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Sample records for automatic vehicle classification

  1. Automatically Identification and Classification of Moving Vehicles at Night

    Directory of Open Access Journals (Sweden)

    Atena Khodarahmi

    2012-07-01

    Full Text Available Todays moving object detection plays an important role in computer vision filed. Although a lot of moving objects detection methods has been proposed but monitoring at nights is still a challenging topic. In this paper, a robust algorithm is proposed for automatic detection moving vehicles at night or in environments with low level of light which has quality problems. In this algorithm, first preprocessing steps were conducted. Then all of vehicles in frame identify and classify according their type. Finally, the moving vehicles detected. The results demonstrate that the proposed algorithm significantly outperforms existing algorithm for the detecting and classification of moving vehicles at night.

  2. Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey

    Directory of Open Access Journals (Sweden)

    Kanwal Yousaf

    2012-09-01

    Full Text Available Vehicle classification has emerged as a significant field of study because of its importance in variety of applications like surveillance, security system, traffic congestion avoidance and accidents prevention etc. So far numerous algorithms have been implemented for classifying vehicle. Each algorithm follows different procedures for detecting vehicles from videos. By evaluating some of the commonly used techniques we highlighted most beneficial methodology for classifying vehicles. In this paper we pointed out the working of several video based vehicle classