigerator and the use of an internal holding coil enabled for the first time the measurement of a spin-dependent total cross section in combination with a polarized solid state target. This new frozen-spin target was used successfully to measure the helicity asymmetry of the total photoabsorption cross-section at the Mainz accelerator facility MAMI. This experiment has been performed in order to verify for the first time the GDH sum rule.

  17. One Novel Multiple-Target Plasmid Reference Molecule Targeting Eight Genetically Modified Canola Events for Genetically Modified Canola Detection.

    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.

  18. Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

    Fergus, Paul; Hignett, David; Hussain, Abir; Al-Jumeily, Dhiya; Abdel-Aziz, Khaled

    2015-01-01

    The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.

  19. Fault detection and analysis in nuclear research facility using artificial intelligence methods

    Ghazali, Abu Bakar, E-mail: Abakar@uniten.edu.my [Department of Electronics & Communication, College of Engineering, Universiti Tenaga Nasional, 43009 Kajang, Selangor (Malaysia); Ibrahim, Maslina Mohd [Instrumentation Program, Malaysian Nuclear Agency, Bangi (Malaysia)

    2016-01-22

    In this article, an online detection of transducer and actuator condition is discussed. A case study is on the reading of area radiation monitor (ARM) installed at the chimney of PUSPATI TRIGA nuclear reactor building, located at Bangi, Malaysia. There are at least five categories of abnormal ARM reading that could happen during the transducer failure, namely either the reading becomes very high, or very low/ zero, or with high fluctuation and noise. Moreover, the reading may be significantly higher or significantly lower as compared to the normal reading. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are good methods for modeling this plant dynamics. The failure of equipment is based on ARM reading so it is then to compare with the estimated ARM data from ANN/ ANFIS function. The failure categories in either ‘yes’ or ‘no’ state are obtained from a comparison between the actual online data and the estimated output from ANN/ ANFIS function. It is found that this system design can correctly report the condition of ARM equipment in a simulated environment and later be implemented for online monitoring. This approach can also be extended to other transducers, such as the temperature profile of reactor core and also to include other critical actuator conditions such as the valves and pumps in the reactor facility provided that the failure symptoms are clearly defined.

  20. An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

    Hajimani, Elmira; Ruano, M G; Ruano, A E

    2017-07-01

    This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). Copyright © 2017. Published by Elsevier B.V.

  1. The Novel Artificial Intelligence Based Sub-Surface Inclusion Detection Device and Algorithm

    Jong-Ha LEE

    2017-05-01

    Full Text Available We design, implement, and test a novel tactile elasticity imaging sensor to detect the elastic modulus of a contacted object. Emulating a human finger, a multi-layer polydimethylsiloxane waveguide has been fabricated as the sensing probe. The light is illuminated under the critical angle to totally reflect within the flexible and transparent waveguide. When a waveguide is compressed by an object, the contact area of the waveguide deforms and causes the light to scatter. The scattered light is captured by a high resolution camera. Multiple images are taken from slightly different loading values. The distributed forces have been estimated using the integrated pixel values of diffused lights. The displacements of the contacted object deformation have been estimated by matching the series of tactile images. For this purpose, a novel pattern matching algorithm is developed. The salient feature of this sensor is that it is capable of measuring the absolute elastic modulus value of soft materials without additional measurement units. The measurements were validated by comparing the measured elasticity of the commercial rubber samples with the known elasticity. The evaluation results showed that this type of sensor can measure elasticity within ±5.38 %.

  2. Antagonism pattern detection between microRNA and target expression in Ewing's sarcoma.

    Loredana Martignetti

    Full Text Available MicroRNAs (miRNAs have emerged as fundamental regulators that silence gene expression at the post-transcriptional and translational levels. The identification of their targets is a major challenge to elucidate the regulated biological processes. The overall effect of miRNA is reflected on target mRNA expression, suggesting the design of new investigative methods based on high-throughput experimental data such as miRNA and transcriptome profiles. We propose a novel statistical measure of non-linear dependence between miRNA and mRNA expression, in order to infer miRNA-target interactions. This approach, which we name antagonism pattern detection, is based on the statistical recognition of a triangular-shaped pattern in miRNA-target expression profiles. This pattern is observed in miRNA-target expression measurements since their simultaneously elevated expression is statistically under-represented in the case of miRNA silencing effect. The proposed method enables miRNA target prediction to strongly rely on cellular context and physiological conditions reflected by expression data. The procedure has been assessed on synthetic datasets and tested on a set of real positive controls. Then it has been applied to analyze expression data from Ewing's sarcoma patients. The antagonism relationship is evaluated as a good indicator of real miRNA-target biological interaction. The predicted targets are consistently enriched for miRNA binding site motifs in their 3'UTR. Moreover, we reveal sets of predicted targets for each miRNA sharing important biological function. The procedure allows us to infer crucial miRNA regulators and their potential targets in Ewing's sarcoma disease. It can be considered as a valid statistical approach to discover new insights in the miRNA regulatory mechanisms.

  3. A Novel Detection Method for Underwater Moving Targets by Measuring Their ELF Emissions with Inductive Sensors

    Jinhong Wang

    2017-07-01

    Full Text Available In this article, we propose a novel detection method for underwater moving targets by detecting their extremely low frequency (ELF emissions with inductive sensors. The ELF field source of the targets is modeled by a horizontal electric dipole at distances more than several times of the targets’ length. The formulas for the fields produced in air are derived with a three-layer model (air, seawater and seafloor and are evaluated with a complementary numerical integration technique. A proof of concept measurement is presented. The ELF emissions from a surface ship were detected by inductive electronic and magnetic sensors as the ship was leaving a harbor. ELF signals are of substantial strength and have typical characteristic of harmonic line spectrum, and the fundamental frequency has a direct relationship with the ship’s speed. Due to the high sensitivity and low noise level of our sensors, it is capable of resolving weak ELF signals at long distance. In our experiment, a detection distance of 1300 m from the surface ship above the sea surface was realized, which shows that this method would be an appealing complement to the usual acoustic detection and magnetic anomaly detection capability.

  4. Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection

    Dalibor Janckulik

    2011-06-01

    Full Text Available Sleep is not just a passive process, but rather a highly dynamic process that is terminated by waking up. Throughout the night a specific number of sleep stages that are repeatedly changing in various periods of time take place. These specific time intervals and specific sleep stages are very important for the wake up event. It is far more difficult to wake up during the deep NREM (2–4 stage of sleep because the rest of the body is still sleeping. On the other hand if we wake up during the mild (REM, NREM1 sleep stage it is a much more pleasant experience for us and for our bodies. This problem led the authors to undertake this study and develop a Windows Mobile-based device application called wakeNsmile. The wakeNsmile application records and monitors the sleep stages for specific amounts of time before a desired alarm time set by users. It uses a built-in microphone and determines the optimal time to wake the user up. Hence, if the user sets an alarm in wakeNsmile to 7:00 and wakeNsmile detects that a more appropriate time to wake up (REM stage is at 6:50, the alarm will start at 6:50. The current availability and low price of mobile devices is yet another reason to use and develop such an application that will hopefully help someone to wakeNsmile in the morning. So far, the wakeNsmile application has been tested on four individuals introduced in the final section.

  5. Robust Detection of Moving Human Target in Foliage-Penetration Environment Based on Hough Transform

    P. Lei

    2014-04-01

    Full Text Available Attention has been focused on the robust moving human target detection in foliage-penetration environment, which presents a formidable task in a radar system because foliage is a rich scattering environment with complex multipath propagation and time-varying clutter. Generally, multiple-bounce returns and clutter are additionally superposed to direct-scatter echoes. They obscure true target echo and lead to poor visual quality time-range image, making target detection particular difficult. Consequently, an innovative approach is proposed to suppress clutter and mitigate multipath effects. In particular, a clutter suppression technique based on range alignment is firstly applied to suppress the time-varying clutter and the instable antenna coupling. Then entropy weighted coherent integration (EWCI algorithm is adopted to mitigate the multipath effects. In consequence, the proposed method effectively reduces the clutter and ghosting artifacts considerably. Based on the high visual quality image, the target trajectory is detected robustly and the radial velocity is estimated accurately with the Hough transform (HT. Real data used in the experimental results are provided to verify the proposed method.

  6. A New Strategy to Reduce Influenza Escape: Detecting Therapeutic Targets Constituted of Invariance Groups

    Julie Lao

    2017-03-01

    Full Text Available The pathogenicity of the different flu species is a real public health problem worldwide. To combat this scourge, we established a method to detect drug targets, reducing the possibility of escape. Besides being able to attach a drug candidate, these targets should have the main characteristic of being part of an essential viral function. The invariance groups that are sets of residues bearing an essential function can be detected genetically. They consist of invariant and synthetic lethal residues (interdependent residues not varying or slightly varying when together. We analyzed an alignment of more than 10,000 hemagglutinin sequences of influenza to detect six invariance groups, close in space, and on the protein surface. In parallel we identified five potential pockets on the surface of hemagglutinin. By combining these results, three potential binding sites were determined that are composed of invariance groups located respectively in the vestigial esterase domain, in the bottom of the stem and in the fusion area. The latter target is constituted of residues involved in the spring-loaded mechanism, an essential step in the fusion process. We propose a model describing how this potential target could block the reorganization of the hemagglutinin HA2 secondary structure and prevent viral entry into the host cell.

  7. The Intelligent Transportation Systems Public Safety Program : opportunities for technological advancement in detecting, responding, and recovering from community emergencies

    2001-01-01

    During the summer and fall of 2000, a group of high level public safety and transportation officials was brought together by the US Department of Transportations (USDOT) Intelligent Transportation Systems (ITS) Program to consider the interaction bet...

  8. NAIMA: target amplification strategy allowing quantitative on-chip detection of GMOs.

    Morisset, Dany; Dobnik, David; Hamels, Sandrine; Zel, Jana; Gruden, Kristina

    2008-10-01

    We have developed a novel multiplex quantitative DNA-based target amplification method suitable for sensitive, specific and quantitative detection on microarray. This new method named NASBA Implemented Microarray Analysis (NAIMA) was applied to GMO detection in food and feed, but its application can be extended to all fields of biology requiring simultaneous detection of low copy number DNA targets. In a first step, the use of tailed primers allows the multiplex synthesis of template DNAs in a primer extension reaction. A second step of the procedure consists of transcription-based amplification using universal primers. The cRNA product is further on directly ligated to fluorescent dyes labelled 3DNA dendrimers allowing signal amplification and hybridized without further purification on an oligonucleotide probe-based microarray for multiplex detection. Two triplex systems have been applied to test maize samples containing several transgenic lines, and NAIMA has shown to be sensitive down to two target copies and to provide quantitative data on the transgenic contents in a range of 0.1-25%. Performances of NAIMA are comparable to singleplex quantitative real-time PCR. In addition, NAIMA amplification is faster since 20 min are sufficient to achieve full amplification.

  9. Joint sparsity based heterogeneous data-level fusion for target detection and estimation

    Niu, Ruixin; Zulch, Peter; Distasio, Marcello; Blasch, Erik; Shen, Dan; Chen, Genshe

    2017-05-01

    Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.

  10. Ship detection for high resolution optical imagery with adaptive target filter

    Ju, Hongbin

    2015-10-01

    Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.

  11. Fast and sensitive detection of indels induced by precise gene targeting

    Yang, Zhang; Steentoft, Catharina; Hauge, Camilla

    2015-01-01

    The nuclease-based gene editing tools are rapidly transforming capabilities for altering the genome of cells and organisms with great precision and in high throughput studies. A major limitation in application of precise gene editing lies in lack of sensitive and fast methods to detect...... and characterize the induced DNA changes. Precise gene editing induces double-stranded DNA breaks that are repaired by error-prone non-homologous end joining leading to introduction of insertions and deletions (indels) at the target site. These indels are often small and difficult and laborious to detect...

  12. TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

    C. Jehan

    2016-06-01

    Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

  13. Detection-Discrimination Method for Multiple Repeater False Targets Based on Radar Polarization Echoes

    Z. W. ZONG

    2014-04-01

    Full Text Available Multiple repeat false targets (RFTs, created by the digital radio frequency memory (DRFM system of jammer, are widely used in practical to effectively exhaust the limited tracking and discrimination resource of defence radar. In this paper, common characteristic of radar polarization echoes of multiple RFTs is used for target recognition. Based on the echoes from two receiving polarization channels, the instantaneous polarization radio (IPR is defined and its variance is derived by employing Taylor series expansion. A detection-discrimination method is designed based on probability grids. By using the data from microwave anechoic chamber, the detection threshold of the method is confirmed. Theoretical analysis and simulations indicate that the method is valid and feasible. Furthermore, the estimation performance of IPRs of RFTs due to the influence of signal noise ratio (SNR is also covered.

  14. Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis.

    Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

    2015-01-01

    Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

  15. Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

    Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

    2015-01-01

    Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks. PMID:26222882

  16. Detection of Pathogenic Biofilms with Bacterial Amyloid Targeting Fluorescent Probe, CDy11

    Kim, Jun Young; Sahu, Srikanta; Yau, Yin Hoe

    2016-01-01

    Bacterial biofilms are responsible for a wide range of persistent infections. In the clinic, diagnosis of biofilm-associated infections relies heavily on culturing methods, which fail to detect nonculturable bacteria. Identification of novel fluorescent probes for biofilm imaging will greatly...... facilitate diagnosis of pathogenic bacterial infection. Herein, we report a novel fluorescent probe, CDy11 (compound of designation yellow 11), which targets amyloid in the Pseudomonas aeruginosa biofilm matrix through a diversity oriented fluorescent library approach (DOFLA). CDy11 was further demonstrated...

  17. Automatic detection of the unknown number point targets in FMICW radar signals

    Rejfek, L.; Mošna, Zbyšek; Beran, L.; Fišer, O.; Dobrovolný, M.

    2017-01-01

    Roč. 4, č. 11 (2017), s. 116-120 ISSN 2313-626X R&D Projects: GA ČR(CZ) GA15-24688S Institutional support: RVO:68378289 Keywords : FMICW radar * 2D FFT * signal filtration * taraget detection * target parameter estimation Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences http://science-gate.com/IJAAS/Articles/2017-4-11/18%202017-4-11-pp.116-120.pdf

  18. Analytical Approach to Target Detection and Localization at High-Frequency Bands Using Multipath Propagation

    2016-04-25

    ElectroMagnetic, Multipath propagation, Reflection-diffraction, SAR signal processing 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18...protection, and traffic surveillance, etc. With these above reasons, we are motivated to introduce a new approach to the target detection and...coherent integrating the backscattering signal , we propose a 3D propagation model that is useful not only in explaining the mechanisms of wave

  19. Ultramild protein-mediated click chemistry creates efficient oligonucleotide probes for targeting and detecting nucleic acids

    Nåbo, Lina J.; Madsen, Charlotte S.; Jensen, Knud J.

    2015-01-01

    Functionalized synthetic oligonucleotides are finding growing applications in research, clinical studies, and therapy. However, it is not easy to prepare them in a biocompatible and highly efficient manner. We report a new strategy to synthesize oligonucleotides with promising nucleic acid...... targeting and detection properties. We focus in particular on the pH sensitivity of these new probes and their high target specificity. For the first time, human copper(I)-binding chaperon Cox17 was applied to effectively catalyze click labeling of oligonucleotides. This was performed under ultramild...... conditions with fluorophore, peptide, and carbohydrate azide derivatives. In thermal denaturation studies, the modified probes showed specific binding to complementary DNA and RNA targets. Finally, we demonstrated the pH sensitivity of the new rhodamine-based fluorescent probes in vitro and rationalize our...

  20. Switchable DNA interfaces for the highly sensitive detection of label-free DNA targets.

    Rant, Ulrich; Arinaga, Kenji; Scherer, Simon; Pringsheim, Erika; Fujita, Shozo; Yokoyama, Naoki; Tornow, Marc; Abstreiter, Gerhard

    2007-10-30

    We report a method to detect label-free oligonucleotide targets. The conformation of surface-tethered probe nucleic acids is modulated by alternating electric fields, which cause the molecules to extend away from or fold onto the biased surface. Binding (hybridization) of targets to the single-stranded probes results in a pronounced enhancement of the layer-height modulation amplitude, monitored optically in real time. The method features an exceptional detection limit of <3 x 10(8) bound targets per cm(2) sensor area. Single base-pair mismatches in the sequences of DNA complements may readily be identified; moreover, binding kinetics and binding affinities can be determined with high accuracy. When driving the DNA to oscillate at frequencies in the kHz regime, distinct switching kinetics are revealed for single- and double-stranded DNA. Molecular dynamics are used to identify the binding state of molecules according to their characteristic kinetic fingerprints by using a chip-compatible detection format.

  1. Detection of ferromagnetic target based on mobile magnetic gradient tensor system

    Gang, Y.I.N., E-mail: gang.gang88@163.com; Yingtang, Zhang; Zhining, Li; Hongbo, Fan; Guoquan, Ren

    2016-03-15

    Attitude change of mobile magnetic gradient tensor system critically affects the precision of gradient measurements, thereby increasing ambiguity in target detection. This paper presents a rotational invariant-based method for locating and identifying ferromagnetic targets. Firstly, unit magnetic moment vector was derived based on the geometrical invariant, such that the intermediate eigenvector of the magnetic gradient tensor is perpendicular to the magnetic moment vector and the source–sensor displacement vector. Secondly, unit source–sensor displacement vector was derived based on the characteristic that the angle between magnetic moment vector and source–sensor displacement is a rotational invariant. By introducing a displacement vector between two measurement points, the magnetic moment vector and the source–sensor displacement vector were theoretically derived. To resolve the problem of measurement noises existing in the realistic detection applications, linear equations were formulated using invariants corresponding to several distinct measurement points and least square solution of magnetic moment vector and source–sensor displacement vector were obtained. Results of simulation and principal verification experiment showed the correctness of the analytical method, along with the practicability of the least square method. - Highlights: • Ferromagnetic target detection method is proposed based on rotational invariants • Intermediate eigenvector is perpendicular to magnetic moment and displacement vector • Angle between magnetic moment and displacement vector is a rotational invariant • Magnetic moment and displacement vector are derived based on invariants of two points.

  2. Artificial intelligence

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  3. Targeted Ultrasound for MR-Detected Lesions in Breast Cancer Patients

    Shin, Jung Hee; Han, Boo Kyung; Choe, Yeon Hyeon; Ko, Kyung Ran; Choi, Nami

    2007-01-01

    To investigate the usefulness of targeted ultrasound (US) in the identification of additional suspicious lesions found by magnetic resonance (MR) imaging in breast cancer patients and the changes in treatment based on the identification of the lesions by the use of targeted US. One-hundred forty nine patients who underwent breast MR imaging for a preoperative evaluation of breast cancer between January 2002 and July 2004 were included in the study. We searched all cases for any additional lesions that were found initially by MR imaging and investigated the performance of targeted US in identifying the lesions. We also investigated their pathological outcomes and changes in treatment as a result of lesion identification. Of the 149 patients with breast cancer, additional suspicious lesions were detected with MR imaging in 62 patients (42%). Of the 69 additional lesions found in those 62 patients, 26 (38%) were confirmed as cancers by histology. Thirty-eight lesions in 31 patients were examined with targeted US and were histologically revealed as cancers in 18 (47%), high risk lesions in two (5%), benign lesions in 15 (39%), and unidentified lesions in three (8%). The cancer rate was statistically higher in lesions with a US correlate than in lesions without a US correlate (p = 0.028). Of 31 patients, the surgical plan was altered in 27 (87%). The use of targeted US justified a change in treatment for 22 patients (81%) and misled five patients (19%) into having an unnecessary surgical excision. Targeted US can play a useful role in the evaluation of additional suspicious lesions detected by MR imaging in breast cancer patients, but is limited in lesions without a US correlate

  4. Adding temporally localized noise can enhance the contribution of target knowledge on contrast detection.

    Silvestre, Daphné; Cavanagh, Patrick; Arleo, Angelo; Allard, Rémy

    2017-02-01

    External noise paradigms are widely used to characterize sensitivity by comparing the effect of a variable on contrast threshold when it is limited by internal versus external noise. A basic assumption of external noise paradigms is that the processing properties are the same in low and high noise. However, recent studies (e.g., Allard & Cavanagh, 2011; Allard & Faubert, 2014b) suggest that this assumption could be violated when using spatiotemporally localized noise (i.e., appearing simultaneously and at the same location as the target) but not when using spatiotemporally extended noise (i.e., continuously displayed, full-screen, dynamic noise). These previous findings may have been specific to the crowding and 0D noise paradigms that were used, so the purpose of the current study is to test if this violation of noise-invariant processing also occurs in a standard contrast detection task in white noise. The rationale of the current study is that local external noise triggers the use of recognition rather than detection and that a recognition process should be more affected by uncertainty about the shape of the target than one involving detection. To investigate the contribution of target knowledge on contrast detection, the effect of orientation uncertainty was evaluated for a contrast detection task in the absence of noise and in the presence of spatiotemporally localized or extended noise. A larger orientation uncertainty effect was observed with temporally localized noise than with temporally extended noise or with no external noise, indicating a change in the nature of the processing for temporally localized noise. We conclude that the use of temporally localized noise in external noise paradigms risks triggering a shift in process, invalidating the noise-invariant processing required for the paradigm. If, instead, temporally extended external noise is used to match the properties of internal noise, no such processing change occurs.

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

    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.

  6. panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.

    Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Hochreiter, Sepp; Wimmer, Katharina

    2017-07-01

    Targeted next-generation-sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy-number variations (CNVs) in addition to single-nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user-friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state-of-the-art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user-selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user-friendliness rendering it highly suitable for routine clinical diagnostics. © 2017 The Authors. Human Mutation published by Wiley Periodicals, Inc.

  7. Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

    Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

    2018-04-01

    Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

  8. Detection of genetically modified organisms (GMOs using isothermal amplification of target DNA sequences

    La Mura Maurizio

    2009-02-01

    Full Text Available Abstract Background The most common method of GMO detection is based upon the amplification of GMO-specific DNA amplicons using the polymerase chain reaction (PCR. Here we have applied the loop-mediated isothermal amplification (LAMP method to amplify GMO-related DNA sequences, 'internal' commonly-used motifs for controlling transgene expression and event-specific (plant-transgene junctions. Results We have tested the specificity and sensitivity of the technique for use in GMO studies. Results show that detection of 0.01% GMO in equivalent background DNA was possible and dilutions of template suggest that detection from single copies of the template may be possible using LAMP. Conclusion This work shows that GMO detection can be carried out using LAMP for routine screening as well as for specific events detection. Moreover, the sensitivity and ability to amplify targets, even with a high background of DNA, here demonstrated, highlights the advantages of this isothermal amplification when applied for GMO detection.

  9. Detection of attack-targeted scans from the Apache HTTP Server access logs

    Merve Baş Seyyar

    2018-01-01

    Full Text Available A web application could be visited for different purposes. It is possible for a web site to be visited by a regular user as a normal (natural visit, to be viewed by crawlers, bots, spiders, etc. for indexing purposes, lastly to be exploratory scanned by malicious users prior to an attack. An attack targeted web scan can be viewed as a phase of a potential attack and can lead to more attack detection as compared to traditional detection methods. In this work, we propose a method to detect attack-oriented scans and to distinguish them from other types of visits. In this context, we use access log files of Apache (or ISS web servers and try to determine attack situations through examination of the past data. In addition to web scan detections, we insert a rule set to detect SQL Injection and XSS attacks. Our approach has been applied on sample data sets and results have been analyzed in terms of performance measures to compare our method and other commonly used detection techniques. Furthermore, various tests have been made on log samples from real systems. Lastly, several suggestions about further development have been also discussed.

  10. A comparison of machine learning techniques for detection of drug target articles.

    Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-12-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. Modern Approaches to the Computation of the Probability of Target Detection in Cluttered Environments

    Meitzler, Thomas J.

    The field of computer vision interacts with fields such as psychology, vision research, machine vision, psychophysics, mathematics, physics, and computer science. The focus of this thesis is new algorithms and methods for the computation of the probability of detection (Pd) of a target in a cluttered scene. The scene can be either a natural visual scene such as one sees with the naked eye (visual), or, a scene displayed on a monitor with the help of infrared sensors. The relative clutter and the temperature difference between the target and background (DeltaT) are defined and then used to calculate a relative signal -to-clutter ratio (SCR) from which the Pd is calculated for a target in a cluttered scene. It is shown how this definition can include many previous definitions of clutter and (DeltaT). Next, fuzzy and neural -fuzzy techniques are used to calculate the Pd and it is shown how these methods can give results that have a good correlation with experiment. The experimental design for actually measuring the Pd of a target by observers is described. Finally, wavelets are applied to the calculation of clutter and it is shown how this new definition of clutter based on wavelets can be used to compute the Pd of a target.

  12. Intelligibility for Binaural Speech with Discarded Low-SNR Speech Components.

    Schoenmaker, Esther; van de Par, Steven

    2016-01-01

    Speech intelligibility in multitalker settings improves when the target speaker is spatially separated from the interfering speakers. A factor that may contribute to this improvement is the improved detectability of target-speech components due to binaural interaction in analogy to the Binaural Masking Level Difference (BMLD). This would allow listeners to hear target speech components within specific time-frequency intervals that have a negative SNR, similar to the improvement in the detectability of a tone in noise when these contain disparate interaural difference cues. To investigate whether these negative-SNR target-speech components indeed contribute to speech intelligibility, a stimulus manipulation was performed where all target components were removed when local SNRs were smaller than a certain criterion value. It can be expected that for sufficiently high criterion values target speech components will be removed that do contribute to speech intelligibility. For spatially separated speakers, assuming that a BMLD-like detection advantage contributes to intelligibility, degradation in intelligibility is expected already at criterion values below 0 dB SNR. However, for collocated speakers it is expected that higher criterion values can be applied without impairing speech intelligibility. Results show that degradation of intelligibility for separated speakers is only seen for criterion values of 0 dB and above, indicating a negligible contribution of a BMLD-like detection advantage in multitalker settings. These results show that the spatial benefit is related to a spatial separation of speech components at positive local SNRs rather than to a BMLD-like detection improvement for speech components at negative local SNRs.

  13. Artificial Intelligence and Moral intelligence

    Laura Pana

    2008-01-01

    We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined,...

  14. Theoretical study and experimental detection of cavitation phenomena in Liquid Lithium Target Facility for IFMIF

    Orco, G. Dell; Horiike, H.; Ida, M.; Nakamura, H.

    2006-01-01

    In the IFMIF (International Fusion Materials Irradiation Facility) testing facility, the required high energy neutrons emission will be produced by reaction of two D + beams with a free surface liquid Lithium jet target flowing along concave back-wall at 20 m/s. The Lithium height in the experimental loop and its relevant static pressure, the high flow velocities and the presence of several devices for the flow control and the pressure reduction increase the risk of cavitation onset in the target system. Special attention has to be taken in the primary pump, in the flow straightener, in the nozzle and their interconnections where the local pressure decreases and/or velocity increases or flow separations could promote the emission of cavitation vapour bubbles. The successive bubble re-implosions, in the higher pressure liquid bulk, could activate material erosion and transportation of activated particulates. These bubbles, if emitted close to the free jet flow, could also procure hydraulic instability and disturbance of the neutron field in the D + beams-Lithium target zone. Therefore, the cavitation risk must be properly foreseen along the whole IFMIF Lithium target circuit and its occurrence at different operating condition should be also monitored by special instrumentation. ENEA, in close cooperation with JAEA, has demonstrated the capability to detect the onset of the cavitation noises in liquid Lithium, by using the ENEA patented accelerometric gauge called CASBA-2000, during hydraulic test campaigns carried-out at Osaka University Lithium facility on a straight mock-up of the IFMIF back plate target. Comparison with the Thoma' cavitation similitude criteria have also determined the critical threshold limit for the estimation of the onset. Theoretical study on the conditions of cavitations generation in the IFMIF Lithium Target Circuit were also launched between ENEA and JAEA aiming at analysing the risk of the cavitation occurrence in the Lithium flow by

  15. Search for extraterrestrial intelligence (SETI)

    Morrison, P.; Billingham, J.; Wolfe, J.

    1977-01-01

    Findings are presented of a series of workshops on the existence of extraterrestrial intelligent life and ways in which extraterrestrial intelligence might be detected. The coverage includes the cosmic and cultural evolutions, search strategies, detection of other planetary systems, alternate methods of communication, and radio frequency interference. 17 references

  16. A Strain-Based Method to Detect Tires’ Loss of Grip and Estimate Lateral Friction Coefficient from Experimental Data by Fuzzy Logic for Intelligent Tire Development

    Jorge Yunta

    2018-02-01

    Full Text Available Tires are a key sub-system of vehicles that have a big responsibility for comfort, fuel consumption and traffic safety. However, current tires are just passive rubber elements which do not contribute actively to improve the driving experience or vehicle safety. The lack of information from the tire during driving gives cause for developing an intelligent tire. Therefore, the aim of the intelligent tire is to monitor tire working conditions in real-time, providing useful information to other systems and becoming an active system. In this paper, tire tread deformation is measured to provide a strong experimental base with different experiments and test results by means of a tire fitted with sensors. Tests under different working conditions such as vertical load or slip angle have been carried out with an indoor tire test rig. The experimental data analysis shows the strong relation that exists between lateral force and the maximum tensile and compressive strain peaks when the tire is not working at the limit of grip. In the last section, an estimation system from experimental data has been developed and implemented in Simulink to show the potential of strain sensors for developing intelligent tire systems, obtaining as major results a signal to detect tire’s loss of grip and estimations of the lateral friction coefficient.

  17. A Strain-Based Method to Detect Tires' Loss of Grip and Estimate Lateral Friction Coefficient from Experimental Data by Fuzzy Logic for Intelligent Tire Development.

    Yunta, Jorge; Garcia-Pozuelo, Daniel; Diaz, Vicente; Olatunbosun, Oluremi

    2018-02-06

    Tires are a key sub-system of vehicles that have a big responsibility for comfort, fuel consumption and traffic safety. However, current tires are just passive rubber elements which do not contribute actively to improve the driving experience or vehicle safety. The lack of information from the tire during driving gives cause for developing an intelligent tire. Therefore, the aim of the intelligent tire is to monitor tire working conditions in real-time, providing useful information to other systems and becoming an active system. In this paper, tire tread deformation is measured to provide a strong experimental base with different experiments and test results by means of a tire fitted with sensors. Tests under different working conditions such as vertical load or slip angle have been carried out with an indoor tire test rig. The experimental data analysis shows the strong relation that exists between lateral force and the maximum tensile and compressive strain peaks when the tire is not working at the limit of grip. In the last section, an estimation system from experimental data has been developed and implemented in Simulink to show the potential of strain sensors for developing intelligent tire systems, obtaining as major results a signal to detect tire's loss of grip and estimations of the lateral friction coefficient.

  18. Rapid amplification/detection of nucleic acid targets utilizing a HDA/thin film biosensor.

    Jenison, Robert; Jaeckel, Heidi; Klonoski, Joshua; Latorra, David; Wiens, Jacinta

    2014-08-07

    Thin film biosensors exploit a flat, optically coated silicon-based surface whereupon formation of nucleic acid hybrids are enzymatically transduced in a molecular thin film that can be detected by the unaided human eye under white light. While the limit of sensitivity for detection of nucleic acid targets is at sub-attomole levels (60 000 copies) many clinical specimens containing bacterial pathogens have much lower levels of analyte present. Herein, we describe a platform, termed HDA/thin film biosensor, which performs helicase-dependant nucleic acid amplification on a thin film biosensor surface to improve the limit of sensitivity to 10 copies of the mecA gene present in methicillin-resistant strains of Staphylococcus. As double-stranded DNA is unwound by helicase it was either bound by solution-phase DNA primers to be copied by DNA polymerase or hybridized to surface immobilized probe on the thin film biosensor surface to be detected. Herein, we show that amplification reactions on the thin film biosensor are equivalent to in standard thin wall tubes, with detection at the limit of sensitivity of the assay occurring after 30 minutes of incubation time. Further we validate the approach by detecting the presence of the mecA gene in methicillin-resistant Staphylococcus aureus (MRSA) from positive blood culture aliquots with high specificity (signal/noise ratio of 105).

  19. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

    Tengyue Zou

    2017-05-01

    Full Text Available Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  20. Detection of Balamuthia mandrillaris DNA by real-time PCR targeting the RNase P gene

    Lewin Astrid

    2008-12-01

    Full Text Available Abstract Background The free-living amoeba Balamuthia mandrillaris may cause fatal encephalitis both in immunocompromised and in – apparently – immunocompetent humans and other mammalian species. Rapid, specific, sensitive, and reliable detection requiring little pathogen-specific expertise is an absolute prerequisite for a successful therapy and a welcome tool for both experimental and epidemiological research. Results A real-time polymerase chain reaction assay using TaqMan® probes (real-time PCR was established specifically targeting the RNase P gene of B. mandrillaris amoebae. The assay detected at least 2 (down to 0.5 genomes of B. mandrillaris grown in axenic culture. It did not react with DNA from closely related Acanthamoeba (3 species, nor with DNA from Toxoplasma gondii, Leishmania major, Pneumocystis murina, Mycobacterium bovis (BCG, human brain, various mouse organs, or from human and murine cell lines. The assay efficiently detected B. mandrillaris DNA in spiked cell cultures, spiked murine organ homogenates, B. mandrillaris-infected mice, and CNS tissue-DNA preparations from 2 patients with proven cerebral balamuthiasis. This novel primer set was successfully combined with a published set that targets the B. mandrillaris 18S rRNA gene in a duplex real-time PCR assay to ensure maximum specificity and as a precaution against false negative results. Conclusion A real-time PCR assay for B. mandrillaris amoebae is presented, that is highly specific, sensitive, and reliable and thus suited both for diagnosis and for research.

  1. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks.

    Zou, Tengyue; Li, Zhenjia; Li, Shuyuan; Lin, Shouying

    2017-05-04

    Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k -means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  2. Intelligent environmental sensing

    Mukhopadhyay, Subhas

    2015-01-01

    Developing environmental sensing and monitoring technologies become essential especially for industries that may cause severe contamination. Intelligent environmental sensing uses novel sensor techniques, intelligent signal and data processing algorithms, and wireless sensor networks to enhance environmental sensing and monitoring. It finds applications in many environmental problems such as oil and gas, water quality, and agriculture. This book addresses issues related to three main approaches to intelligent environmental sensing and discusses their latest technological developments. Key contents of the book include:   Agricultural monitoring Classification, detection, and estimation Data fusion Geological monitoring Motor monitoring Multi-sensor systems Oil reservoirs monitoring Sensor motes Water quality monitoring Wireless sensor network protocol  

  3. Hotspots ampersand other hidden targets: Probability of detection, number, frequency and area

    Vita, C.L.

    1994-01-01

    Concepts and equations are presented for making probability-based estimates of the detection probability, and the number, frequency, and area of hidden targets, including hotspots, at a given site. Targets include hotspots, which are areas of extreme or particular contamination, and any object or feature that is hidden from direct visual observation--including buried objects and geologic or hydrologic details or anomalies. Being Bayesian, results are fundamentally consistent with observational methods. Results are tools for planning or interpreting exploration programs used in site investigation or characterization, remedial design, construction, or compliance monitoring, including site closure. Used skillfully and creatively, these tools can help streamline and expedite environmental restoration, reducing time and cost, making site exploration cost-effective, and providing acceptable risk at minimum cost. 14 refs., 4 figs

  4. Research on the algorithm of infrared target detection based on the frame difference and background subtraction method

    Liu, Yun; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Hui, Mei; Liu, Xiaohua; Wu, Yijian

    2015-09-01

    As an important branch of infrared imaging technology, infrared target tracking and detection has a very important scientific value and a wide range of applications in both military and civilian areas. For the infrared image which is characterized by low SNR and serious disturbance of background noise, an innovative and effective target detection algorithm is proposed in this paper, according to the correlation of moving target frame-to-frame and the irrelevance of noise in sequential images based on OpenCV. Firstly, since the temporal differencing and background subtraction are very complementary, we use a combined detection method of frame difference and background subtraction which is based on adaptive background updating. Results indicate that it is simple and can extract the foreground moving target from the video sequence stably. For the background updating mechanism continuously updating each pixel, we can detect the infrared moving target more accurately. It paves the way for eventually realizing real-time infrared target detection and tracking, when transplanting the algorithms on OpenCV to the DSP platform. Afterwards, we use the optimal thresholding arithmetic to segment image. It transforms the gray images to black-white images in order to provide a better condition for the image sequences detection. Finally, according to the relevance of moving objects between different frames and mathematical morphology processing, we can eliminate noise, decrease the area, and smooth region boundaries. Experimental results proves that our algorithm precisely achieve the purpose of rapid detection of small infrared target.

  5. Targeted histology sampling from atypical small acinar proliferation area detected by repeat transrectal prostate biopsy

    A. V. Karman

    2017-01-01

    Full Text Available Оbjective: to define the approach to the management of patients with the detected ASAP area.Materials and methods. In the time period from 2012 through 2015, 494 patients with previously negative biopsy and remaining suspicion of prostate cancer (PCa were examined. The patients underwent repeat 24-core multifocal prostate biopsy with taking additional tissue samples from suspicious areas detected by multiparametric magnetic resonance imaging and transrectal ultrasound. An isolated ASAP area was found in 127 (25. 7 % of the 494 examined men. All of them were offered to perform repeat target transrectal biopsy of this area. Targeted transrectal ultrasound guided biopsy of the ASAP area was performed in 56 (44.1 % of the 127 patients, 53 of them being included in the final analysis.Results. PCa was diagnosed in 14 (26.4 % of the 53 patients, their mean age being 64.4 ± 6.9 years. The average level of prostate-specific antigen (PSA in PCa patients was 6.8 ± 3.0 ng/ml, in those with benign lesions – 9.3 ± 6.5 ng/ml; the percentage ratio of free/total PSA with PCa was 16.2 ± 7,8 %, with benign lesions – 23.3 ± 7.7 %; PSA density in PCa patients was 0.14 ± 0.07 ng/ml/cm3, in those with benign lesions – 0.15 ± 0.12 ng/ml/cm3. Therefore, with ASAP area being detected in repeat prostate biopsy samples, it is advisable that targeted extended biopsy of this area be performed. 

  6. Target vessel detection by epicardial ultrasound in off-pump coronary bypass surgery.

    Hayakawa, Masato; Asai, Tohru; Kinoshita, Takeshi; Suzuki, Tomoaki; Shiraishi, Shoichiro

    2013-01-01

    The detection of embedded coronary arteries is difficult especially in off-pump coronary bypass surgery. From June 2010, we introduced high-frequency epicardial ultrasound (ECUS) to assess and evaluate embedded arteries during off-pump coronary bypass surgery. Between June 2010 and June 2011, a total of 89 consecutive patients underwent isolated coronary bypass surgery at our institution. The patients consisted of 72 men and 17 women with a mean age of 67.9 years. We routinely use the VeriQC system (MediStim, Oslo, Norway) to detect the target vessels in the operation. The patients were assigned to one of two groups, depending on whether ECUS was used in the operation (n = 10, ECUS group) or not (n = 79, non-ECUS group). We analyzed the impact of introducing the ECUS in terms of operative outcome. All patients underwent revascularization using the off-pump technique without emergent conversion to cardiopulmonary bypass during surgery. The total number of distal anastomoses was 299, and 12 target vessels could not be identified either visually or on palpation. Thus, the frequency of the embedded coronary arteries was 4.01% (12/299 cases). The preoperative profiles of the two groups were not significantly different. Operation time was significantly longer in the ECUS group (P = 0.02). There were no significant differences in postoperative outcome between the two groups. In the present study, in which the target coronary arteries could not be detected either visually or on palpation in 12 (4.01%) of 299 cases, the use of high-frequency ECUS allowed all patients to undergo off-pump coronary bypass surgery without conversion to cardiopulmonary bypass during the operation. High-frequency ECUS is therefore useful in off-pump coronary bypass surgery.

  7. Detection of Accelerating Targets in Clutter Using a De-Chirping Technique

    2014-06-01

    Cubic Phase Functions By definition the Wigner -Ville distribution, W , of a signal x(n) can be written generally as W (n, ω) = ∑ m x(n+m/2)x∗(n−m/2)e−jωm...which is known as the Radon- Wigner transform (RWT), as discussed by Wood [3] and Lohmann [16]. Similarly, the Ambiguity Function , AF, for the signal...the integrated cubic phase functions , at least in the case of single target detection with the linear fre- quency modulated (LFM) waveform. This is

  8. Intelligent Home Control System Based on ARM10

    Chen, G. X.; Jiang, J.; Zhong, L. H.

    2017-10-01

    Intelligent home is becoming the hot spot of social attention in the 21st century. When it is in China, it is a really new industry. However, there is no doubt that Intelligent home will become a new economic growth point of social development; it will change the life-style of human being. To develop the intelligent home, we should keep up with the development trend of technology. This is the reason why I talk about the intelligent home control system here. In this paper, intelligent home control system is designed for alarm and remote control on gas- leaking, fire disaster, earthquake prediction, etc., by examining environmental changes around house. When the Intelligent home control system has detected an accident occurs, the processor will communicate with the GSM module, informing the house keeper the occurrence of accident. User can receive and send the message to the system to cut the power by mobile phone. The system can get access to DCCthrough ARM10 JTAG interface, using DCC to send and receive messages. At the same time, the debugger on the host is mainly used to receive the user’s command and send it to the debug component in the target system. The data that returned from the target system is received and displayed to the user in a certain format.

  9. Detection method of elastic scattering in the Coulomb interference region: scintillation target

    Azaiez, Hamza.

    1981-01-01

    Measurement of polarization in (p-p) elastic scattering in the Coulomb interference region is considered as a valid method for calibrating high energy polarized proton beams. Possibility of using a scintillation target to detect low energy recoil protons in this /t/ region has been studied by using a 4 GeV/c π - beam from CERN PS. The results obtained with a steack of thin plastic scintillators, each 1 mm thick, showed the feasibility of detecting recoil protons in a /t/ range as low a 5.10 -3 (GeV/c) 2 . This method thus confirmed experimentally can be used also to measure, using a polarized beam, polarization in Coulomb interference region [fr

  10. Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

    Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar; Baker, Erin M.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

    2018-05-01

    Abstract. In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150–250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMSCID- TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements

  11. Infrared maritime target detection using a probabilistic single Gaussian model of sea clutter in Fourier domain

    Zhou, Anran; Xie, Weixin; Pei, Jihong; Chen, Yapei

    2018-02-01

    For ship targets detection in cluttered infrared image sequences, a robust detection method, based on the probabilistic single Gaussian model of sea background in Fourier domain, is put forward. The amplitude spectrum sequences at each frequency point of the pure seawater images in Fourier domain, being more stable than the gray value sequences of each background pixel in the spatial domain, are regarded as a Gaussian model. Next, a probability weighted matrix is built based on the stability of the pure seawater's total energy spectrum in the row direction, to make the Gaussian model more accurate. Then, the foreground frequency points are separated from the background frequency points by the model. Finally, the false-alarm points are removed utilizing ships' shape features. The performance of the proposed method is tested by visual and quantitative comparisons with others.

  12. Harmonic pulsed excitation and motion detection of a vibrating reflective target.

    Urban, Matthew W; Greenleaf, James F

    2008-01-01

    Elasticity imaging is an emerging medical imaging modality. Methods involving acoustic radiation force excitation and pulse-echo ultrasound motion detection have been investigated to assess the mechanical response of tissue. In this work new methods for dynamic radiation force excitation and motion detection are presented. The theory and model for harmonic motion detection of a vibrating reflective target are presented. The model incorporates processing of radio frequency data acquired using pulse-echo ultrasound to measure harmonic motion with amplitudes ranging from 100 to 10,000 nm. A numerical study was performed to assess the effects of different parameters on the accuracy and precision of displacement amplitude and phase estimation and showed how estimation errors could be minimized. Harmonic pulsed excitation is introduced as a multifrequency radiation force excitation method that utilizes ultrasound tonebursts repeated at a rate f(r). The radiation force, consisting of frequency components at multiples of f(r), is generated using 3.0 MHz ultrasound, and motion detection is performed simultaneously with 9.0 MHz pulse-echo ultrasound. A parameterized experimental analysis showed that displacement can be measured with small errors for motion with amplitudes as low as 100 nm. The parameterized numerical and experimental analyses provide insight into how to optimize acquisition parameters to minimize measurement errors.

  13. Detection, Quantification, and Microlocalisation of Targets of Pesticides Using Microchannel Plate Autoradiographic Imagers

    Mabruka H. Tarhoni

    2011-10-01

    Full Text Available Organophosphorus (OP compounds are a diverse chemical group that includes nerve agents and pesticides. They share a common chemical signature that facilitates their binding and adduction of acetylcholinesterase (AChE within nerve synapses to induce cholinergic toxicity. However, this group diversity results in non-uniform binding and inactivation of other secondary protein targets, some of which may be adducted and protein activity influenced, even when only a relatively minor portion of tissue AChE is inhibited. The determination of individual OP protein binding targets has been hampered by the sensitivity of methods of detection and quantification of protein-pesticide adducts. We have overcome this limitation by the employment of a microchannel plate (MCP autoradiographic detector to monitor a radiolabelled OP tracer compound. We preincubated rat thymus tissue in vitro with the OP pesticides, azamethiphos-oxon, chlorfenvinphos-oxon, chlorpyrifos-oxon, diazinon-oxon, and malaoxon, and then subsequently radiolabelled the free OP binding sites remaining with 3H-diisopropylfluorophosphate (3H-DFP. Proteins adducted by OP pesticides were detected as a reduction in 3H-DFP radiolabelling after protein separation by one dimensional polyacrylamide gel electrophoresis and quantitative digital autoradiography using the MCP imager. Thymus tissue proteins of molecular weights ~28 kDa, 59 kDa, 66 kDa, and 82 kDa displayed responsiveness to adduction by this panel of pesticides. The 59 kDa protein target (previously putatively identified as carboxylesterase I was only significantly adducted by chlorfenvinphos-oxon (p < 0.001, chlorpyrifos-oxon (p < 0.0001, and diazinon-oxon (p < 0.01, the 66 kDa protein target (previously identified as serum albumin similarly only adducted by the same three pesticides (p < 0.0001, (p < 0.001, and (p < 0.01, and the 82 kDa protein target (previously identified as acyl peptide hydrolase only adducted by chlorpyrifos-oxon (p

  14. Artificial Intelligence.

    Information Technology Quarterly, 1985

    1985-01-01

    This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

  15. Competitive Intelligence.

    Bergeron, Pierrette; Hiller, Christine A.

    2002-01-01

    Reviews the evolution of competitive intelligence since 1994, including terminology and definitions and analytical techniques. Addresses the issue of ethics; explores how information technology supports the competitive intelligence process; and discusses education and training opportunities for competitive intelligence, including core competencies…

  16. Intelligent multimedia surveillance current trends and research

    Atrey, Pradeep K; Cavallaro, Andrea

    2013-01-01

    Intelligent multimedia surveillance concerns the analysis of multiple sensing inputs including video and audio streams, radio-frequency identification (RFID), and depth data. These data are processed for the automated detection and tracking of people, vehicles, and other objects. The goal is to locate moving targets, to understand their behavior, and to detect suspicious or abnormal activities for crime prevention. Despite its benefits, there is societal apprehension regarding the use of such technology, so an important challenge in this research area is to balance public safety and privacy.

  17. Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

    Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.; Baker, Erin S.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

    2017-09-01

    In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150-250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10-200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins. [Figure not available: see fulltext.

  18. A Plant Immune Receptor Detects Pathogen Effectors that Target WRKY Transcription Factors.

    Sarris, Panagiotis F; Duxbury, Zane; Huh, Sung Un; Ma, Yan; Segonzac, Cécile; Sklenar, Jan; Derbyshire, Paul; Cevik, Volkan; Rallapalli, Ghanasyam; Saucet, Simon B; Wirthmueller, Lennart; Menke, Frank L H; Sohn, Kee Hoon; Jones, Jonathan D G

    2015-05-21

    Defense against pathogens in multicellular eukaryotes depends on intracellular immune receptors, yet surveillance by these receptors is poorly understood. Several plant nucleotide-binding, leucine-rich repeat (NB-LRR) immune receptors carry fusions with other protein domains. The Arabidopsis RRS1-R NB-LRR protein carries a C-terminal WRKY DNA binding domain and forms a receptor complex with RPS4, another NB-LRR protein. This complex detects the bacterial effectors AvrRps4 or PopP2 and then activates defense. Both bacterial proteins interact with the RRS1 WRKY domain, and PopP2 acetylates lysines to block DNA binding. PopP2 and AvrRps4 interact with other WRKY domain-containing proteins, suggesting these effectors interfere with WRKY transcription factor-dependent defense, and RPS4/RRS1 has integrated a "decoy" domain that enables detection of effectors that target WRKY proteins. We propose that NB-LRR receptor pairs, one member of which carries an additional protein domain, enable perception of pathogen effectors whose function is to target that domain. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Alteration mineral mapping in inaccessible regions using target detection algorithms to ASTER data

    Pour, A B; Hashim, M; Park, Y

    2017-01-01

    In this study, the applications of target detection algorithms such as Constrained Energy Minimization (CEM), Orthogonal Subspace Projection (OSP) and Adaptive Coherence Estimator (ACE) to shortwave infrared bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was investigated to extract geological information for alteration mineral mapping in poorly exposed lithologies in inaccessible domains. The Oscar II coast area north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. It is an inaccessible region due to the remoteness of many rock exposures and the necessity to travel over sever mountainous and glacier-cover terrains for geological field mapping and sample collection. Fractional abundance of alteration minerals such as muscovite, kaolinite, illite, montmorillonite, epidote, chlorite and biotite were identified in alteration zones using CEM, OSP and ACE algorithms in poorly mapped and unmapped zones at district scale for the Oscar II coast area. The results of this investigation demonstrated the applicability of ASTER shortwave infrared spectral data for lithological and alteration mineral mapping in poorly exposed lithologies and inaccessible regions, particularly using the image processing algorithms that are capable to detect sub-pixel targets in the remotely sensed images, where no prior information is available. (paper)

  20. Workflow for the Targeted and Untargeted Detection of Small Metabolites in Fish Skin Mucus

    Lada Ivanova

    2018-06-01

    Full Text Available The skin mucus of fish is in permanent contact with the aquatic environment. Data from the analysis of the chemical composition of skin mucus could potentially be used for monitoring the health status of the fish. Knowledge about mucus composition or change in composition over time could also contribute to understanding the aetiology of certain diseases. The objective of the present study was the development of a workflow for non-invasive sampling of skin mucus from farmed salmon (Salmo salar for the targeted and untargeted detection of small metabolites. Skin mucus was either scraped off, wiped off using medical wipes, or the mucus’ water phase was absorbed using the same type of medical wipes that was used for the wiping method. Following a simple filtration step, the obtained mucus samples were subjected to hydrophilic interaction chromatography coupled to high-resolution mass spectrometry. Post-acquisition processing included the targeted analysis of 86 small metabolites, of which up to 60 were detected in absorbed mucus. Untargeted analysis of the mucus samples from equally treated salmon revealed that the total variation of the metabolome was lowest in absorbed mucus and highest in the scraped mucus. Thus, future studies including small-molecule metabolomics of skin mucus in fish would benefit from a sampling regime employing absorption of the water phase in order to minimize the bias related to the sampling step. Furthermore, the absorption method is also a less invasive approach allowing for repetitive sampling within short time intervals.

  1. A false-positive detection bias as a function of state and trait schizotypy in interaction with intelligence

    Phillip eGrant

    2014-09-01

    Full Text Available Hallucinatory experiences are by far not limited to patients with clinical psychosis. A number of internal and external factors may bring about such experiences in healthy individuals, whereby the personality trait of (positive schizotypy is a major mediator of individual differences. Psychotic experiences are defined as associating abnormal meaning to real but objectively irrelevant perceptions. Especially the ambiguity of a stimulus correlates positively with the likelihood of abnormal interpretation, and intelligence is believed to have an important influence and act as protective against clinical psychosis in highly schizotypic individuals.In this study we presented 131 healthy participants with 216 15-letter strings containing either a word, a non-word or only random letters and asked them to report, whether or not they believed to have seen a word. The aim was to replicate findings that participants with high values in positive schizotypy on the trait-level make more false-positive errors and assess the role of stimulus-ambiguity and verbal intelligence. Additionally, we wanted to examine whether the same effect could be shown for indices of state schizotypy.Our results support findings that both state and trait positive schizotypy explain significant variance in seeing things that are not there and that the properties of individual stimuli have additional strong effects on the false-positive hit rates. Finally, we found that verbal intelligence and positive schizotypy interact with stimulus-ambiguity in the production of false-positive perceptions.

  2. A false-positive detection bias as a function of state and trait schizotypy in interaction with intelligence.

    Grant, Phillip; Balser, Mona; Munk, Aisha Judith Leila; Linder, Jens; Hennig, Juergen

    2014-01-01

    Hallucinatory experiences are by far not limited to patients with clinical psychosis. A number of internal and external factors may bring about such experiences in healthy individuals, whereby the personality trait of (positive) schizotypy is a major mediator of individual differences. Psychotic experiences are defined as associating abnormal meaning to real but objectively irrelevant perceptions. Especially, the ambiguity of a stimulus correlates positively with the likelihood of abnormal interpretation, and intelligence is believed to have an important influence and act as protective against clinical psychosis in highly schizotypic individuals. In this study, we presented 131 healthy participants with 216 15-letter strings containing either a word, a non-word, or only random letters and asked them to report, whether or not they believed to have seen a word. The aim was to replicate findings that participants with high values in positive schizotypy on the trait-level make more false-positive errors and assess the role of stimulus-ambiguity and verbal intelligence. Additionally, we wanted to examine whether the same effect could be shown for indices of state schizotypy. Our results support findings that both state and trait positive schizotypy explain significant variance in "seeing things that are not there" and that the properties of individual stimuli have additional strong effects on the false-positive hit rates. Finally, we found that verbal intelligence and positive schizotypy interact with stimulus-ambiguity in the production of false-positive perceptions.

  3. REVISED STELLAR PROPERTIES OF KEPLER TARGETS FOR THE QUARTER 1-16 TRANSIT DETECTION RUN

    Huber, Daniel [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Aguirre, Victor Silva [Stellar Astrophysics Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, DK-8000 Aarhus C (Denmark); Matthews, Jaymie M. [Department of Physics and Astronomy, University of British Columbia, Vancouver (Canada); Pinsonneault, Marc H. [Department of Astronomy, Ohio State University, OH 43210 (United States); Gaidos, Eric [Department of Geology and Geophysics, University of Hawaii at Manoa, Honolulu, HI 96822 (United States); García, Rafael A. [Laboratoire AIM, CEA/DSM-CNRS, Université Paris 7 Diderot, IRFU/SAp, Centre de Saclay, F-91191 Gif-sur-Yvette (France); Hekker, Saskia [Max-Planck-Institut für Sonnensystemforschung, Justus-von-Liebig-Weg 3, D-37077 Göttingen (Germany); Mathur, Savita [Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, CO 80301 (United States); Mosser, Benoit [LESIA, CNRS, Université Pierre et Marie Curie, Université Denis, Diderot, Observatoire de Paris, F-92195 Meudon cedex (France); Torres, Guillermo [Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138 (United States); Bastien, Fabienne A. [Department of Physics and Astronomy, Vanderbilt University, 1807 Station B, Nashville, TN 37235 (United States); Basu, Sarbani [Department of Astronomy, Yale University, New Haven, CT 06511 (United States); Bedding, Timothy R. [Sydney Institute for Astronomy (SIfA), School of Physics, University of Sydney, NSW 2006 (Australia); Chaplin, William J. [School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT (United Kingdom); Demory, Brice-Olivier [Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States); Fleming, Scott W., E-mail: daniel.huber@nasa.gov [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); and others

    2014-03-01

    We present revised properties for 196,468 stars observed by the NASA Kepler mission and used in the analysis of Quarter 1-16 (Q1-Q16) data to detect and characterize transiting planets. The catalog is based on a compilation of literature values for atmospheric properties (temperature, surface gravity, and metallicity) derived from different observational techniques (photometry, spectroscopy, asteroseismology, and exoplanet transits), which were then homogeneously fitted to a grid of Dartmouth stellar isochrones. We use broadband photometry and asteroseismology to characterize 11,532 Kepler targets which were previously unclassified in the Kepler Input Catalog (KIC). We report the detection of oscillations in 2762 of these targets, classifying them as giant stars and increasing the number of known oscillating giant stars observed by Kepler by ∼20% to a total of ∼15,500 stars. Typical uncertainties in derived radii and masses are ∼40% and ∼20%, respectively, for stars with photometric constraints only, and 5%-15% and ∼10% for stars based on spectroscopy and/or asteroseismology, although these uncertainties vary strongly with spectral type and luminosity class. A comparison with the Q1-Q12 catalog shows a systematic decrease in radii of M dwarfs, while radii for K dwarfs decrease or increase depending on the Q1-Q12 provenance (KIC or Yonsei-Yale isochrones). Radii of F-G dwarfs are on average unchanged, with the exception of newly identified giants. The Q1-Q16 star properties catalog is a first step toward an improved characterization of all Kepler targets to support planet-occurrence studies.

  4. Sensitive targeted multiple protein quantification based on elemental detection of Quantum Dots

    Montoro Bustos, Antonio R.; Garcia-Cortes, Marta [Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, Oviedo 33006 (Spain); González-Iglesias, Hector [Fundación de Investigación Oftalmológica, Instituto Oftalmológico Fernandez-Vega, Avenida Doctores Fernández-Vega, 34, Oviedo 33012 (Spain); Ruiz Encinar, Jorge, E-mail: ruizjorge@uniovi.es [Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, Oviedo 33006 (Spain); Costa-Fernández, José M. [Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, Oviedo 33006 (Spain); Coca-Prados, Miguel [Fundación de Investigación Oftalmológica, Instituto Oftalmológico Fernandez-Vega, Avenida Doctores Fernández-Vega, 34, Oviedo 33012 (Spain); Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, CT 06510 (United States); Sanz-Medel, Alfredo, E-mail: asm@uniovi.es [Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, Oviedo 33006 (Spain)

    2015-06-16

    Highlights: • Novel generic platform for multiparametric quantification of proteins. • QDs labeling and ICP-MS detection allow significant analytical signal amplification. • ICP-MS mass balances information provided an internal validation of the immunoassay. • Multiparametric determination of 5 proteins in human serum samples. • ICP-MS reduced matrix effects as compared to other conventional detection techniques. - Abstract: A generic strategy based on the use of CdSe/ZnS Quantum Dots (QDs) as elemental labels for protein quantification, using immunoassays with elemental mass spectrometry (ICP-MS), detection is presented. In this strategy, streptavidin modified QDs (QDs-SA) are bioconjugated to a biotinylated secondary antibody (b-Ab{sub 2}). After a multi-technique characterization of the synthesized generic platform (QDs-SA-b-Ab{sub 2}) it was applied to the sequential quantification of five proteins (transferrin, complement C3, apolipoprotein A1, transthyretin and apolipoprotein A4) at different concentration levels in human serum samples. It is shown how this generic strategy does only require the appropriate unlabeled primary antibody for each protein to be detected. Therefore, it introduces a way out to the need for the cumbersome and specific bioconjugation of the QDs to the corresponding specific recognition antibody for every target analyte (protein). Results obtained were validated with those obtained using UV–vis spectrophotometry and commercial ELISA Kits. As expected, ICP-MS offered one order of magnitude lower DL (0.23 fmol absolute for transferrin) than the classical spectrophotometric detection (3.2 fmol absolute). ICP-MS precision and detection limits, however turned out to be compromised by procedural blanks. The full analytical performance of the ICP-MS-based immunoassay proposed was assessed for detection of transferrin (Tf), present at the low ng mL{sup −1} range in a complex “model” synthetic matrix, where the total protein

  5. Intelligence Ethics:

    Rønn, Kira Vrist

    2016-01-01

    Questions concerning what constitutes a morally justified conduct of intelligence activities have received increased attention in recent decades. However, intelligence ethics is not yet homogeneous or embedded as a solid research field. The aim of this article is to sketch the state of the art...... of intelligence ethics and point out subjects for further scrutiny in future research. The review clusters the literature on intelligence ethics into two groups: respectively, contributions on external topics (i.e., the accountability of and the public trust in intelligence agencies) and internal topics (i.......e., the search for an ideal ethical framework for intelligence actions). The article concludes that there are many holes to fill for future studies on intelligence ethics both in external and internal discussions. Thus, the article is an invitation – especially, to moral philosophers and political theorists...

  6. Time reversal optical tomography and decomposition methods for detection and localization of targets in highly scattering turbid media

    Wu, Binlin

    New near-infrared (NIR) diffuse optical tomography (DOT) approaches were developed to detect, locate, and image small targets embedded in highly scattering turbid media. The first approach, referred to as time reversal optical tomography (TROT), is based on time reversal (TR) imaging and multiple signal classification (MUSIC). The second approach uses decomposition methods of non-negative matrix factorization (NMF) and principal component analysis (PCA) commonly used in blind source separation (BSS) problems, and compare the outcomes with that of optical imaging using independent component analysis (OPTICA). The goal is to develop a safe, affordable, noninvasive imaging modality for detection and characterization of breast tumors in early growth stages when those are more amenable to treatment. The efficacy of the approaches was tested using simulated data, and experiments involving model media and absorptive, scattering, and fluorescent targets, as well as, "realistic human breast model" composed of ex vivo breast tissues with embedded tumors. The experimental arrangements realized continuous wave (CW) multi-source probing of samples and multi-detector acquisition of diffusely transmitted signal in rectangular slab geometry. A data matrix was generated using the perturbation in the transmitted light intensity distribution due to the presence of absorptive or scattering targets. For fluorescent targets the data matrix was generated using the diffusely transmitted fluorescence signal distribution from the targets. The data matrix was analyzed using different approaches to detect and characterize the targets. The salient features of the approaches include ability to: (a) detect small targets; (b) provide three-dimensional location of the targets with high accuracy (~within a millimeter or 2); and (c) assess optical strength of the targets. The approaches are less computation intensive and consequently are faster than other inverse image reconstruction methods that

  7. Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

    Patcharoen Theerasak

    2018-04-01

    Full Text Available This paper describes the combination of discrete wavelet transforms (DWT and artificial intelligence (AI, which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51 and unbalanced current (60C protection relay for an application of shunt capacitor bank protection in the future.

  8. Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

    Patcharoen, Theerasak; Yoomak, Suntiti; Ngaopitakkul, Atthapol; Pothisarn, Chaichan

    2018-04-01

    This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

  9. A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection

    Weijie Xia

    2016-01-01

    Full Text Available The generalized Radon-Fourier transform (GRFT has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space. Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL which will cause serious false alarms. The BSSL learning-based particle swarm optimization (BPSO has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously. However, the BPSO suffers from an apparent loss in detection performance compared with GRFT. In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO is proposed. In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon. Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment. Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost. Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method.

  10. Wildlife detection dog training: A case study on achieving generalization between target odor variations while retaining specificity

    Oldenburg, Cor; Schoon, Adee; Heitkönig, I.M.A.

    2016-01-01

    Wildlife detection dogs are required to correctly discriminate target wildlife species odor from nontarget
    species odors (specificity), while enabling some degree of target odor variation (generality). Because
    there is no standardized training protocol, and little knowledge on training

  11. Simultaneous detection of multiple DNA targets by integrating dual-color graphene quantum dot nanoprobes and carbon nanotubes.

    Qian, Zhaosheng; Shan, Xiaoyue; Chai, Lujing; Chen, Jianrong; Feng, Hui

    2014-12-01

    Simultaneous detection of multiple DNA targets was achieved based on a biocompatible graphene quantum dots (GQDs) and carbon nanotubes (CNTs) platform through spontaneous assembly between dual-color GQD-based probes and CNTs and subsequently self-recognition between DNA probes and targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. The effects of incidentally learned temporal and spatial predictability on response times and visual fixations during target detection and discrimination.

    Melissa R Beck

    Full Text Available Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response times when target predictability is embedded within the inter-trial relationships. The current research examined the combined effects of spatial and temporal target predictability on reaction time (RT and allocation of overt attention in a sustained attention task. Participants responded as quickly as possible to stimuli while their RT and eye movements were measured. Target temporal and spatial predictability were manipulated by altering the number of: 1 different time intervals between a response and the next target; and 2 possible spatial locations of the target. The effects of target predictability on target detection (Experiment 1 and target discrimination (Experiment 2 were tested. For both experiments, shorter RTs as target predictability increased across both space and time were found. In addition, the influences of spatial and temporal target predictability on RT and the overt allocation of attention were task dependent; suggesting that effective orienting of attention relies on both spatial and temporal predictability. These results indicate that stimulus predictability can be increased without overt cues and detected purely through inter-trial relationships over the course of repeated stimulus presentations.

  13. Intelligence Naturelle et Intelligence Artificielle

    Dubois, Daniel

    2011-01-01

    Cet article présente une approche systémique du concept d’intelligence naturelle en ayant pour objectif de créer une intelligence artificielle. Ainsi, l’intelligence naturelle, humaine et animale non-humaine, est une fonction composée de facultés permettant de connaître et de comprendre. De plus, l'intelligence naturelle reste indissociable de la structure, à savoir les organes du cerveau et du corps. La tentation est grande de doter les systèmes informatiques d’une intelligence artificielle ...

  14. High-sensitivity assay for Hg (II) and Ag (I) ion detection: A new class of droplet digital PCR logic gates for an intelligent DNA calculator.

    Cheng, Nan; Zhu, Pengyu; Xu, Yuancong; Huang, Kunlun; Luo, Yunbo; Yang, Zhansen; Xu, Wentao

    2016-10-15

    The first example of droplet digital PCR logic gates ("YES", "OR" and "AND") for Hg (II) and Ag (I) ion detection has been constructed based on two amplification events triggered by a metal-ion-mediated base mispairing (T-Hg(II)-T and C-Ag(I)-C). In this work, Hg(II) and Ag(I) were used as the input, and the "true" hierarchical colors or "false" green were the output. Through accurate molecular recognition and high sensitivity amplification, positive droplets were generated by droplet digital PCR and viewed as the basis of hierarchical digital signals. Based on this principle, YES gate for Hg(II) (or Ag(I)) detection, OR gate for Hg(II) or Ag(I) detection and AND gate for Hg(II) and Ag(I) detection were developed, and their sensitively and selectivity were reported. The results indicate that the ddPCR logic system developed based on the different indicators for Hg(II) and Ag(I) ions provides a useful strategy for developing advanced detection methods, which are promising for multiplex metal ion analysis and intelligent DNA calculator design applications. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Permafrost-An alternative target material for ultra-high energy neutrino detection?

    Nahnhauer, R.; Rostovtsev, A.A.; Tosi, D.

    2008-01-01

    The interest in the detection of cosmic neutrinos with energies above 10 17 eV has increased considerably in recent years. Possible target materials for in-matter arrays of ∼100 km 3 size under discussion are water, ice and rock salt. Here we propose to investigate permafrost as an additional alternative, covering ∼20% of Earth land surface and reaching down to more than 1000 m depth at certain locations. If sufficiently large attenuation lengths for radio and acoustic signals can be demonstrated by in-situ measurements, the construction of a large hybrid array within this material may be possible in the Northern Hemisphere. Properties and problems of a possible location in Siberia are discussed below. Some acoustic data are compared with laboratory measurements using 'artificial' permafrost

  16. Detecting and Georegistering Moving Ground Targets in Airborne QuickSAR via Keystoning and Multiple-Phase Center Interferometry

    R. P. Perry

    2008-03-01

    Full Text Available SAR images experience significant range walk and, without some form of motion compensation, can be quite blurred. The MITRE-developed Keystone formatting simultaneously and automatically compensates for range walk due to the radial velocity component of each moving target, independent of the number of targets or the value of each target's radial velocity with respect to the ground. Target radial motion also causes moving targets in synthetic aperture radar images to appear at locations offset from their true instantaneous locations on the ground. In a multichannel radar, the interferometric phase values associated with all nonmoving points on the ground appear as a continuum of phase differences while the moving targets appear as interferometric phase discontinuities. By multiple threshold comparisons and grouping of pixels within the intensity and the phase images, we show that it is possible to reliably detect and accurately georegister moving targets within short-duration SAR (QuickSAR images.

  17. SUPERPIXEL BASED FACTOR ANALYSIS AND TARGET TRANSFORMATION METHOD FOR MARTIAN MINERALS DETECTION

    X. Wu

    2018-04-01

    Full Text Available The Factor analysis and target transformation (FATT is an effective method to test for the presence of particular mineral on Martian surface. It has been used both in thermal infrared (Thermal Emission Spectrometer, TES and near-infrared (Compact Reconnaissance Imaging Spectrometer for Mars, CRISM hyperspectral data. FATT derived a set of orthogonal eigenvectors from a mixed system and typically selected first 10 eigenvectors to least square fit the library mineral spectra. However, minerals present only in a limited pixels will be ignored because its weak spectral features compared with full image signatures. Here, we proposed a superpixel based FATT method to detect the mineral distributions on Mars. The simple linear iterative clustering (SLIC algorithm was used to partition the CRISM image into multiple connected image regions with spectral homogeneous to enhance the weak signatures by increasing their proportion in a mixed system. A least square fitting was used in target transformation and performed to each region iteratively. Finally, the distribution of the specific minerals in image was obtained, where fitting residual less than a threshold represent presence and otherwise absence. We validate our method by identifying carbonates in a well analysed CRISM image in Nili Fossae on Mars. Our experimental results indicate that the proposed method work well both in simulated and real data sets.

  18. Better target detection in the presence of collinear flankers under high working memory load

    Jan W. De Fockert

    2014-10-01

    Full Text Available There are multiple ways in which working memory can influence selective attention. Aside from the content-specific effects of working memory on selective attention, whereby attention is more likely to be directed towards information that matches the contents of working memory, the mere level of load on working memory has also been shown to have an effect on selective attention. Specifically, high load on working memory is associated with increased processing of irrelevant information. In most demonstrations of the effect to-date, this has led to impaired target performance, leaving open the possibility that the effect partly reflects an increase in general task difficulty under high load. Here we show that working memory load can result in a performance gain when processing of distracting information aids target performance. The facilitation in the detection of a low-contrast Gabor stimulus in the presence of collinear flanking Gabors was greater when load on a concurrent working memory task was high, compared to low. This finding suggests that working memory can interact with selective attention at an early stage in visual processing.

  19. A New Methodology for 3D Target Detection in Automotive Radar Applications

    Fabio Baselice

    2016-04-01

    Full Text Available Today there is a growing interest in automotive sensor monitoring systems. One of the main challenges is to make them an effective and valuable aid in dangerous situations, improving transportation safety. The main limitation of visual aid systems is that they do not produce accurate results in critical visibility conditions, such as in presence of rain, fog or smoke. Radar systems can greatly help in overcoming such limitations. In particular, imaging radar is gaining interest in the framework of Driver Assistance Systems (DAS. In this manuscript, a new methodology able to reconstruct the 3D imaged scene and to detect the presence of multiple targets within each line of sight is proposed. The technique is based on the use of Compressive Sensing (CS theory and produces the estimation of multiple targets for each line of sight, their range distance and their reflectivities. Moreover, a fast approach for 2D focus based on the FFT algorithm is proposed. After the description of the proposed methodology, different simulated case studies are reported in order to evaluate the performances of the proposed approach.

  20. Science + Technology = Intelligence on Target

    2008-03-01

    that now it includes almost all states in the West except for perhaps California. Th is is caused by a prion. Prion is a protein with no nucleic acid...kind of a dinosaur or a pachyderm, kind of lumbering along that blue shallow curve where we may invest ten years and $10 billion to get a ten percent

  1. Structure Study on Electromagnetic Navigation Intelligent Vehicle Detection and Control System%电磁导航式智能车控制系统研究

    侯代坡; 孔琳琳; 王烁; 杨成; 韩致信

    2013-01-01

    With "the Freescale cup intelligent car racing" of the electromagnetic navigation intelligent car' s electromagnetic signal as the research and study object. The inductance coil and operational amplifier components selection and circuit design is discussed in detail; the sensor layout methods on the influence to the precision of detection is essential analyzed, a kind of "piecewise interpolation label" direction control scheme is put forward.%以“飞思卡尔智能车”电磁组比赛中的磁导航方式的电磁信号为研究和分析对象,对电感线圈和运算放大器等元器件的选择和电路设计方面作了详细论述;重点分析传感器的布置方式对检测精度的影响,提出了一种“分段插值标号”的方向控制方案.

  2. A Tumor-Targeted Nanodelivery System to Improve Early MRI Detection of Cancer

    Kathleen F. Pirollo

    2006-01-01

    Full Text Available The development of improvements in magnetic resonance imaging (MRI that would enhance sensitivity, leading to earlier detection of cancer and visualization of metastatic disease, is an area of intense exploration. We have devised a tumor-targeting, liposomal nanodelivery platform for use in gene medicine. This systemically administered nanocomplex has been shown to specifically and efficiently deliver both genes and oligonucleotides to primary and metastatic tumor cells, resulting in significant tumor growth inhibition and even tumor regression. Here we examine the effect on MRI of incorporating conventional MRI contrast agent Magnevist® into our anti-transferrin receptor single-chain antibody (TfRscFv liposomal complex. Both in vitro and in an in vivo orthotopic mouse model of pancreatic cancer, we show increased resolution and image intensity with the complexed Magnevist®. Using advanced microscopy techniques (scanning electron microscopy and scanning probe microscopy, we also established that the Magnevist® is in fact encapsulated by the liposome in the complex and that the complex still retains its nanodimensional size. These results demonstrate that this TfRscFv-liposome-Magnevist® nanocomplex has the potential to become a useful tool in early cancer detection.

  3. Improved Deep Belief Networks (IDBN Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

    Lianpeng Li

    2018-04-01

    Full Text Available In recent years, the robots, especially heavy-duty robots, have become the hardest-hit areas for targeted attacks. These attacks come from both the cyber-domain and the physical-domain. In order to improve the security of heavy-duty robots, this paper proposes a detection and mitigation mechanism which based on improved deep belief networks (IDBN and dynamic model. The detection mechanism consists of two parts: (1 IDBN security checks, which can detect targeted attacks from the cyber-domain; (2 Dynamic model and security detection, used to detect the targeted attacks which can possibly lead to a physical-domain damage. The mitigation mechanism was established on the base of the detection mechanism and could mitigate transient and discontinuous attacks. Moreover, a test platform was established to carry out the performance evaluation test for the proposed mechanism. The results show that, the detection accuracy for the attack of the cyber-domain of IDBN reaches 96.2%, and the detection accuracy for the attack of physical-domain control commands reaches 94%. The performance evaluation test has verified the reliability and high efficiency of the proposed detection and mitigation mechanism for heavy-duty robots.

  4. Detection of short repeated genomic sequences on metaphase chromosomes using padlock probes and target primed rolling circle DNA synthesis

    Stougaard Magnus

    2007-11-01

    Full Text Available Abstract Background In situ detection of short sequence elements in genomic DNA requires short probes with high molecular resolution and powerful specific signal amplification. Padlock probes can differentiate single base variations. Ligated padlock probes can be amplified in situ by rolling circle DNA synthesis and detected by fluorescence microscopy, thus enhancing PRINS type reactions, where localized DNA synthesis reports on the position of hybridization targets, to potentially reveal the binding of single oligonucleotide-size probe molecules. Such a system has been presented for the detection of mitochondrial DNA in fixed cells, whereas attempts to apply rolling circle detection to metaphase chromosomes have previously failed, according to the literature. Methods Synchronized cultured cells were fixed with methanol/acetic acid to prepare chromosome spreads in teflon-coated diagnostic well-slides. Apart from the slide format and the chromosome spreading everything was done essentially according to standard protocols. Hybridization targets were detected in situ with padlock probes, which were ligated and amplified using target primed rolling circle DNA synthesis, and detected by fluorescence labeling. Results An optimized protocol for the spreading of condensed metaphase chromosomes in teflon-coated diagnostic well-slides was developed. Applying this protocol we generated specimens for target primed rolling circle DNA synthesis of padlock probes recognizing a 40 nucleotide sequence in the male specific repetitive satellite I sequence (DYZ1 on the Y-chromosome and a 32 nucleotide sequence in the repetitive kringle IV domain in the apolipoprotein(a gene positioned on the long arm of chromosome 6. These targets were detected with good efficiency, but the efficiency on other target sites was unsatisfactory. Conclusion Our aim was to test the applicability of the method used on mitochondrial DNA to the analysis of nuclear genomes, in particular as

  5. Integrated detection, estimation, and guidance in pursuit of a maneuvering target

    Dionne, Dany

    The thesis focuses on efficient solutions of non-cooperative pursuit-evasion games with imperfect information on the state of the system. This problem is important in the context of interception of future maneuverable ballistic missiles. However, the theoretical developments are expected to find application to a broad class of hybrid control and estimation problems in industry. The validity of the results is nevertheless confirmed using a benchmark problem in the area of terminal guidance. A specific interception scenario between an incoming target with no information and a single interceptor missile with noisy measurements is analyzed in the form of a linear hybrid system subject to additive abrupt changes. The general research is aimed to achieve improved homing accuracy by integrating ideas from detection theory, state estimation theory and guidance. The results achieved can be summarized as follows. (i) Two novel maneuver detectors are developed to diagnose abrupt changes in a class of hybrid systems (detection and isolation of evasive maneuvers): a new implementation of the GLR detector and the novel adaptive- H0 GLR detector. (ii) Two novel state estimators for target tracking are derived using the novel maneuver detectors. The state estimators employ parameterized family of functions to described possible evasive maneuvers. (iii) A novel adaptive Bayesian multiple model predictor of the ballistic miss is developed which employs semi-Markov models and ideas from detection theory. (iv) A novel integrated estimation and guidance scheme that significantly improves the homing accuracy is also presented. The integrated scheme employs banks of estimators and guidance laws, a maneuver detector, and an on-line governor; the scheme is adaptive with respect to the uncertainty affecting the probability density function of the filtered state. (v) A novel discretization technique for the family of continuous-time, game theoretic, bang-bang guidance laws is introduced. The

  6. Twin target self-amplification-based DNA machine for highly sensitive detection of cancer-related gene.

    Xu, Huo; Jiang, Yifan; Liu, Dengyou; Liu, Kai; Zhang, Yafeng; Yu, Suhong; Shen, Zhifa; Wu, Zai-Sheng

    2018-06-29

    The sensitive detection of cancer-related genes is of great significance for early diagnosis and treatment of human cancers, and previous isothermal amplification sensing systems were often based on the reuse of target DNA, the amplification of enzymatic products and the accumulation of reporting probes. However, no reporting probes are able to be transformed into target species and in turn initiate the signal of other probes. Herein we reported a simple, isothermal and highly sensitive homogeneous assay system for tumor suppressor p53 gene detection based on a new autonomous DNA machine, where the signaling probe, molecular beacon (MB), was able to execute the function similar to target DNA besides providing the common signal. In the presence of target p53 gene, the operation of DNA machine can be initiated, and cyclical nucleic acid strand-displacement polymerization (CNDP) and nicking/polymerization cyclical amplification (NPCA) occur, during which the MB was opened by target species and cleaved by restriction endonuclease. In turn, the cleaved fragments could activate the next signaling process as target DNA did. According to the functional similarity, the cleaved fragment was called twin target, and the corresponding fashion to amplify the signal was named twin target self-amplification. Utilizing this newly-proposed DNA machine, the target DNA could be detected down to 0.1 pM with a wide dynamic range (6 orders of magnitude) and single-base mismatched targets were discriminated, indicating a very high assay sensitivity and good specificity. In addition, the DNA machine was not only used to screen the p53 gene in complex biological matrix but also was capable of practically detecting genomic DNA p53 extracted from A549 cell line. This indicates that the proposed DNA machine holds the potential application in biomedical research and early clinical diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. Multi-modal intelligent seizure acquisition (MISA) system--a new approach towards seizure detection based on full body motion measures.

    Conradsen, Isa; Beniczky, Sandor; Wolf, Peter; Terney, Daniella; Sams, Thomas; Sorensen, Helge B D

    2009-01-01

    Many epilepsy patients cannot call for help during a seizure, because they are unconscious or because of the affection of their motor system or speech function. This can lead to injuries, medical complications and at worst death. An alarm system setting off at seizure onset could help to avoid hazards. Today no reliable alarm systems are available. A Multi-modal Intelligent Seizure Acquisition (MISA) system based on full body motion data seems as a good approach towards detection of epileptic seizures. The system is the first to provide a full body description for epilepsy applications. Three test subjects were used for this pilot project. Each subject simulated 15 seizures and in addition performed some predefined normal activities, during a 4-hour monitoring with electromyography (EMG), accelerometer, magnetometer and gyroscope (AMG), electrocardiography (ECG), electroencephalography (EEG) and audio and video recording. The results showed that a non-subject specific MISA system developed on data from the modalities: accelerometer (ACM), gyroscope and EMG is able to detect 98% of the simulated seizures and at the same time mistakes only 4 of the normal movements for seizures. If the system is individualized (subject specific) it is able to detect all simulated seizures with a maximum of 1 false positive. Based on the results from the simulated seizures and normal movements the MISA system seems to be a promising approach to seizure detection.

  8. Intelligent Extruder

    AlperEker; Mark Giammattia; Paul Houpt; Aditya Kumar; Oscar Montero; Minesh Shah; Norberto Silvi; Timothy Cribbs

    2003-04-24

    ''Intelligent Extruder'' described in this report is a software system and associated support services for monitoring and control of compounding extruders to improve material quality, reduce waste and energy use, with minimal addition of new sensors or changes to the factory floor system components. Emphasis is on process improvements to the mixing, melting and de-volatilization of base resins, fillers, pigments, fire retardants and other additives in the :finishing'' stage of high value added engineering polymer materials. While GE Plastics materials were used for experimental studies throughout the program, the concepts and principles are broadly applicable to other manufacturers materials. The project involved a joint collaboration among GE Global Research, GE Industrial Systems and Coperion Werner & Pleiderer, USA, a major manufacturer of compounding equipment. Scope of the program included development of a algorithms for monitoring process material viscosity without rheological sensors or generating waste streams, a novel detection scheme for rapid detection of process upsets and an adaptive feedback control system to compensate for process upsets where at line adjustments are feasible. Software algorithms were implemented and tested on a laboratory scale extruder (50 lb/hr) at GE Global Research and data from a production scale system (2000 lb/hr) at GE Plastics was used to validate the monitoring and detection software. Although not evaluated experimentally, a new concept for extruder process monitoring through estimation of high frequency drive torque without strain gauges is developed and demonstrated in simulation. A plan to commercialize the software system is outlined, but commercialization has not been completed.

  9. Claudin-4-targeted optical imaging detects pancreatic cancer and its precursor lesions.

    Neesse, Albrecht; Hahnenkamp, Anke; Griesmann, Heidi; Buchholz, Malte; Hahn, Stefan A; Maghnouj, Abdelouahid; Fendrich, Volker; Ring, Janine; Sipos, Bence; Tuveson, David A; Bremer, Christoph; Gress, Thomas M; Michl, Patrick

    2013-07-01

    Novel imaging methods based on specific molecular targets to detect both established neoplasms and their precursor lesions are highly desirable in cancer medicine. Previously, we identified claudin-4, an integral constituent of tight junctions, as highly expressed in various gastrointestinal tumours including pancreatic cancer. Here, we investigate the potential of targeting claudin-4 with a naturally occurring ligand to visualise pancreatic cancer and its precursor lesions in vitro and in vivo by near-infrared imaging approaches. A non-toxic C-terminal fragment of the claudin-4 ligand Clostridium perfringens enterotoxin (C-CPE) was labelled with a cyanine dye (Cy5.5). Binding of the optical tracer was analysed on claudin-4 positive and negative cells in vitro, and tumour xenografts in vivo. In addition, two genetically engineered mouse models for pancreatic intraepithelial neoplasia (PanIN) and pancreatic cancer were used for in vivo validation. Optical imaging studies were conducted using 2D planar fluorescence reflectance imaging (FRI) technology and 3D fluorescence-mediated tomography (FMT). In vitro, the peptide-dye conjugate showed high binding affinity to claudin-4 positive CAPAN1 cells, while claudin-4 negative HT1080 cells revealed little or no fluorescence. In vivo, claudin-4 positive tumour xenografts, endogenous pancreatic tumours, hepatic metastases, as well as preinvasive PanIN lesions, were visualised by FRI and FMT up to 48 h after injection showing a significantly higher average of fluorochrome concentration as compared with claudin-4 negative xenografts and normal pancreatic tissue. C-CPE-Cy5.5 combined with novel optical imaging methods enables non-invasive visualisation of claudin-4 positive murine pancreatic tumours and their precursor lesions, representing a promising modality for early diagnostic imaging.

  10. Artificial Intelligence.

    Wash, Darrel Patrick

    1989-01-01

    Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

  11. Intelligent Design

    Hjorth, Poul G.

    2005-01-01

    Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig.......Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig....

  12. Efficacy of Exome-Targeted Capture Sequencing to Detect Mutations in Known Cerebellar Ataxia Genes.

    Coutelier, Marie; Hammer, Monia B; Stevanin, Giovanni; Monin, Marie-Lorraine; Davoine, Claire-Sophie; Mochel, Fanny; Labauge, Pierre; Ewenczyk, Claire; Ding, Jinhui; Gibbs, J Raphael; Hannequin, Didier; Melki, Judith; Toutain, Annick; Laugel, Vincent; Forlani, Sylvie; Charles, Perrine; Broussolle, Emmanuel; Thobois, Stéphane; Afenjar, Alexandra; Anheim, Mathieu; Calvas, Patrick; Castelnovo, Giovanni; de Broucker, Thomas; Vidailhet, Marie; Moulignier, Antoine; Ghnassia, Robert T; Tallaksen, Chantal; Mignot, Cyril; Goizet, Cyril; Le Ber, Isabelle; Ollagnon-Roman, Elisabeth; Pouget, Jean; Brice, Alexis; Singleton, Andrew; Durr, Alexandra

    2018-05-01

    Molecular diagnosis is difficult to achieve in disease groups with a highly heterogeneous genetic background, such as cerebellar ataxia (CA). In many patients, candidate gene sequencing or focused resequencing arrays do not allow investigators to reach a genetic conclusion. To assess the efficacy of exome-targeted capture sequencing to detect mutations in genes broadly linked to CA in a large cohort of undiagnosed patients and to investigate their prevalence. Three hundred nineteen index patients with CA and without a history of dominant transmission were included in the this cohort study by the Spastic Paraplegia and Ataxia Network. Centralized storage was in the DNA and cell bank of the Brain and Spine Institute, Salpetriere Hospital, Paris, France. Patients were classified into 6 clinical groups, with the largest being those with spastic ataxia (ie, CA with pyramidal signs [n = 100]). Sequencing was performed from January 1, 2014, through December 31, 2016. Detected variants were classified as very probably or definitely causative, possibly causative, or of unknown significance based on genetic evidence and genotype-phenotype considerations. Identification of variants in genes broadly linked to CA, classified in pathogenicity groups. The 319 included patients had equal sex distribution (160 female [50.2%] and 159 male patients [49.8%]; mean [SD] age at onset, 27.9 [18.6] years). The age at onset was younger than 25 years for 131 of 298 patients (44.0%) with complete clinical information. Consanguinity was present in 101 of 298 (33.9%). Very probable or definite diagnoses were achieved for 72 patients (22.6%), with an additional 19 (6.0%) harboring possibly pathogenic variants. The most frequently mutated genes were SPG7 (n = 14), SACS (n = 8), SETX (n = 7), SYNE1 (n = 6), and CACNA1A (n = 6). The highest diagnostic rate was obtained for patients with an autosomal recessive CA with oculomotor apraxia-like phenotype (6 of 17 [35.3%]) or

  13. Targeted Screening With Combined Age- and Morphology-Based Criteria Enriches Detection of Lynch Syndrome in Endometrial Cancer.

    Lin, Douglas I; Hecht, Jonathan L

    2016-06-01

    Endometrial cancer is associated with Lynch syndrome in 2% to 6% of cases. Adequate screening may prevent of a second cancer and incident cancers in family members via risk-reducing strategies. The goal of the study was to evaluate the detection rate of Lynch syndrome via a targeted screening approach. In 2009, we incorporated targeted Lynch syndrome screening via immunohistochemistry for MLH1, PMS2, MSH2, and MSH6, followed by MLH1 promoter hypermethylation, in select cases of endometrial carcinoma. Criteria for patient selection included (1) all patients Lynch syndrome. Therefore, targeted screening with combined age and morphology based criteria enriches detection of Lynch syndrome in endometrial cancer. However, the detection rate is lower than the rates from published series that offer universal screening. © The Author(s) 2016.

  14. Integration of bio-inspired, control-based visual and olfactory data for the detection of an elusive target

    Duong, Tuan A.; Duong, Nghi; Le, Duong

    2017-01-01

    In this paper, we present an integration technique using a bio-inspired, control-based visual and olfactory receptor system to search for elusive targets in practical environments where the targets cannot be seen obviously by either sensory data. Bio-inspired Visual System is based on a modeling of extended visual pathway which consists of saccadic eye movements and visual pathway (vertebrate retina, lateral geniculate nucleus and visual cortex) to enable powerful target detections of noisy, partial, incomplete visual data. Olfactory receptor algorithm, namely spatial invariant independent component analysis, that was developed based on data of old factory receptor-electronic nose (enose) of Caltech, is adopted to enable the odorant target detection in an unknown environment. The integration of two systems is a vital approach and sets up a cornerstone for effective and low-cost of miniaturized UAVs or fly robots for future DOD and NASA missions, as well as for security systems in Internet of Things environments.

  15. A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

    Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan

    2018-03-01

    False alarm rate and detection rate are still two contradictory metrics for infrared small target detection in an infrared search and track system (IRST), despite the development of new detection algorithms. In certain circumstances, not detecting true targets is more tolerable than detecting false items as true targets. Hence, considering background clutter and detector noise as the sources of the false alarm in an IRST system, in this paper, a false alarm aware methodology is presented to reduce false alarm rate while the detection rate remains undegraded. To this end, advantages and disadvantages of each detection algorithm are investigated and the sources of the false alarms are determined. Two target detection algorithms having independent false alarm sources are chosen in a way that the disadvantages of the one algorithm can be compensated by the advantages of the other one. In this work, multi-scale average absolute gray difference (AAGD) and Laplacian of point spread function (LoPSF) are utilized as the cornerstones of the desired algorithm of the proposed methodology. After presenting a conceptual model for the desired algorithm, it is implemented through the most straightforward mechanism. The desired algorithm effectively suppresses background clutter and eliminates detector noise. Also, since the input images are processed through just four different scales, the desired algorithm has good capability for real-time implementation. Simulation results in term of signal to clutter ratio and background suppression factor on real and simulated images prove the effectiveness and the performance of the proposed methodology. Since the desired algorithm was developed based on independent false alarm sources, our proposed methodology is expandable to any pair of detection algorithms which have different false alarm sources.

  16. Masking of Time-Frequency Patterns in Applications of Passive Underwater Target Detection

    Jüri Sildam

    2010-01-01

    Full Text Available Spectrogram analysis of acoustical sounds for underwater target classification is utilized when loud nonstationary interference sources overlap with a signal of interest in time but can be separated in time-frequency (TF domain. We propose a signal masking method which in a TF plane combines local statistical and morphological features of the signal of interest. A dissimilarity measure D of adjacent TF cells is used for local estimation of entropy H, followed by estimation of ΔH=Htc−Hfc entropy difference, where Hfc is calculated along the time axis at a mean frequency fc and Htc is calculated along the frequency axis at a mean time tc of the TF window, respectively. Due to a limited number of points used in ΔH estimation, the number of possible ΔH values, which define a primary mask, is also limited. A secondary mask is defined using morphological operators applied to, for example, H and ΔH. We demonstrate how primary and secondary masks can be used for signal detection and discrimination, respectively. We also show that the proposed approach can be generalized within the framework of Genetic Programming.

  17. Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera

    Jiatong Bao

    2016-12-01

    Full Text Available Controlling robots by natural language (NL is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications.

  18. Detection of low-energy antinuclei in space using an active-target particle detector

    Poeschl, Thomas; Greenwald, Daniel; Konorov, Igor; Paul, Stephan [Physics Department E18, Technische Universitaet Muenchen (Germany); Losekamm, Martin [Physics Department E18, Technische Universitaet Muenchen (Germany); Institute of Astronautics, Technische Universitaet Muenchen (Germany)

    2015-07-01

    Measuring antimatter in space excellently probes various astrophysical processes. The abundances and energy spectra of antiparticles reveal a lot about the creation and propagation of cosmic-ray particles in the universe. Abnormalities in their spectra can reveal exotic sources or inaccuracies in our understanding of the involved processes. The measurement of antiprotons and the search for antideuterons and antihelium are optimal at low kinetic energies since background from high-energy cosmic-ray collisions is low. For this reason, we are developing an active-target particle detector capable of detecting ions and anti-ions in the energy range of 30-100 MeV per nucleon. The detector consists of 900 scintillating fibers coupled to silicon photomultipliers and is designed to operate on nanosatellites. The primary application of the detector will be the Antiproton Flux in Space (AFIS) mission, whose goal is the measurement of geomagnetically trapped antiprotons inside Earth's inner radiation belt. In this talk, we explain our particle identification technique and present results from first in-beam measurements with a prototype.

  19. Research on the development of space target detecting system and three-dimensional reconstruction technology

    Li, Dong; Wei, Zhen; Song, Dawei; Sun, Wenfeng; Fan, Xiaoyan

    2016-11-01

    With the development of space technology, the number of spacecrafts and debris are increasing year by year. The demand for detecting and identification of spacecraft is growing strongly, which provides support to the cataloguing, crash warning and protection of aerospace vehicles. The majority of existing approaches for three-dimensional reconstruction is scattering centres correlation, which is based on the radar high resolution range profile (HRRP). This paper proposes a novel method to reconstruct the threedimensional scattering centre structure of target from a sequence of radar ISAR images, which mainly consists of three steps. First is the azimuth scaling of consecutive ISAR images based on fractional Fourier transform (FrFT). The later is the extraction of scattering centres and matching between adjacent ISAR images using grid method. Finally, according to the coordinate matrix of scattering centres, the three-dimensional scattering centre structure is reconstructed using improved factorization method. The three-dimensional structure is featured with stable and intuitive characteristic, which provides a new way to improve the identification probability and reduce the complexity of the model matching library. A satellite model is reconstructed using the proposed method from four consecutive ISAR images. The simulation results prove that the method has gotten a satisfied consistency and accuracy.

  20. Target-Specific Assay for Rapid and Quantitative Detection of Mycobacterium chimaera DNA.

    Zozaya-Valdés, Enrique; Porter, Jessica L; Coventry, John; Fyfe, Janet A M; Carter, Glen P; Gonçalves da Silva, Anders; Schultz, Mark B; Seemann, Torsten; Johnson, Paul D R; Stewardson, Andrew J; Bastian, Ivan; Roberts, Sally A; Howden, Benjamin P; Williamson, Deborah A; Stinear, Timothy P

    2017-06-01

    Mycobacterium chimaera is an opportunistic environmental mycobacterium belonging to the Mycobacterium avium - M. intracellulare complex. Although most commonly associated with pulmonary disease, there has been growing awareness of invasive M. chimaera infections following cardiac surgery. Investigations suggest worldwide spread of a specific M. chimaera clone, associated with contaminated hospital heater-cooler units used during the surgery. Given the global dissemination of this clone, its potential to cause invasive disease, and the laboriousness of current culture-based diagnostic methods, there is a pressing need to develop rapid and accurate diagnostic assays specific for M. chimaera Here, we assessed 354 mycobacterial genome sequences and confirmed that M. chimaera is a phylogenetically coherent group. In silico comparisons indicated six DNA regions present only in M. chimaera We targeted one of these regions and developed a TaqMan quantitative PCR (qPCR) assay for M. chimaera with a detection limit of 100 CFU/ml in whole blood spiked with bacteria. In vitro screening against DNA extracted from 40 other mycobacterial species and 22 bacterial species from 21 diverse genera confirmed the in silico -predicted specificity for M. chimaera Screening 33 water samples from heater-cooler units with this assay highlighted the increased sensitivity of PCR compared to culture, with 15 of 23 culture-negative samples positive by M. chimaera qPCR. We have thus developed a robust molecular assay that can be readily and rapidly deployed to screen clinical and environmental specimens for M. chimaera . Copyright © 2017 American Society for Microbiology.

  1. Developing and evaluating a target-background similarity metric for camouflage detection.

    Chiuhsiang Joe Lin

    Full Text Available BACKGROUND: Measurement of camouflage performance is of fundamental importance for military stealth applications. The goal of camouflage assessment algorithms is to automatically assess the effect of camouflage in agreement with human detection responses. In a previous study, we found that the Universal Image Quality Index (UIQI correlated well with the psychophysical measures, and it could be a potentially camouflage assessment tool. METHODOLOGY: In this study, we want to quantify the camouflage similarity index and psychophysical results. We compare several image quality indexes for computational evaluation of camouflage effectiveness, and present the results of an extensive human visual experiment conducted to evaluate the performance of several camouflage assessment algorithms and analyze the strengths and weaknesses of these algorithms. SIGNIFICANCE: The experimental data demonstrates the effectiveness of the approach, and the correlation coefficient result of the UIQI was higher than those of other methods. This approach was highly correlated with the human target-searching results. It also showed that this method is an objective and effective camouflage performance evaluation method because it considers the human visual system and image structure, which makes it consistent with the subjective evaluation results.

  2. Developing and evaluating a target-background similarity metric for camouflage detection.

    Lin, Chiuhsiang Joe; Chang, Chi-Chan; Liu, Bor-Shong

    2014-01-01

    Measurement of camouflage performance is of fundamental importance for military stealth applications. The goal of camouflage assessment algorithms is to automatically assess the effect of camouflage in agreement with human detection responses. In a previous study, we found that the Universal Image Quality Index (UIQI) correlated well with the psychophysical measures, and it could be a potentially camouflage assessment tool. In this study, we want to quantify the camouflage similarity index and psychophysical results. We compare several image quality indexes for computational evaluation of camouflage effectiveness, and present the results of an extensive human visual experiment conducted to evaluate the performance of several camouflage assessment algorithms and analyze the strengths and weaknesses of these algorithms. The experimental data demonstrates the effectiveness of the approach, and the correlation coefficient result of the UIQI was higher than those of other methods. This approach was highly correlated with the human target-searching results. It also showed that this method is an objective and effective camouflage performance evaluation method because it considers the human visual system and image structure, which makes it consistent with the subjective evaluation results.

  3. Human synthetic lethal inference as potential anti-cancer target gene detection

    Solé Ricard V

    2009-12-01

    Full Text Available Abstract Background Two genes are called synthetic lethal (SL if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. Results In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases as well as on existent approved drugs (DrugBank database supports our selection of cancer-therapy candidates. Conclusions Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

  4. Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

    Yang Zhang

    2016-10-01

    Full Text Available Ultra-wideband (UWB radar has been widely used for detecting human physiological signals (respiration, movement, etc. in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc., the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

  5. Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

    Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

    2016-10-27

    Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

  6. Intelligent video surveillance systems and technology

    Ma, Yunqian

    2009-01-01

    From the streets of London to subway stations in New York City, hundreds of thousands of surveillance cameras ubiquitously collect hundreds of thousands of videos, often running 24/7. How can such vast volumes of video data be stored, analyzed, indexed, and searched? How can advanced video analysis and systems autonomously recognize people and detect targeted activities real-time? Collating and presenting the latest information Intelligent Video Surveillance: Systems and Technology explores these issues, from fundamentals principle to algorithmic design and system implementation.An Integrated

  7. High affinity γPNA sandwich hybridization assay for rapid detection of short nucleic acid targets with single mismatch discrimination.

    Goldman, Johnathan M; Zhang, Li Ang; Manna, Arunava; Armitage, Bruce A; Ly, Danith H; Schneider, James W

    2013-07-08

    Hybridization analysis of short DNA and RNA targets presents many challenges for detection. The commonly employed sandwich hybridization approach cannot be implemented for these short targets due to insufficient probe-target binding strengths for unmodified DNA probes. Here, we present a method capable of rapid and stable sandwich hybridization detection for 22 nucleotide DNA and RNA targets. Stable hybridization is achieved using an n-alkylated, polyethylene glycol γ-carbon modified peptide nucleic acid (γPNA) amphiphile. The γPNA's exceptionally high affinity enables stable hybridization of a second DNA-based probe to the remaining bases of the short target. Upon hybridization of both probes, an electrophoretic mobility shift is measured via interaction of the n-alkane modification on the γPNA with capillary electrophoresis running buffer containing nonionic surfactant micelles. We find that sandwich hybridization of both probes is stable under multiple binding configurations and demonstrate single base mismatch discrimination. The binding strength of both probes is also stabilized via coaxial stacking on adjacent hybridization to targets. We conclude with a discussion on the implementation of the proposed sandwich hybridization assay as a high-throughput microRNA detection method.

  8. Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent.

    Canuto, Holly C; McLachlan, Charles; Kettunen, Mikko I; Velic, Marko; Krishnan, Anant S; Neves, Andre' A; de Backer, Maaike; Hu, D-E; Hobson, Michael P; Brindle, Kevin M

    2009-05-01

    A targeted Gd(3+)-based contrast agent has been developed that detects tumor cell death by binding to the phosphatidylserine (PS) exposed on the plasma membrane of dying cells. Although this agent has been used to detect tumor cell death in vivo, the differences in signal intensity between treated and untreated tumors was relatively small. As cell death is often spatially heterogeneous within tumors, we investigated whether an image analysis technique that parameterizes heterogeneity could be used to increase the sensitivity of detection of this targeted contrast agent. Two-dimensional (2D) Minkowski functionals (MFs) provided an automated and reliable method for parameterization of image heterogeneity, which does not require prior assumptions about the number of regions or features in the image, and were shown to increase the sensitivity of detection of the contrast agent as compared to simple signal intensity analysis. (c) 2009 Wiley-Liss, Inc.

  9. Dynamic surface-enhanced Raman spectroscopy and Chemometric methods for fast detection and intelligent identification of methamphetamine and 3, 4-Methylenedioxy methamphetamine in human urine

    Weng, Shizhuang; Dong, Ronglu; Zhu, Zede; Zhang, Dongyan; Zhao, Jinling; Huang, Linsheng; Liang, Dong

    2018-01-01

    Conventional Surface-Enhanced Raman Spectroscopy (SERS) for fast detection of drugs in urine on the portable Raman spectrometer remains challenges because of low sensitivity and unreliable Raman signal, and spectra process with manual intervention. Here, we develop a novel detection method of drugs in urine using chemometric methods and dynamic SERS (D-SERS) with mPEG-SH coated gold nanorods (GNRs). D-SERS combined with the uniform GNRs can obtain giant enhancement, and the signal is also of high reproducibility. On the basis of the above advantages, we obtained the spectra of urine, urine with methamphetamine (MAMP), urine with 3, 4-Methylenedioxy Methamphetamine (MDMA) using D-SERS. Simultaneously, some chemometric methods were introduced for the intelligent and automatic analysis of spectra. Firstly, the spectra at the critical state were selected through using K-means. Then, the spectra were proposed by random forest (RF) with feature selection and principal component analysis (PCA) to develop the recognition model. And the identification accuracy of model were 100%, 98.7% and 96.7%, respectively. To validate the effect in practical issue further, the drug abusers'urine samples with 0.4, 3, 30 ppm MAMP were detected using D-SERS and identified by the classification model. The high recognition accuracy of > 92.0% can meet the demand of practical application. Additionally, the parameter optimization of RF classification model was simple. Compared with the general laboratory method, the detection process of urine's spectra using D-SERS only need 2 mins and 2 μL samples volume, and the identification of spectra based on chemometric methods can be finish in seconds. It is verified that the proposed approach can provide the accurate, convenient and rapid detection of drugs in urine.

  10. Targeted Metabolomics Approach To Detect the Misuse of Steroidal Aromatase Inhibitors in Equine Sports by Biomarker Profiling.

    Chan, George Ho Man; Ho, Emmie Ngai Man; Leung, David Kwan Kon; Wong, Kin Sing; Wan, Terence See Ming

    2016-01-05

    The use of anabolic androgenic steroids (AAS) is prohibited in both human and equine sports. The conventional approach in doping control testing for AAS (as well as other prohibited substances) is accomplished by the direct detection of target AAS or their characteristic metabolites in biological samples using hyphenated techniques such as gas chromatography or liquid chromatography coupled with mass spectrometry. Such an approach, however, falls short when dealing with unknown designer steroids where reference materials and their pharmacokinetics are not available. In addition, AASs with fast elimination times render the direct detection approach ineffective as the detection window is short. A targeted metabolomics approach is a plausible alternative to the conventional direct detection approach for controlling the misuse of AAS in sports. Because the administration of AAS of the same class may trigger similar physiological responses or effects in the body, it may be possible to detect such administrations by monitoring changes in the endogenous steroidal expression profile. This study attempts to evaluate the viability of using the targeted metabolomics approach to detect the administration of steroidal aromatase inhibitors, namely androst-4-ene-3,6,17-trione (6-OXO) and androsta-1,4,6-triene-3,17-dione (ATD), in horses. Total (free and conjugated) urinary concentrations of 31 endogenous steroids were determined by gas chromatography-tandem mass spectrometry for a group of 2 resting and 2 in-training thoroughbred geldings treated with either 6-OXO or ATD. Similar data were also obtained from a control (untreated) group of in-training thoroughbred geldings (n = 28). Statistical processing and chemometric procedures using principle component analysis and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) have highlighted 7 potential biomarkers that could be used to differentiate urine samples obtained from the control and the treated groups

  11. Multiple advanced logic gates made of DNA-Ag nanocluster and the application for intelligent detection of pathogenic bacterial genes.

    Lin, Xiaodong; Liu, Yaqing; Deng, Jiankang; Lyu, Yanlong; Qian, Pengcheng; Li, Yunfei; Wang, Shuo

    2018-02-21

    The integration of multiple DNA logic gates on a universal platform to implement advance logic functions is a critical challenge for DNA computing. Herein, a straightforward and powerful strategy in which a guanine-rich DNA sequence lighting up a silver nanocluster and fluorophore was developed to construct a library of logic gates on a simple DNA-templated silver nanoclusters (DNA-AgNCs) platform. This library included basic logic gates, YES, AND, OR, INHIBIT, and XOR, which were further integrated into complex logic circuits to implement diverse advanced arithmetic/non-arithmetic functions including half-adder, half-subtractor, multiplexer, and demultiplexer. Under UV irradiation, all the logic functions could be instantly visualized, confirming an excellent repeatability. The logic operations were entirely based on DNA hybridization in an enzyme-free and label-free condition, avoiding waste accumulation and reducing cost consumption. Interestingly, a DNA-AgNCs-based multiplexer was, for the first time, used as an intelligent biosensor to identify pathogenic genes, E. coli and S. aureus genes, with a high sensitivity. The investigation provides a prototype for the wireless integration of multiple devices on even the simplest single-strand DNA platform to perform diverse complex functions in a straightforward and cost-effective way.

  12. Extended-Search, Bézier Curve-Based Lane Detection and Reconstruction System for an Intelligent Vehicle

    Xiaoyun Huang

    2015-09-01

    Full Text Available To improve the real-time performance and detection rate of a Lane Detection and Reconstruction (LDR system, an extended-search-based lane detection method and a Bézier curve-based lane reconstruction algorithm are proposed in this paper. The extended-search-based lane detection method is designed to search boundary blocks from the initial position, in an upwards direction and along the lane, with small search areas including continuous search, discontinuous search and bending search in order to detect different lane boundaries. The Bézier curve-based lane reconstruction algorithm is employed to describe a wide range of lane boundary forms with comparatively simple expressions. In addition, two Bézier curves are adopted to reconstruct the lanes' outer boundaries with large curvature variation. The lane detection and reconstruction algorithm — including initial-blocks' determining, extended search, binarization processing and lane boundaries' fitting in different scenarios — is verified in road tests. The results show that this algorithm is robust against different shadows and illumination variations; the average processing time per frame is 13 ms. Significantly, it presents an 88.6% high-detection rate on curved lanes with large or variable curvatures, where the accident rate is higher than that of straight lanes.

  13. Universal, colorimetric microRNA detection strategy based on target-catalyzed toehold-mediated strand displacement reaction

    Park, Yeonkyung; Lee, Chang Yeol; Kang, Shinyoung; Kim, Hansol; Park, Ki Soo; Park, Hyun Gyu

    2018-02-01

    In this work, we developed a novel, label-free, and enzyme-free strategy for the colorimetric detection of microRNA (miRNA), which relies on a target-catalyzed toehold-mediated strand displacement (TMSD) reaction. The system employs a detection probe that specifically binds to the target miRNA and sequentially releases a catalyst strand (CS) intended to trigger the subsequent TMSD reaction. Thus, the presence of target miRNA releases the CS that mediates the formation of an active G-quadruplex DNAzyme which is initially caged and inactivated by a blocker strand. In addition, a fuel strand that is supplemented for the recycling of the CS promotes another TMSD reaction, consequently generating a large number of active G-quadruplex DNAzymes. As a result, a distinct colorimetric signal is produced by the ABTS oxidation promoted by the peroxidase mimicking activity of the released G-quadruplex DNAzymes. Based on this novel strategy, we successfully detected miR-141, a promising biomarker for human prostate cancer, with high selectivity. The diagnostic capability of this system was also demonstrated by reliably determining target miR-141 in human serum, showing its great potential towards real clinical applications. Importantly, the proposed approach is composed of separate target recognition and signal transduction modules. Thus, it could be extended to analyze different target miRNAs by simply redesigning the detection probe while keeping the same signal transduction module as a universal signal amplification unit, which was successfully demonstrated by analyzing another target miRNA, let-7d.

  14. Relationship between Emotional Intelligence and

    Vahid Rafieyan

    2014-07-01

    Full Text Available Language learners’ awareness of target language pragmatic features is influenced by individual difference variables, the least explored one being emotional intelligence. To investigate the relationship between emotional intelligence and pragmatic awareness, the current study was conducted over 120 Iranian senior undergraduates of English as a Foreign Language at a university in Iran. Pragmatic awareness was measured through a 12-scenario contextualized pragmatic judgment task. Emotional intelligence was also measured through the EQ-i. The results of the Pearson correlation revealed a strong positive relationship between emotional intelligence and pragmatic awareness. The pedagogical implications of the findings suggested incorporation of emotion-driven authentic materials in English language classes to invoke emotional intelligence in language learners.

  15. Intelligent playgrounds

    Larsen, Lasse Juel

    2009-01-01

    This paper examines play, gaming and learning in regard to intelligent playware developed for outdoor use. The key questions are how does these novel artefacts influence the concept of play, gaming and learning. Up until now play and game have been understood as different activities. This paper...... examines if the sharp differentiation between the two can be uphold in regard to intelligent playware for outdoor use. Play and game activities will be analysed and viewed in conjunction with learning contexts. This paper will stipulate that intelligent playware facilitates rapid shifts in contexts...

  16. Artificial intelligence

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  17. Artificial Intelligence

    Warwick, Kevin

    2011-01-01

    if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory

  18. HaloPlex Targeted Resequencing for Mutation Detection in Clinical Formalin-Fixed, Paraffin-Embedded Tumor Samples.

    Moens, Lotte N J; Falk-Sörqvist, Elin; Ljungström, Viktor; Mattsson, Johanna; Sundström, Magnus; La Fleur, Linnéa; Mathot, Lucy; Micke, Patrick; Nilsson, Mats; Botling, Johan

    2015-11-01

    In recent years, the advent of massively parallel next-generation sequencing technologies has enabled substantial advances in the study of human diseases. Combined with targeted DNA enrichment methods, high sequence coverage can be obtained for different genes simultaneously at a reduced cost per sample, creating unique opportunities for clinical cancer diagnostics. However, the formalin-fixed, paraffin-embedded (FFPE) process of tissue samples, routinely used in pathology departments, results in DNA fragmentation and nucleotide modifications that introduce a number of technical challenges for downstream biomolecular analyses. We evaluated the HaloPlex target enrichment system for somatic mutation detection in 80 tissue fractions derived from 20 clinical cancer cases with paired tumor and normal tissue available in both FFPE and fresh-frozen format. Several modifications to the standard method were introduced, including a reduced target fragment length and two strand capturing. We found that FFPE material can be used for HaloPlex-based target enrichment and next-generation sequencing, even when starting from small amounts of DNA. By specifically capturing both strands for each target fragment, we were able to reduce the number of false-positive errors caused by FFPE-induced artifacts and lower the detection limit for somatic mutations. We believe that the HaloPlex method presented here will be broadly applicable as a tool for somatic mutation detection in clinical cancer settings. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

  19. Impact of tDCS on Performance and Learning of Target Detection: Interaction with Stimulus Characteristics and Experimental Design

    Coffman, B. A.; Trumbo, M. C.; Flores, R. A.; Garcia, C. M.; van der Merwe, A. J.; Wassermann, E. M.; Weisend, M. P.; Clark, V. P.

    2012-01-01

    We have previously found that transcranial direct current stimulation (tDCS) over right inferior frontal cortex (RIFC) enhances performance during learning of a difficult visual target detection task (Clark et al., 2012). In order to examine the cognitive mechanisms of tDCS that lead to enhanced performance, here we analyzed its differential…

  20. Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

    Millikan, Brian; Dutta, Aritra; Sun, Qiyu; Foroosh, Hassan

    2017-01-01

    Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.

  1. Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

    Millikan, Brian

    2017-05-02

    Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.

  2. DESIGN OF AN INTELLIGENT SYSTEM TO DETECT TYPE OF PAIN USING ARTIFICIAL NEURAL NETWORK FOR PATIENTS WITH SPINAL CORD INJURY IN SHEFA NEUROSCIENCE RESEARCH CENTER

    Nasrolah Nasr HeidarAbadi, Reza Safdari, Peirhossein Kolivand, Amir Javadi, Azimeh Danesh Shahraki1, Marjan Ghazi Saeidi*

    2017-01-01

    Using artificial intelligence in computerized clinical systems helps physicians diagnose disease or choose treatment. Intelligent methods are constantly changed to be more effective and accurate for quick medical diagnosis. Neural networks are a powerful tool to help physicians. The tools can process a high number of data and minimize errors in ignoring patients' information. Intelligent system design based on artificial neural network was performed in 3 phases. Phase1: Designing the data rec...

  3. Intelligent Advertising

    Díaz Pinedo, Edilfredo Eliot

    2012-01-01

    Intelligent Advertisement diseña e implementa un sistema de publicidad para dispositivos móviles en un centro comercial, donde los clientes reciben publicidad de forma pasiva en sus dispositivos mientras están dentro.

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

    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.

  5. Non-target activity detection by post-radioembolization yttrium-90 PET/CT: Image assessment technique and case examples

    Yung Hsiang eKao

    2014-02-01

    Full Text Available High-resolution yttrium-90 (90Y imaging of post-radioembolization microsphere biodistribution may be achieved by conventional positron emission tomography with integrated computed tomography (PET/CT scanners that have time-of-flight capability. However, reconstructed 90Y PET/CT images have high background noise, making non-target activity detection technically challenging. This educational article describes our image assessment technique for non-target activity detection by 90Y PET/CT which qualitatively overcomes the problem of background noise. We present selected case examples of non-target activity in untargeted liver, stomach, gallbladder, chest wall and kidney, supported by angiography and 90Y bremsstrahlung single photon emission computed tomography with integrated computed tomography (SPECT/CT or technetium-99m macroaggregated albumin SPECT/CT.

  6. Cell Density Affects the Detection of Chk1 Target Engagement by the Selective Inhibitor V158411.

    Geneste, Clara C; Massey, Andrew J

    2018-02-01

    Understanding drug target engagement and the relationship to downstream pharmacology is critical for drug discovery. Here we have evaluated target engagement of Chk1 by the small-molecule inhibitor V158411 using two different target engagement methods (autophosphorylation and cellular thermal shift assay [CETSA]). Target engagement measured by these methods was subsequently related to Chk1 inhibitor-dependent pharmacology. Inhibition of autophosphorylation was a robust method for measuring V158411 Chk1 target engagement. In comparison, while target engagement determined using CETSA appeared robust, the V158411 CETSA target engagement EC 50 values were 43- and 19-fold greater than the autophosphorylation IC 50 values. This difference was attributed to the higher cell density in the CETSA assay configuration. pChk1 (S296) IC 50 values determined using the CETSA assay conditions were 54- and 33-fold greater than those determined under standard conditions and were equivalent to the CETSA EC 50 values. Cellular conditions, especially cell density, influenced the target engagement of V158411 for Chk1. The effects of high cell density on apparent compound target engagement potency should be evaluated when using target engagement assays that necessitate high cell densities (such as the CETSA conditions used in this study). In such cases, the subsequent relation of these data to downstream pharmacological changes should therefore be interpreted with care.

  7. Performance Evaluation of Target Detection with a Near-Space Vehicle-Borne Radar in Blackout Condition.

    Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Deng, Bin; Qin, Yuliang

    2016-01-06

    Radar is a very important sensor in surveillance applications. Near-space vehicle-borne radar (NSVBR) is a novel installation of a radar system, which offers many benefits, like being highly suited to the remote sensing of extremely large areas, having a rapidly deployable capability and having low vulnerability to electronic countermeasures. Unfortunately, a target detection challenge arises because of complicated scenarios, such as nuclear blackout, rain attenuation, etc. In these cases, extra care is needed to evaluate the detection performance in blackout situations, since this a classical problem along with the application of an NSVBR. However, the existing evaluation measures are the probability of detection and the receiver operating curve (ROC), which cannot offer detailed information in such a complicated application. This work focuses on such requirements. We first investigate the effect of blackout on an electromagnetic wave. Performance evaluation indexes are then built: three evaluation indexes on the detection capability and two evaluation indexes on the robustness of the detection process. Simulation results show that the proposed measure will offer information on the detailed performance of detection. These measures are therefore very useful in detecting the target of interest in a remote sensing system and are helpful for both the NSVBR designers and users.

  8. BUSINESS INTELLIGENCE

    Bogdan Mohor Dumitrita

    2011-01-01

    The purpose of this work is to present business intelligence systems. These systems can be extremely complex and important in modern market competition. Its effectiveness also reflects in price, so we have to exlore their financial potential before investment. The systems have 20 years long history and during that time many of such tools have been developed, but they are rarely still in use. Business intelligence system consists of three main areas: Data Warehouse, ETL tools and tools f...

  9. Intelligent indexing

    Farkas, J.

    1992-01-01

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space ι 2 to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs

  10. Intelligent indexing

    Farkas, J

    1993-12-31

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space {iota}{sup 2} to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs.

  11. Combined effects of expectations and visual uncertainty upon detection and identification of a target in the fog.

    Quétard, Boris; Quinton, Jean-Charles; Colomb, Michèle; Pezzulo, Giovanni; Barca, Laura; Izaute, Marie; Appadoo, Owen Kevin; Mermillod, Martial

    2015-09-01

    Detecting a pedestrian while driving in the fog is one situation where the prior expectation about the target presence is integrated with the noisy visual input. We focus on how these sources of information influence the oculomotor behavior and are integrated within an underlying decision-making process. The participants had to judge whether high-/low-density fog scenes displayed on a computer screen contained a pedestrian or a deer by executing a mouse movement toward the response button (mouse-tracking). A variable road sign was added on the scene to manipulate expectations about target identity. We then analyzed the timing and amplitude of the deviation of mouse trajectories toward the incorrect response and, using an eye tracker, the detection time (before fixating the target) and the identification time (fixations on the target). Results revealed that expectation of the correct target results in earlier decisions with less deviation toward the alternative response, this effect being partially explained by the facilitation of target identification.

  12. Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

    Jianhui Zhao

    2018-01-01

    Full Text Available In order to improve the visual effect of the around view monitor (AVM, we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

  13. Development of a qualitative real-time PCR method to detect 19 targets for identification of genetically modified organisms.

    Peng, Cheng; Wang, Pengfei; Xu, Xiaoli; Wang, Xiaofu; Wei, Wei; Chen, Xiaoyun; Xu, Junfeng

    2016-01-01

    As the amount of commercially available genetically modified organisms (GMOs) grows recent years, the diversity of target sequences for molecular detection techniques are eagerly needed. Considered as the gold standard for GMO analysis, the real-time PCR technology was optimized to produce a high-throughput GMO screening method. With this method we can detect 19 transgenic targets. The specificity of the assays was demonstrated to be 100 % by the specific amplification of DNA derived from reference material from 20 genetically modified crops and 4 non modified crops. Furthermore, most assays showed a very sensitive detection, reaching the limit of ten copies. The 19 assays are the most frequently used genetic elements present in GM crops and theoretically enable the screening of the known GMO described in Chinese markets. Easy to use, fast and cost efficient, this method approach fits the purpose of GMO testing laboratories.

  14. Appearing smart: the impression management of intelligence, person perception accuracy, and behavior in social interaction.

    Murphy, Nora A

    2007-03-01

    Intelligence is an important trait that affects everyday social interaction. The present research utilized the ecological perspective of social perception to investigate the impression management of intelligence and strangers' evaluations of targets' intelligence levels. The ability to effectively portray an impression of intelligence to outside judges as well as interaction partners was appraised and the effect of impression management on the accurate judgment of intelligence was assessed. In addition, targets' behavior was studied in relation to impression management, perceived intelligence, and actual measured intelligence. Impression-managing targets appeared more intelligent to video judges but not to their interaction partner as compared to controls. The intelligence quotient (IQ) of impression-managing targets was more accurately judged than controls' IQ. Impression-managing targets displayed distinct nonverbal behavioral patterns that differed from controls. Looking while speaking was a key behavior: It significantly correlated with IQ, was successfully manipulated by impression-managing targets, and contributed to higher perceived intelligence ratings.

  15. A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

    S. Sharifi hashjin

    2016-06-01

    Full Text Available In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

  16. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

    Best, Myron G.; Sol, Nik; In ‘t Veld, Sjors G.J.G.; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna Larissa N.; Fejes, Aniko V.; Tjon Kon Fat, Lee Ann; Huis in 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y.; Meijer, Laura L.; Kooi, Irsan E.; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E.; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D.; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C.; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C.; Urbanus, Rolf T.; Hoefer, Imo E.; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P.; Vandertop, W. Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R. Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B.; Tannous, Bakhos A.; de Langen, Adrianus J.; Smit, Egbert F.; van den Heuvel, Michel M; Wurdinger, Thomas

    2017-01-01

    Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from

  17. Intelligent leak detection system for oil pipelines; Sistema inteligente para deteccao de vazamentos em dutos de petroleo

    Freitas, Ricardo Dantas Gadelha de [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil)

    2003-07-01

    One of the most challenging tasks in an oil field is implementation of a software-based leak detection system on a multi-phase flow pipeline. This paper will discuss implementation of a leak detection system in a particular oil field using state-of-the-art signal processing techniques to apply to the data collected in a oil pipeline. This leak detection system is still in development and uses a more practical approach to the problem than traditional methods and was implemented on a PC under the Windows operating system. Windowing, joint time-frequency analysis and wavelets were considered to develop methods of detecting leaks by watching for the wavefront. The idea behind these techniques is to cut the signal of interest into several parts and then analyze the parts separately. It is impossible to know the exact frequency and the exact time of occurrence of the leak frequency in a signal. In other words, a leak signal can simply not be represented as a point in the time-frequency space. It is very important how one cuts the signal to implement the analysis. The wavelet transform or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier transform. So, this paper shows some tests and how these techniques are being implementing during the development of the system. (author)

  18. Performance and blood monitoring in sports: the artificial intelligence evoking target testing in antidoping (AR.I.E.T.T.A.) project.

    Manfredini, A F; Malagoni, A M; Litmanen, H; Zhukovskaja, L; Jeannier, P; Dal Follo, D; Felisatti, M; Besseberg, A; Geistlinger, M; Bayer, P; Carrabre, J E

    2011-03-01

    Substances and methods used to increase oxygen blood transport and physical performance can be detected in the blood, but the screening of the athletes to be tested remains a critical issue for the International Federations. This project, AR.I.E.T.T.A., aimed to develop a software capable of analysing athletes' hematological and performance profiles to detect abnormal patterns. One-hundred eighty athletes belonging to the International Biathlon Union gave written informed consent to have their hematological data, previously collected according to anti-doping rules, used to develop the AR.I.E.T.T.A. software. Software was developed with the included sections: 1) log-in; 2) data-entry: where data are loaded, stored and grouped; 3) analysis: where data are analysed, validated scores are calculated, and parameters are simultaneously displayed as statistics, tables and graphs, and individual or subpopulation profiles; 4) screening: where an immediate evaluation of the risk score of the present sample and/or the athlete under study is obtained. The sample risk score or AR.I.E.T.T.A. score is calculated by a simple computational system combining different parameters (absolute values and intra-individual variations) considered concurrently. The AR.I.E.T.T.A. score is obtained by the sum of the deviation units derived from each parameter, considering the shift of the present value from the reference values, based on the number of standard deviations. AR.I.E.T.T.A. enables a quick evaluation of blood results assisting surveillance programs and perform timely target testing controls on athletes by the International Federations. Future studies aiming to validate the AR.I.E.T.T.A. score and improve the diagnostic accuracy will improve the system.

  19. Automated intelligent video surveillance system for ships

    Wei, Hai; Nguyen, Hieu; Ramu, Prakash; Raju, Chaitanya; Liu, Xiaoqing; Yadegar, Jacob

    2009-05-01

    To protect naval and commercial ships from attack by terrorists and pirates, it is important to have automatic surveillance systems able to detect, identify, track and alert the crew on small watercrafts that might pursue malicious intentions, while ruling out non-threat entities. Radar systems have limitations on the minimum detectable range and lack high-level classification power. In this paper, we present an innovative Automated Intelligent Video Surveillance System for Ships (AIVS3) as a vision-based solution for ship security. Capitalizing on advanced computer vision algorithms and practical machine learning methodologies, the developed AIVS3 is not only capable of efficiently and robustly detecting, classifying, and tracking various maritime targets, but also able to fuse heterogeneous target information to interpret scene activities, associate targets with levels of threat, and issue the corresponding alerts/recommendations to the man-in- the-loop (MITL). AIVS3 has been tested in various maritime scenarios and shown accurate and effective threat detection performance. By reducing the reliance on human eyes to monitor cluttered scenes, AIVS3 will save the manpower while increasing the accuracy in detection and identification of asymmetric attacks for ship protection.

  20. Method for detecting binding efficiencies of synthetic oligonucleotides: Targeting bacteria and insects

    Expanding applications of gene-based targeting biotechnology in functional genomics and the treatment of plants, animals, and microbes has synergized the need for new methods to measure binding efficiencies of these products to their genetic targets. The adaptation and innovative use of Cell–Penetra...

  1. Detecting Targeted Malicious Email through Supervised Classification of Persistent Threat and Recipient Oriented Features

    Amin, Rohan Mahesh

    2010-01-01

    Targeted email attacks to enable computer network exploitation have become more prevalent, more insidious, and more widely documented in recent years. Beyond nuisance spam or phishing designed to trick users into revealing personal information, targeted malicious email (TME) facilitates computer network exploitation and the gathering of sensitive…

  2. Breaking camouflage and detecting targets require optic flow and image structure information.

    Pan, Jing Samantha; Bingham, Ned; Chen, Chang; Bingham, Geoffrey P

    2017-08-01

    Use of motion to break camouflage extends back to the Cambrian [In the Blink of an Eye: How Vision Sparked the Big Bang of Evolution (New York Basic Books, 2003)]. We investigated the ability to break camouflage and continue to see camouflaged targets after motion stops. This is crucial for the survival of hunting predators. With camouflage, visual targets and distracters cannot be distinguished using only static image structure (i.e., appearance). Motion generates another source of optical information, optic flow, which breaks camouflage and specifies target locations. Optic flow calibrates image structure with respect to spatial relations among targets and distracters, and calibrated image structure makes previously camouflaged targets perceptible in a temporally stable fashion after motion stops. We investigated this proposal using laboratory experiments and compared how many camouflaged targets were identified either with optic flow information alone or with combined optic flow and image structure information. Our results show that the combination of motion-generated optic flow and target-projected image structure information yielded efficient and stable perception of camouflaged targets.

  3. Target detection and localization in shallow water: an experimental demonstration of the acoustic barrier problem at the laboratory scale.

    Marandet, Christian; Roux, Philippe; Nicolas, Barbara; Mars, Jérôme

    2011-01-01

    This study demonstrates experimentally at the laboratory scale the detection and localization of a wavelength-sized target in a shallow ultrasonic waveguide between two source-receiver arrays at 3 MHz. In the framework of the acoustic barrier problem, at the 1/1000 scale, the waveguide represents a 1.1-km-long, 52-m-deep ocean acoustic channel in the kilohertz frequency range. The two coplanar arrays record in the time-domain the transfer matrix of the waveguide between each pair of source-receiver transducers. Invoking the reciprocity principle, a time-domain double-beamforming algorithm is simultaneously performed on the source and receiver arrays. This array processing projects the multireverberated acoustic echoes into an equivalent set of eigenrays, which are defined by their launch and arrival angles. Comparison is made between the intensity of each eigenray without and with a target for detection in the waveguide. Localization is performed through tomography inversion of the acoustic impedance of the target, using all of the eigenrays extracted from double beamforming. The use of the diffraction-based sensitivity kernel for each eigenray provides both the localization and the signature of the target. Experimental results are shown in the presence of surface waves, and methodological issues are discussed for detection and localization.

  4. Right hemisphere dominance during spatial selective attention and target detection occurs outside the dorsal fronto-parietal network

    Shulman, Gordon L.; Pope, Daniel L. W.; Astafiev, Serguei V.; McAvoy, Mark P.; Snyder, Abraham Z.; Corbetta, Maurizio

    2010-01-01

    Spatial selective attention is widely considered to be right hemisphere dominant. Previous functional magnetic resonance imaging (fMRI) studies, however, have reported bilateral blood-oxygenation-level-dependent (BOLD) responses in dorsal fronto-parietal regions during anticipatory shifts of attention to a location (Kastner et al., 1999; Corbetta et al., 2000; Hopfinger et al., 2000). Right-lateralized activity has mainly been reported in ventral fronto-parietal regions for shifts of attention to an unattended target stimulus (Arrington et al., 2000; Corbetta et al., 2000). However, clear conclusions cannot be drawn from these studies because hemispheric asymmetries were not assessed using direct voxel-wise comparisons of activity in left and right hemispheres. Here, we used this technique to measure hemispheric asymmetries during shifts of spatial attention evoked by a peripheral cue stimulus and during target detection at the cued location. Stimulus-driven shifts of spatial attention in both visual fields evoked right-hemisphere dominant activity in temporo-parietal junction (TPJ). Target detection at the attended location produced a more widespread right hemisphere dominance in frontal, parietal, and temporal cortex, including the TPJ region asymmetrically activated during shifts of spatial attention. However, hemispheric asymmetries were not observed during either shifts of attention or target detection in the dorsal fronto-parietal regions (anterior precuneus, medial intraparietal sulcus, frontal eye fields) that showed the most robust activations for shifts of attention. Therefore, right hemisphere dominance during stimulus-driven shifts of spatial attention and target detection reflects asymmetries in cortical regions that are largely distinct from the dorsal fronto-parietal network involved in the control of selective attention. PMID:20219998

  5. A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection

    Xuan Dau Hoang

    2016-10-01

    Full Text Available On the high-speed connections of the Internet or computer networks, the IP (Internet Protocol packet traffic passing through the network is extremely high, and that makes it difficult for network monitoring and attack detection applications. This paper reviews methods to find the high-occurrence-frequency elements in the data stream and applies the most efficient methods to find Hot-IPs that are high-frequency IP addresses of IP packets passing through the network. Fast finding of Hot-IPs in the IP packet stream can be effectively used in early detection of DDoS (Distributed Denial of Service attack targets and spreading sources of network worms. Research results show that the Count-Min method gives the best overall performance for Hot-IP detection thanks to its low computational complexity, low space requirement and fast processing speed. We also propose an early detection model of DDoS attack targets based on Hot-IP finding, which can be deployed on the target network routers.

  6. A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology.

    Durrant, Jacob D; Amaro, Rommie E; Xie, Lei; Urbaniak, Michael D; Ferguson, Michael A J; Haapalainen, Antti; Chen, Zhijun; Di Guilmi, Anne Marie; Wunder, Frank; Bourne, Philip E; McCammon, J Andrew

    2010-01-22

    Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.

  7. A three-step vehicle detection framework for range estimation using a single camera

    Kanjee, R

    2015-12-01

    Full Text Available This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle...

  8. Targeted detection of murine colonic dysplasia in vivo with flexible multispectral scanning fiber endoscopy

    Joshi, Bishnu P.; Miller, Sharon J.; Lee, Cameron; Gustad, Adam; Seibel, Eric J.; Wang, Thomas D.

    2012-02-01

    We demonstrate a multi-spectral scanning fiber endoscope (SFE) that collects fluorescence images in vivo from three target peptides that bind specifically to murine colonic adenomas. This ultrathin endoscope was demonstrated in a genetically engineered mouse model of spontaneous colorectal adenomas based on somatic Apc (adenomatous polyposis coli) gene inactivation. The SFE delivers excitation at 440, 532, 635 nm with human patients by simultaneously visualizing multiple over expressed molecular targets unique to dysplasia.

  9. Effect of Various Environmental Stressors on Target Detection, Identification, and Marksmanship

    2007-03-01

    diverses conditions environnementales difficiles, dont l’exposition à la chaleur et au froid, le bruit, l’exercice épuisant et la privation de sommeil...urban scenario used for marksmanship testing. The arrow indicates a walking target and the centrepiece depicting a poster of a soldier was a permanent... poster of a soldier was a permanent display that was used to hide targets. The heating and cooling protocols caused significant separations in core

  10. Survey and visual detection of Zaire ebolavirus in clinical samples targeting the nucleoprotein gene in Sierra Leone

    Jing Yuan

    2015-12-01

    Full Text Available Ebola virus (EBOV can lead to severe hemorrhagic fever with a high risk of death in humans and other primates. To guide treatment and prevent spread of the viral infection, a rapid and sensitive detection method is required for clinical samples. Here, we described and evaluated a reverse transcription loop-mediated isothermal amplification (RT-LAMP method to detect Zaire ebolavirus using the nucleoprotein gene (NP as a target sequence. Two different techniques were used, a calcein/Mn2+ complex chromogenic method and real-time turbidity monitoring. The RT-LAMP assay detected the NP target sequence with a limit of 4.56 copies/μL within 45 min under 61°C, a similar even or increase in sensitivity than that of real-time reverse transcription-polymerase chain reaction (RT-PCR. Additionally, all pseudoviral particles or non- Zaire EBOV genomes were negative for LAMP detection, indicating that the assay was highly specific for EBOV. To appraise the availability of the RT-LAMP method for use in clinical diagnosis of EBOV, of 417 blood or swab samples collected from patients with clinically suspected infections in Sierra Leone, 307 were identified for RT-LAMP-based surveillance of EBOV. Therefore, the highly specific and sensitive RT-LAMP method allows the rapid detection of EBOV, and is a suitable tool for clinical screening, diagnosis, and primary quarantine purposes.

  11. Design of mitochondria-targeted colorimetric and ratiometric fluorescent probes for rapid detection of SO2 derivatives in living cells

    Yang, Yutao; Zhou, Tingting; Bai, Bozan; Yin, Caixia; Xu, Wenzhi; Li, Wei

    2018-05-01

    Two mitochondria-targeted colorimetric and ratiometric fluorescent probes for SO2 derivatives were constructed based on the SO2 derivatives-triggered Michael addition reaction. The probes exhibit high specificity toward HSO3-/SO32- by interrupting their conjugation system resulting in a large ratiometric blue shift of 46-121 nm in their emission spectrum. The two well-resolved emission bands can ensure accurate detection of HSO3-. The detection limits were calculated to be 1.09 and 1.35 μM. Importantly, probe 1 and probe 2 were successfully used to fluorescence ratiometric imaging of endogenous HSO3- in BT-474 cells.

  12. Early Detection Rapid Response Program Targets New Noxious Weed Species in Washington State

    Andreas, Jennifer E.; Halpern, Alison D.; DesCamp, Wendy C.; Miller, Timothy W.

    2015-01-01

    Early detection, rapid response is a critical component of invasive plant management. It can be challenging, however, to detect new invaders before they become established if landowners cannot identify species of concern. In order to increase awareness, eye-catching postcards were developed in Washington State as part of a noxious weed educational…

  13. A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4)

    Akhoondzadeh, M.

    2013-04-01

    In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM), have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4). The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST) night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT) data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i) ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii) A multilayer perceptron (MLP) feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii) Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods.

  14. Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

    Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

    2010-01-01

    In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

  15. Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

    Xuesong Zhou

    2010-08-01

    Full Text Available In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

  16. Target-induced structure switching of hairpin aptamers for label-free and sensitive fluorescent detection of ATP via exonuclease-catalyzed target recycling amplification.

    Xu, Yunying; Xu, Jin; Xiang, Yun; Yuan, Ruo; Chai, Yaqin

    2014-01-15

    In this work, we described the development of a new label-free, simple and sensitive fluorescent ATP sensing platform based on exonuclease III (Exo III)-catalyzed target recycling (ECTR) amplification and SYBR Green I indicator. The hairpin aptamer probes underwent conformational structure switching and re-configuration in the presence of ATP, which led to catalytic cleavage of the re-configured aptamers by Exo III to release ATP and to initiate the ECTR process. Such ECTR process resulted in the digestion of a significant number of the hairpin aptamer probes, leading to much less intercalation of SYBR Green I to the hairpin stems and drastic suppression of the fluorescence emission for sensitive ATP detection down to the low nanomolar level. Due to the highly specific affinity bindings between aptamers and ATP, the developed method exhibited excellent selectivity toward ATP against other analogous molecules. Besides, our ATP sensing approach used un-modified aptamer probes and could be performed in a "mix-and-detect" fashion in homogenous solutions. All these distinct advantages of the developed method thus made it hold great potential for the development of simple and robust sensing strategies for the detection of other small molecules. © 2013 Elsevier B.V. All rights reserved.

  17. Intelligent systems

    Irwin, J David

    2011-01-01

    Technology has now progressed to the point that intelligent systems are replacing humans in the decision making processes as well as aiding in the solution of very complex problems. In many cases intelligent systems are already outperforming human activities. Artificial neural networks are not only capable of learning how to classify patterns, such images or sequence of events, but they can also effectively model complex nonlinear systems. Their ability to classify sequences of events is probably more popular in industrial applications where there is an inherent need to model nonlinear system

  18. Intelligent Universe

    Hoyle, F

    1983-01-01

    The subject is covered in chapters, entitled: chance and the universe (synthesis of proteins; the primordial soup); the gospel according to Darwin (discussion of Darwin theory of evolution); life did not originate on earth (fossils from space; life in space); the interstellar connection (living dust between the stars; bacteria in space falling to the earth; interplanetary dust); evolution by cosmic control (microorganisms; genetics); why aren't the others here (a cosmic origin of life); after the big bang (big bang and steady state); the information rich universe; what is intelligence up to; the intelligent universe.

  19. Artificial intelligence

    Perret-Galix, D.

    1992-01-01

    A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of artificial intelligence and related themes. This was reflected in the second international workshop on 'Software Engineering, Artificial Intelligence and Expert Systems in High Energy and Nuclear Physics' which took place from 13-18 January at France Telecom's Agelonde site at La Londe des Maures, Provence. It was the second in a series, the first having been held at Lyon in 1990

  20. Seizure Onset Detection based on a Uni- or Multi-modal Intelligent Seizure Acquisition (UISA/MISA) System

    Conradsen, Isa; Beniczky, Sándor; Wolf, Peter

    2010-01-01

    An automatic Uni- or Multi-modal Inteligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic...... algorithm on motion data is extracting features as “log-sum” measures of discrete wavelet components. Classification into the two groups “seizure” versus “nonseizure” is made based on the support vector machine (SVM) algorithm. The algorithm performs with a sensitivity of 91-100%, a median latency of 1...... second and a specificity of 100% on multi-modal data from five healthy subjects simulating seizures. The uni-modal algorithm based on sEMG data from the subjects and patients performs satisfactorily in some cases. As expected, our results clearly show superiority of the multimodal approach, as compared...

  1. Artificial Intelligence and Moral intelligence

    Laura Pana

    2008-07-01

    Full Text Available We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined, even unpredictable conduct, 2- entities endowed with diverse or even multiple intelligence forms, like moral intelligence, 3- open and, even, free-conduct performing systems (with specific, flexible and heuristic mechanisms and procedures of decision, 4 – systems which are open to education, not just to instruction, 5- entities with “lifegraphy”, not just “stategraphy”, 6- equipped not just with automatisms but with beliefs (cognitive and affective complexes, 7- capable even of reflection (“moral life” is a form of spiritual, not just of conscious activity, 8 – elements/members of some real (corporal or virtual community, 9 – cultural beings: free conduct gives cultural value to the action of a ”natural” or artificial being. Implementation of such characteristics does not necessarily suppose efforts to design, construct and educate machines like human beings. The human moral code is irremediably imperfect: it is a morality of preference, of accountability (not of responsibility and a morality of non-liberty, which cannot be remedied by the invention of ethical systems, by the circulation of ideal values and by ethical (even computing education. But such an imperfect morality needs perfect instruments for its implementation: applications of special logic fields; efficient psychological (theoretical and technical attainments to endow the machine not just with intelligence, but with conscience and even spirit; comprehensive technical

  2. Using a Regression Method for Estimating Performance in a Rapid Serial Visual Presentation Target-Detection Task

    2017-12-01

    Fig. 2 Simulation method; the process for one iteration of the simulation . It was repeated 250 times per combination of HR and FAR. Analysis was...distribution is unlimited. 8 Fig. 2 Simulation method; the process for one iteration of the simulation . It was repeated 250 times per combination of HR...stimuli. Simulations show that this regression method results in an unbiased and accurate estimate of target detection performance. The regression

  3. Detection of occult, undisplaced hip fractures with a dual-energy CT algorithm targeted to detection of bone marrow edema.

    Reddy, T; McLaughlin, P D; Mallinson, P I; Reagan, A C; Munk, P L; Nicolaou, S; Ouellette, H A

    2015-02-01

    The purpose of this study is to describe our initial clinical experience with dual-energy computed tomography (DECT) virtual non-calcium (VNC) images for the detection of bone marrow (BM) edema in patients with suspected hip fracture following trauma. Twenty-five patients presented to the emergency department at a level 1 trauma center between January 1, 2011 and January 1, 2013 with clinical suspicion of hip fracture and normal radiographs were included. All CT scans were performed on a dual-source, dual-energy CT system. VNC images were generated using prototype software and were compared to regular bone reconstructions by two musculoskeletal radiologists in consensus. Radiological and/or clinical diagnosis of fracture at 30-day follow-up was used as the reference standard. Twenty-one patients were found to have DECT-VNC signs of bone marrow edema. Eighteen of these 21 patients were true positive and three were false positive. A concordant fracture was clearly seen on bone reconstruction images in 15 of the 18 true positive cases. In three cases, DECT-VNC was positive for bone marrow edema where bone reconstruction CT images were negative. Four patients demonstrated no DECT-VNC signs of bone marrow edema: two cases were true negative, two cases were false negative. When compared with the gold standard of hip fracture determined at retrospective follow-up, the sensitivity of DECT-VNC images of the hip was 90 %, specificity was 40 %, positive predictive value was 86 %, and negative predictive value was 50 %. Our initial experience would suggest that DECT-VNC is highly sensitive but poorly specific in the diagnosis of hip fractures in patients with normal radiographs. The value of DECT-VNC primarily lies in its ability to help detect fractures which may be subtle or undetectable on bone reconstruction CT images.

  4. Relationships between the Kaufman Brief Intelligence Test and the Wechsler Adult Intelligence Scale-Third Edition.

    Walters, Steven O; Weaver, Kenneth A

    2003-06-01

    The Kaufman Brief Intelligence Test detects learning problems of young students and is a screen for whether a more comprehensive test of intelligence is needed. A study to assess whether this test was valid as an adult intelligence test was conducted with 20 undergraduate psychology majors. The correlations between the Kaufman Brief Intelligence Test's Composite, Vocabulary, and Matrices test scores and their corresponding Wechsler Adult Intelligence Scale-Third Edition test scores, the Full Scale (r=.88), Verbal (r=.77), and Performance scores (r=.87), indicated very strong relationships. In addition, no significant differences were obtained between the Composite, Vocabulary, and Matrices means of the Kaufman Brief Intelligence Test and the Full Scale, Verbal, and Performance means of the WAIS-III. The Kaufman Brief Intelligence Test appears to be a valid test of intelligence for adults.

  5. [Using exon combined target region capture sequencing chip to detect the disease-causing genes of retinitis pigmentosa].

    Rong, Weining; Chen, Xuejuan; Li, Huiping; Liu, Yani; Sheng, Xunlun

    2014-06-01

    To detect the disease-causing genes of 10 retinitis pigmentosa pedigrees by using exon combined target region capture sequencing chip. Pedigree investigation study. From October 2010 to December 2013, 10 RP pedigrees were recruited for this study in Ningxia Eye Hospital. All the patients and family members received complete ophthalmic examinations. DNA was abstracted from patients, family members and controls. Using exon combined target region capture sequencing chip to screen the candidate disease-causing mutations. Polymerase chain reaction (PCR) and direct sequencing were used to confirm the disease-causing mutations. Seventy patients and 23 normal family members were recruited from 10 pedigrees. Among 10 RP pedigrees, 1 was autosomal dominant pedigrees and 9 were autosomal recessive pedigrees. 7 mutations related to 5 genes of 5 pedigrees were detected. A frameshift mutation on BBS7 gene was detected in No.2 pedigree, the patients of this pedigree combined with central obesity, polydactyly and mental handicap. No.2 pedigree was diagnosed as Bardet-Biedl syndrome finally. A missense mutation was detected in No.7 and No.10 pedigrees respectively. Because the patients suffered deafness meanwhile, the final diagnosis was Usher syndrome. A missense mutation on C3 gene related to age-related macular degeneration was also detected in No. 7 pedigrees. A nonsense mutation and a missense mutation on CRB1 gene were detected in No. 1 pedigree and a splicesite mutation on PROM1 gene was detected in No. 5 pedigree. Retinitis pigmentosa is a kind of genetic eye disease with diversity clinical phenotypes. Rapid and effective genetic diagnosis technology combined with clinical characteristics analysis is helpful to improve the level of clinical diagnosis of RP.

  6. Pancreatic cancer cell detection by targeted lipid microbubbles and multiphoton imaging

    Cromey, Benjamin; McDaniel, Ashley; Matsunaga, Terry; Vagner, Josef; Kieu, Khanh Quoc; Banerjee, Bhaskar

    2018-04-01

    Surgical resection of pancreatic cancer represents the only chance of cure and long-term survival in this common disease. Unfortunately, determination of a cancer-free margin at surgery is based on one or two tiny frozen section biopsies, which is far from ideal. Not surprisingly, cancer is usually left behind and is responsible for metastatic disease. We demonstrate a method of receptor-targeted imaging using peptide ligands, lipid microbubbles, and multiphoton microscopy that could lead to a fast and accurate way of examining the entire cut surface during surgery. Using a plectin-targeted microbubble, we performed a blinded in-vitro study to demonstrate avid binding of targeted microbubbles to pancreatic cancer cells but not noncancerous cell lines. Further work should lead to a much-needed point-of-care diagnostic test for determining clean margins in oncologic surgery.

  7. Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

    Zu, Xingshui; Guo, Feng; Huang, Jingchang; Zhao, Qin; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

    2017-01-01

    Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively. PMID:28273838

  8. An Investigation into the Passive Detection of Point Targets Using FM Broadcasts or White Noise

    Lindstrom, Chadwick

    1998-01-01

    Over the past three years, researchers at the University of Washington have been developing a multistatic radar system that uses commercial FM radio broadcasts to detect plasma waves in the ionosphere...

  9. Combination of atomic force microscopy and mass spectrometry for the detection of target protein in the serum samples of children with autism spectrum disorders

    Kaysheva, A. L.; Pleshakova, T. O.; Kopylov, A. T.; Shumov, I. D.; Iourov, I. Y.; Vorsanova, S. G.; Yurov, Y. B.; Ziborov, V. S.; Archakov, A. I.; Ivanov, Y. D.

    2017-10-01

    Possibility of detection of target proteins associated with development of autistic disorders in children with use of combined atomic force microscopy and mass spectrometry (AFM/MS) method is demonstrated. The proposed method is based on the combination of affine enrichment of proteins from biological samples and visualization of these proteins by AFM and MS analysis with quantitative detection of target proteins.

  10. Performance comparison of multi-detector detection statistics in targeted compact binary coalescence GW search

    Haris, K; Pai, Archana

    2016-01-01

    Global network of advanced Interferometric gravitational wave (GW) detectors are expected to be on-line soon. Coherent observation of GW from a distant compact binary coalescence (CBC) with a network of interferometers located in different continents give crucial information about the source such as source location and polarization information. In this paper we compare different multi-detector network detection statistics for CBC search. In maximum likelihood ratio (MLR) based detection appro...

  11. Artificial intelligence and ultrasonic tests in detection of defects; Inteligencias artificiales y ensayos ultrasonicos para la deteccion de defectos

    Barrera Cardiel, G.; Fabian Alvarez, M. a.; Velez Martinez, M.; Villasenor, L.

    2001-07-01

    One of the most serious problems in the quality control of welded unions is the location, identification and classification of defects. As a solution to this problem, a technique for classification, applicable to welded unions done by electric arc welding as well as by friction, is proposed; it is based on ultrasonic signals. The neuronal networks proposed are Kohonen and Multilayer Percept ron, all in a virtual instrument environment. Currently the techniques most used in this field are: radiological analysis (X-rays) and ultrasonic analysis (ultrasonic waves). The X-ray technique in addition to being dangerous requires highly specialized personnel and equipment, therefore its use is restricted. The ultrasonic technique, in spite of being one of the most used for detection of discontinuities, requires personnel with wide experience in the interpretation of ultrasonic signals, this is a time-consuming process which necessarily increases its operation cost. The classification techniques that we propose turn out to be safe, reliable, inexpensive and easy to implement for the solution of this important problem. (Author) 8 refs.

  12. A novel SERRS sandwich-hybridization assay to detect specific DNA target.

    Cécile Feuillie

    Full Text Available In this study, we have applied Surface Enhanced Resonance Raman Scattering (SERRS technology to the specific detection of DNA. We present an innovative SERRS sandwich-hybridization assay that allows specific DNA detection without any enzymatic amplification, such as is the case with Polymerase Chain Reaction (PCR. In some substrates, such as ancient or processed remains, enzymatic amplification fails due to DNA alteration (degradation, chemical modification or to the presence of inhibitors. Consequently, the development of a non-enzymatic method, allowing specific DNA detection, could avoid long, expensive and inconclusive amplification trials. Here, we report the proof of concept of a SERRS sandwich-hybridization assay that leads to the detection of a specific chamois DNA. This SERRS assay reveals its potential as a non-enzymatic alternative technology to DNA amplification methods (particularly the PCR method with several applications for species detection. As the amount and type of damage highly depend on the preservation conditions, the present SERRS assay would enlarge the range of samples suitable for DNA analysis and ultimately would provide exciting new opportunities for the investigation of ancient DNA in the fields of evolutionary biology and molecular ecology, and of altered DNA in food frauds detection and forensics.

  13. The California Law Enforcement Community’s Intelligence-Led Policing Capacity

    2010-12-01

    intelligence product used for sound decision making , strategic targeting, and more efficient resource allocation, whereas lack of clarity and the...providing law enforcement executives with actionable intelligence products for sound decision making , strategic targeting, and efficient resource

  14. Generic detection of poleroviruses using an RT-PCR assay targeting the RdRp coding sequence.

    Lotos, Leonidas; Efthimiou, Konstantinos; Maliogka, Varvara I; Katis, Nikolaos I

    2014-03-01

    In this study a two-step RT-PCR assay was developed for the generic detection of poleroviruses. The RdRp coding region was selected as the primers' target, since it differs significantly from that of other members in the family Luteoviridae and its sequence can be more informative than other regions in the viral genome. Species specific RT-PCR assays targeting the same region were also developed for the detection of the six most widespread poleroviral species (Beet mild yellowing virus, Beet western yellows virus, Cucurbit aphid-borne virus, Carrot red leaf virus, Potato leafroll virus and Turnip yellows virus) in Greece and the collection of isolates. These isolates along with other characterized ones were used for the evaluation of the generic PCR's detection range. The developed assay efficiently amplified a 593bp RdRp fragment from 46 isolates of 10 different Polerovirus species. Phylogenetic analysis using the generic PCR's amplicon sequence showed that although it cannot accurately infer evolutionary relationships within the genus it can differentiate poleroviruses at the species level. Overall, the described generic assay could be applied for the reliable detection of Polerovirus infections and, in combination with the specific PCRs, for the identification of new and uncharacterized species in the genus. Copyright © 2013 Elsevier B.V. All rights reserved.

  15. Plant intelligence

    Lipavská, Helena; Žárský, Viktor

    2009-01-01

    The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test. “You were hurt?” Kumiko said, looking at the scar. Sally looked down. “Yeah.” “Why didn't you have it removed?” “Sometimes it's good to remember.” “Being hurt?” “Being stupid.”—(W. Gibson: Mona Lisa Overdrive) PMID:19816094

  16. Speech Intelligibility

    Brand, Thomas

    Speech intelligibility (SI) is important for different fields of research, engineering and diagnostics in order to quantify very different phenomena like the quality of recordings, communication and playback devices, the reverberation of auditoria, characteristics of hearing impairment, benefit using hearing aids or combinations of these things.

  17. Time-Resolved Spectroscopy and Near Infrared Imaging for Prostate Cancer Detection: Receptor-targeted and Native Biomarker

    Pu, Yang

    Optical spectroscopy and imaging using near-infrared (NIR) light provides powerful tools for non-invasive detection of cancer in tissue. Optical techniques are capable of quantitative reconstructions maps of tissue absorption and scattering properties, thus can map in vivo the differences in the content of certain marker chromophores and/or fluorophores in normal and cancerous tissues (for example: water, tryptophan, collagen and NADH contents). Potential clinical applications of optical spectroscopy and imaging include functional tumor detection and photothermal therapeutics. Optical spectroscopy and imaging apply contrasts from intrinsic tissue chromophores such as water, collagen and NADH, and extrinsic optical contrast agents such as Indocyanine Green (ICG) to distinguish disease tissue from the normal one. Fluorescence spectroscopy and imaging also gives high sensitivity and specificity for biomedical diagnosis. Recent developments on specific-targeting fluorophores such as small receptor-targeted dye-peptide conjugate contrast agent offer high contrast between normal and cancerous tissues hence provide promising future for early tumour detection. This thesis focus on a study to distinguish the cancerous prostate tissue from the normal prostate tissues with enhancement of specific receptor-targeted prostate cancer contrast agents using optical spectroscopy and imaging techniques. The scattering and absorption coefficients, and anisotropy factor of cancerous and normal prostate tissues were investigated first as the basis for the biomedical diagnostic and optical imaging. Understanding the receptors over-expressed prostate cancer cells and molecular target mechanism of ligand, two small ICG-derivative dye-peptides, namely Cypate-Bombesin Peptide Analogue Conjugate (Cybesin) and Cypate-Octreotate Peptide Conjugate (Cytate), were applied to study their clinical potential for human prostate cancer detection. In this work, the steady-state and time

  18. Binary Masking & Speech Intelligibility

    Boldt, Jesper

    The purpose of this thesis is to examine how binary masking can be used to increase intelligibility in situations where hearing impaired listeners have difficulties understanding what is being said. The major part of the experiments carried out in this thesis can be categorized as either experime......The purpose of this thesis is to examine how binary masking can be used to increase intelligibility in situations where hearing impaired listeners have difficulties understanding what is being said. The major part of the experiments carried out in this thesis can be categorized as either...... experiments under ideal conditions or as experiments under more realistic conditions useful for real-life applications such as hearing aids. In the experiments under ideal conditions, the previously defined ideal binary mask is evaluated using hearing impaired listeners, and a novel binary mask -- the target...... binary mask -- is introduced. The target binary mask shows the same substantial increase in intelligibility as the ideal binary mask and is proposed as a new reference for binary masking. In the category of real-life applications, two new methods are proposed: a method for estimation of the ideal binary...

  19. Identification of target cells by immunohistochemical detection of covalently rearranged estradiol in rehydrated paraffin sections.

    Jungblut, P W; Sierralta, W D

    1998-04-01

    Estradiol is released from the binding niche of the receptor and covalently arrested in the molecular vicinity by the Mannich reaction during target fixation in acetic acid/formaldehyde. The exposed steroid is freely accessible for appropriate antibodies. It can be visualized in sections by the second antibody/enzyme technique in high resolution and without enhancements.

  20. Quencher-free molecular beacon tethering 7-hydroxycoumarin detects targets through protonation/deprotonation.

    Kashida, Hiromu; Yamaguchi, Kyohei; Hara, Yuichi; Asanuma, Hiroyuki

    2012-07-15

    In this study, we synthesized a simple but efficient quencher-free molecular beacon tethering 7-hydroxycoumarin on D-threoninol based on its pK(a) change. The pK(a) of 7-hydroxycoumarin in a single strand was determined as 8.8, whereas that intercalated in the duplex was over 10. This large pK(a) shift (more than 1.2) upon hybridization could be attributed to the anionic and hydrophobic microenvironment inside the DNA duplex. Because 7-hydroxycoumarin quenches its fluorescence upon protonation, the emission intensity of the duplex at pH 8.5 was 1/15 that of the single strand. We applied this quenching mechanism to the preparation of a quencher-free molecular beacon by introducing the dye into the middle of the stem part. In the absence of the target, the stem region formed a duplex and fluorescence was quenched. However, when the target was added, the molecular beacon opened and the dye was deprotonated. As a result, the emission intensity of the molecular beacon with the target was 10 times higher than that without the target. Accordingly, a quencher-free molecular beacon utilizing the pK(a) change was successfully developed. Copyright © 2012 Elsevier Ltd. All rights reserved.

  1. Agroterrorism targeting livestock: a review with a focus on early detection systems

    Elbers, A.R.W.; Knutsson, R.

    2013-01-01

    Agroterrorism targeting livestock can be described as the intentional introduction of an animal disease agent against livestock with the purpose of causing economic damage, disrupting socioeconomic stability of a country, and creating panic and distress. This type of terrorism can be alluring to

  2. Multi-target detection and positioning in crowds using multiple camera surveillance

    Huang, Jiahu; Zhu, Qiuyu; Xing, Yufeng

    2018-04-01

    In this study, we propose a pixel correspondence algorithm for positioning in crowds based on constraints on the distance between lines of sight, grayscale differences, and height in a world coordinates system. First, a Gaussian mixture model is used to obtain the background and foreground from multi-camera videos. Second, the hair and skin regions are extracted as regions of interest. Finally, the correspondences between each pixel in the region of interest are found under multiple constraints and the targets are positioned by pixel clustering. The algorithm can provide appropriate redundancy information for each target, which decreases the risk of losing targets due to a large viewing angle and wide baseline. To address the correspondence problem for multiple pixels, we construct a pixel-based correspondence model based on a similar permutation matrix, which converts the correspondence problem into a linear programming problem where a similar permutation matrix is found by minimizing an objective function. The correct pixel correspondences can be obtained by determining the optimal solution of this linear programming problem and the three-dimensional position of the targets can also be obtained by pixel clustering. Finally, we verified the algorithm with multiple cameras in experiments, which showed that the algorithm has high accuracy and robustness.

  3. Early Detection of Prostate Cancer with New Nanoparticle-Based Ultrasound Contrast Agents Targeted to PSMA

    2017-08-01

    protein therapy making use of a filamentous nanotechnology. The targeted delivery of doxorubicin and tumor necrosis factor-related apoptosis...3/31/2012 Vascular modulation with or without chemotherapy for enhancement of RF ablation; Role: Co- Investigator 2010 SIR Foundation Grant (PI...Characterization of Slow Precipitating Implants for Vascular Occlusion. IEEE International Ultrasonics Symposium 2017 Annual meeting. In preparation

  4. Analytical purification of a 60-kDa target protein of artemisinin detected in Trypanosoma brucei brucei

    Benetode Konziase

    2015-12-01

    Full Text Available Here we describe the isolation and purity determination of Trypanosoma brucei (T. b. brucei candidate target proteins of artemisinin. The candidate target proteins were detected and purified from their biological source (T. b. brucei lysate using the diazirine-free biotinylated probe 5 for an affinity binding to a streptavidin-tagged resin and, subsequently, the labeled target proteins were purified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE. We herein showed the electrophoresis gel and the immunoblotting film containing the 60-kDa trypanosomal candidate target protein of artemisinin as a single band, which was visualized on-gel by the reverse-staining method and on a Western blotting film by enhanced chemiluminescence. The data provided in this article are related to the original research article “Biotinylated probes of artemisinin with labeling affinity toward Trypanosoma brucei brucei target proteins”, by Konziase (Anal. Biochem., vol. 482, 2015, pp. 25–31. http://dx.doi.org/10.1016/j.ab.2015.04.020.

  5. Tumor Specific Detection of an Optically Targeted Antibody Combined with a Quencher-conjugated Neutravidin “Quencher-Chaser”: A Dual “Quench and Chase” Strategy to Improve Target to Non-target Ratios for Molecular Imaging of Cancer

    Ogawa, Mikako; Kosaka, Nobuyuki; Choyke, Peter L; Kobayashi, Hisataka

    2009-01-01

    In vivo molecular cancer imaging with monoclonal antibodies has great potential not only for cancer detection but also for cancer characterization. However, the prolonged retention of intravenously injected antibody in the blood causes low target tumor-to-background ratio (TBR). Avidin has been used as a “chase” to clear the unbound, circulating biotinylated antibody and decrease the background signal. Here, we utilize a combined approach of a Fluorescence Resonance Energy Transfer (FRET) quenched antibody with an “avidin chase” to increase TBR. Trastuzumab, a humanized monoclonal antibody against human epidermal growth factor receptor type 2 (HER2), was biotinylated and conjugated with the near-infrared (NIR) fluorophore Alexa680 to synthesize Tra-Alexa680-biotin. Next, the FRET quencher, QSY-21, was conjugated to avidin, neutravidin (nAv) or streptavidin (sAv), thus creating Av-QSY21, nAv-QSY21 or sAv-QSY21 as “chasers”. The fluorescence was quenched in vitro by binding Tra-Alexa680-biotin to Av-QSY21, nAv-QSY21 or sAv-QSY21. To evaluate if the injection of quencher-conjugated avidin-derivatives can improve target TBR by using a dual “quench and chase” strategy, both target (3T3/HER2+) and non-target (Balb3T3/ZsGreen) tumor bearing mice were employed. The “FRET quench” effect induced by all the QSY21 avidin-based conjugates reduced but did not totally eliminate background signal from the blood pool. The addition of nAv-QSY21 administration increased target TBR mainly due to the “chase” effect where unbound conjugated antibody was preferentially cleared to the liver. The relatively slow clearance of unbound nAv-QSY21 leads to further reductions in background signal by leaking out of the vascular space and binding to unbound antibodies in the extravascular space of tumors resulting in decreased non-target tumor-to-background ratios but increased target TBR due to the “FRET quench” effect because target-bound antibodies were internalized

  6. Change detection in urban and rural driving scenes: Effects of target type and safety relevance on change blindness.

    Beanland, Vanessa; Filtness, Ashleigh J; Jeans, Rhiannon

    2017-03-01

    The ability to detect changes is crucial for safe driving. Previous research has demonstrated that drivers often experience change blindness, which refers to failed or delayed change detection. The current study explored how susceptibility to change blindness varies as a function of the driving environment, type of object changed, and safety relevance of the change. Twenty-six fully-licenced drivers completed a driving-related change detection task. Changes occurred to seven target objects (road signs, cars, motorcycles, traffic lights, pedestrians, animals, or roadside trees) across two environments (urban or rural). The contextual safety relevance of the change was systematically manipulated within each object category, ranging from high safety relevance (i.e., requiring a response by the driver) to low safety relevance (i.e., requiring no response). When viewing rural scenes, compared with urban scenes, participants were significantly faster and more accurate at detecting changes, and were less susceptible to "looked-but-failed-to-see" errors. Interestingly, safety relevance of the change differentially affected performance in urban and rural environments. In urban scenes, participants were more efficient at detecting changes with higher safety relevance, whereas in rural scenes the effect of safety relevance has marginal to no effect on change detection. Finally, even after accounting for safety relevance, change blindness varied significantly between target types. Overall the results suggest that drivers are less susceptible to change blindness for objects that are likely to change or move (e.g., traffic lights vs. road signs), and for moving objects that pose greater danger (e.g., wild animals vs. pedestrians). Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Hyperspectral target detection analysis of a cluttered scene from a virtual airborne sensor platform using MuSES

    Packard, Corey D.; Viola, Timothy S.; Klein, Mark D.

    2017-10-01

    The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been

  8. Dynamical scene analysis with a moving camera: mobile targets detection system

    Hennebert, Christine

    1996-01-01

    This thesis work deals with the detection of moving objects in monocular image sequences acquired with a mobile camera. We propose a method able to detect small moving objects in visible or infrared images of real outdoor scenes. In order to detect objects of very low apparent motion, we consider an analysis on a large temporal interval. We have chosen to compensate for the dominant motion due to the camera displacement for several consecutive images in order to form a sub-sequence of images for which the camera seems virtually static. We have also developed a new approach allowing to extract the different layers of a real scene in order to deal with cases where the 2D motion due to the camera displacement cannot be globally compensated for. To this end, we use a hierarchical model with two levels: the local merging step and the global merging one. Then, an appropriate temporal filtering is applied to registered image sub-sequence to enhance signals corresponding to moving objects. The detection issue is stated as a labeling problem within a statistical regularization based on Markov Random Fields. Our method has been validated on numerous real image sequences depicting complex outdoor scenes. Finally, the feasibility of an integrated circuit for mobile object detection has been proved. This circuit could lead to an ASIC creation. (author) [fr

  9. Identification of Sarcosine as a Target Molecule for the Canine Olfactory Detection of Prostate Carcinoma.

    Pacik, Dalibor; Plevova, Mariana; Urbanova, Lucie; Lackova, Zuzana; Strmiska, Vladislav; Necas, Alois; Heger, Zbynek; Adam, Vojtech

    2018-03-21

    The hypothesis that dogs can detect malignant tumours through the identification of specific molecules is nearly 30 years old. To date, several reports have described the successful detection of distinct types of cancer. However, is still a lack of data regarding the specific molecules that can be recognized by a dog's olfactory apparatus. Hence, we performed a study with artificially prepared, well-characterized urinary specimens that were enriched with sarcosine, a widely reported urinary biomarker for prostate cancer (PCa). For the purposes of the study, a German shepherd dog was utilized for analyses of 60 positive and 120 negative samples. Our study provides the first evidence that a sniffer dog specially trained for the olfactory detection of PCa can recognize sarcosine in artificial urine with a performance [sensitivity of 90%, specificity of 95%, and precision of 90% for the highest amount of sarcosine (10 µmol/L)] that is comparable to the identification of PCa-diagnosed subjects (sensitivity of 93.5% and specificity of 91.6%). This study casts light on the unrevealed phenomenon of PCa olfactory detection and opens the door for further studies with canine olfactory detection and cancer diagnostics.

  10. Critical heat flux acoustic detection: Methods and application to ITER divertor vertical target monitoring

    Courtois, X., E-mail: xavier.courtois@cea.fr [CEA, IRFM, F-13108 Saint-Paul-Lez-Durance (France); Escourbiac, F. [ITER Organization, Route de Vinon sur Verdon, F-13115 Saint-Paul-Lez-Durance (France); Richou, M.; Cantone, V. [CEA, IRFM, F-13108 Saint-Paul-Lez-Durance (France); Constans, S. [AREVA-NP, Le Creusot (France)

    2013-10-15

    Actively cooled plasma facing components (PFCs) have to exhaust high heat fluxes from plasma radiation and plasma–wall interaction. Critical heat flux (CHF) event may occur in the cooling channel due to unexpected heat loading or operational conditions, and has to be detected as soon as possible. Therefore it is essential to develop means of monitoring based on precursory signals providing an early detection of this destructive phenomenon, in order to be able to stop operation before irremediable damages appear. Capabilities of CHF early detection based on acoustic techniques on PFC mock-ups cooled by pressurised water were already demonstrated. This paper addresses the problem of the detection in case of flow rate reduction and of flow dilution resulting from multiple plasma facing units (PFU) which are hydraulically connected in parallel, which is the case of ITER divertor. An experimental study is launched on a dedicated mock-up submitted to heat loads up to the CHF. It shows that the measurement of the acoustic waves, generated by the cooling phenomena, allows the CHF detection in conditions similar to that of the ITER divertor, with a reasonable number of sensors. The paper describes the mock-ups and the tests sequences, and comments the results.

  11. CD33 monoclonal antibody conjugated Au cluster nano-bioprobe for targeted flow-cytometric detection of acute myeloid leukaemia

    Retnakumari, Archana; Jayasimhan, Jasusri; Chandran, Parwathy; Menon, Deepthy; Nair, Shantikumar; Mony, Ullas; Koyakutty, Manzoor

    2011-07-01

    Protein stabilized gold nanoclusters (Au-NCs) are biocompatible, near-infrared (NIR) emitting nanosystems having a wide range of biomedical applications. Here, we report the development of a Au-NC based targeted fluorescent nano-bioprobe for the flow-cytometric detection of acute myeloid leukaemia (AML) cells. Au-NCs with ~ 25-28 atoms showing bright red-NIR fluorescence (600-750 nm) and average size of ~ 0.8 nm were prepared by bovine serum albumin assisted reduction-cum-stabilization in aqueous phase. The protein protected clusters were conjugated with monoclonal antibody against CD33 myeloid antigen, which is overexpressed in ~ 99.2% of the primitive population of AML cells, as confirmed by immunophenotyping using flow cytometry. Au-NC-CD33 conjugates having average size of ~ 12 nm retained bright fluorescence over an extended duration of ~ a year, as the albumin protein protects Au-NCs against degradation. Nanotoxicity studies revealed excellent biocompatibility of Au-NC conjugates, as they showed no adverse effect on the cell viability and inflammatory response. Target specificity of the conjugates for detecting CD33 expressing AML cells (KG1a) in flow cytometry showed specific staining of ~ 95.4% of leukaemia cells within 1-2 h compared to a non-specific uptake of ~ 8.2% in human peripheral blood cells (PBMCs) which are CD33low. The confocal imaging also demonstrated the targeted uptake of CD33 conjugated Au-NCs by leukaemia cells, thus confirming the flow cytometry results. This study demonstrates that novel nano-bioprobes can be developed using protein protected fluorescent nanoclusters of Au for the molecular receptor targeted flow cytometry based detection and imaging of cancer cells.

  12. CD33 monoclonal antibody conjugated Au cluster nano-bioprobe for targeted flow-cytometric detection of acute myeloid leukaemia

    Retnakumari, Archana; Jayasimhan, Jasusri; Chandran, Parwathy; Menon, Deepthy; Nair, Shantikumar; Mony, Ullas; Koyakutty, Manzoor

    2011-01-01

    Protein stabilized gold nanoclusters (Au-NCs) are biocompatible, near-infrared (NIR) emitting nanosystems having a wide range of biomedical applications. Here, we report the development of a Au-NC based targeted fluorescent nano-bioprobe for the flow-cytometric detection of acute myeloid leukaemia (AML) cells. Au-NCs with ∼ 25-28 atoms showing bright red-NIR fluorescence (600-750 nm) and average size of ∼ 0.8 nm were prepared by bovine serum albumin assisted reduction-cum-stabilization in aqueous phase. The protein protected clusters were conjugated with monoclonal antibody against CD33 myeloid antigen, which is overexpressed in ∼ 99.2% of the primitive population of AML cells, as confirmed by immunophenotyping using flow cytometry. Au-NC-CD33 conjugates having average size of ∼ 12 nm retained bright fluorescence over an extended duration of ∼ a year, as the albumin protein protects Au-NCs against degradation. Nanotoxicity studies revealed excellent biocompatibility of Au-NC conjugates, as they showed no adverse effect on the cell viability and inflammatory response. Target specificity of the conjugates for detecting CD33 expressing AML cells (KG1a) in flow cytometry showed specific staining of ∼ 95.4% of leukaemia cells within 1-2 h compared to a non-specific uptake of ∼ 8.2% in human peripheral blood cells (PBMCs) which are CD33 low . The confocal imaging also demonstrated the targeted uptake of CD33 conjugated Au-NCs by leukaemia cells, thus confirming the flow cytometry results. This study demonstrates that novel nano-bioprobes can be developed using protein protected fluorescent nanoclusters of Au for the molecular receptor targeted flow cytometry based detection and imaging of cancer cells.

  13. CD33 monoclonal antibody conjugated Au cluster nano-bioprobe for targeted flow-cytometric detection of acute myeloid leukaemia

    Retnakumari, Archana; Jayasimhan, Jasusri; Chandran, Parwathy; Menon, Deepthy; Nair, Shantikumar; Mony, Ullas; Koyakutty, Manzoor, E-mail: manzoork@aims.amrita.edu, E-mail: ullasmony@aims.amrita.edu [Amrita Centre for Nanoscience and Molecular Medicine, Amrita Institute of Medical Science, Cochin 682 041 (India)

    2011-07-15

    Protein stabilized gold nanoclusters (Au-NCs) are biocompatible, near-infrared (NIR) emitting nanosystems having a wide range of biomedical applications. Here, we report the development of a Au-NC based targeted fluorescent nano-bioprobe for the flow-cytometric detection of acute myeloid leukaemia (AML) cells. Au-NCs with {approx} 25-28 atoms showing bright red-NIR fluorescence (600-750 nm) and average size of {approx} 0.8 nm were prepared by bovine serum albumin assisted reduction-cum-stabilization in aqueous phase. The protein protected clusters were conjugated with monoclonal antibody against CD33 myeloid antigen, which is overexpressed in {approx} 99.2% of the primitive population of AML cells, as confirmed by immunophenotyping using flow cytometry. Au-NC-CD33 conjugates having average size of {approx} 12 nm retained bright fluorescence over an extended duration of {approx} a year, as the albumin protein protects Au-NCs against degradation. Nanotoxicity studies revealed excellent biocompatibility of Au-NC conjugates, as they showed no adverse effect on the cell viability and inflammatory response. Target specificity of the conjugates for detecting CD33 expressing AML cells (KG1a) in flow cytometry showed specific staining of {approx} 95.4% of leukaemia cells within 1-2 h compared to a non-specific uptake of {approx} 8.2% in human peripheral blood cells (PBMCs) which are CD33{sup low}. The confocal imaging also demonstrated the targeted uptake of CD33 conjugated Au-NCs by leukaemia cells, thus confirming the flow cytometry results. This study demonstrates that novel nano-bioprobes can be developed using protein protected fluorescent nanoclusters of Au for the molecular receptor targeted flow cytometry based detection and imaging of cancer cells.

  14. Sensor and methods of detecting target materials and situations in closed systems

    Mee, David K.; Ripley, Edward B.; Nienstedt, Zachary C.; Nienstedt, Alex W.; Howell, Jr., Layton N.

    2018-03-13

    Disclosed is a passive, in-situ pressure sensor. The sensor includes a sensing element having a ferromagnetic metal and a tension inducing mechanism coupled to the ferromagnetic metal. The tension inducing mechanism is operable to change a tensile stress upon the ferromagnetic metal based on a change in pressure in the sensing element. Changes in pressure are detected based on changes in the magnetic switching characteristics of the ferromagnetic metal when subjected to an alternating magnetic field caused by the change in the tensile stress. The sensing element is embeddable in a closed system for detecting pressure changes without the need for any penetrations of the system for power or data acquisition by detecting changes in the magnetic switching characteristics of the ferromagnetic metal caused by the tensile stress.

  15. Development and Testing of a Multiple Frequency Continuous Wave Radar for Target Detection and Classification

    2007-03-01

    1 2’ VIH " 1 ’ 󈧏) (34) where is the modified Bessel function of zero order. Here is the conditional variance and is the conditional probability...10, the probability of detection is the area under the signal-plus-noise curve above the detection threshold co M vF (V 2+ A2)]10 ( vAPd= fnp~ju,( vIH ...Spectrogram O /STFT < 12 +J F Q’I " ’ " ""-’"’" -STFT TFRgram 2I1+ IST 21 U- •’j -/STFT,, I HP STFT ISTFTI Figure 19. 3FCW radar processing prior to

  16. Detectability Measurement of GPR for Buried Target in Self-Designed Test Field

    Son, Soo Jung; Shin, Byoung Chul

    2000-01-01

    In this paper, we were investigated the detectability on various specimen in self-designed test field using the GPR system with three antenna elements. The GPR system was constantly radiated 730MHz frequency. To examine the detectability on various condition, the test were experimented using different materials, size and buried depth. As an adjusted wave-propagation velocity, the location of hyperbolic curve pattern were displayed B-scan CRT. And the pattern was exactly positioned when it was compared to the real buried-depth. Therefore, we can confirm similarity between the wave-propagation velocity and previous results

  17. Improving specificity of Bordetella pertussis detection using a four target real-time PCR.

    Helena Martini

    Full Text Available The incidence of whooping cough, a contagious respiratory disease caused by Bordetella pertussis, is on the rise despite existing vaccination programmes. Similar, though usually milder, respiratory symptoms may be caused by other members of the Bordetella genus: B. parapertussis, B. holmesii, and B. bronchiseptica. Pertussis diagnosis is mostly done using PCR, but the use of multiple targets is necessary in order to differentiate the different Bordetella spp. with sufficient sensitivity and specificity. In this study we evaluate a multiplex PCR assay for the differentiation of B. pertussis from other Bordetella spp., using the targets IS481, IS1001, IS1002, and recA. Moreover, we retrospectively explore the epidemiology of Bordetella spp. infections in Belgium, using the aforementioned assay over a three-year period, from 2013 until 2015.

  18. Improving specificity of Bordetella pertussis detection using a four target real-time PCR

    Detemmerman, Liselot; Soetens, Oriane; Yusuf, Erlangga; Piérard, Denis

    2017-01-01

    The incidence of whooping cough, a contagious respiratory disease caused by Bordetella pertussis, is on the rise despite existing vaccination programmes. Similar, though usually milder, respiratory symptoms may be caused by other members of the Bordetella genus: B. parapertussis, B. holmesii, and B. bronchiseptica. Pertussis diagnosis is mostly done using PCR, but the use of multiple targets is necessary in order to differentiate the different Bordetella spp. with sufficient sensitivity and specificity. In this study we evaluate a multiplex PCR assay for the differentiation of B. pertussis from other Bordetella spp., using the targets IS481, IS1001, IS1002, and recA. Moreover, we retrospectively explore the epidemiology of Bordetella spp. infections in Belgium, using the aforementioned assay over a three-year period, from 2013 until 2015. PMID:28403204

  19. Design of an intelligent car

    Na, Yongyi

    2017-03-01

    The design of simple intelligent car, using AT89S52 single chip microcomputer as the car detection and control core; The metal sensor TL - Q5MC induction to iron, to detect the way to send feedback to the signal of single chip microcomputer, make SCM according to the scheduled work mode to control the car in the area according to the predetermined speed, and the operation mode of the microcontroller choose different also can control the car driving along s-shaped iron; Use A44E hall element to detect the car speeds; Adopts 1602 LCD display time of car driving, driving the car to stop, take turns to show the car driving time, distance, average speed and the speed of time. This design has simple structure and is easy to implement, but are highly intelligent, humane, to a certain extent reflects the intelligence.

  20. Detection of Traffic Initiated by Mobile Malware Targeting Android Devices in 3GPP Networks

    Kühnel, Marián

    2017-01-01

    Android devices have become the most popular of mobile devices; omnipresent in both business and private use. They are virtually always on and offer functionalities exceeding those of desktop computers. These properties, as well as sensitive information stored on Android devices, render them an attractive target for mobile malware authors. As the volume of mobile malware increases, analysis is becoming challenging and, sometimes, infeasible. Additionally, current network-based intrusion detec...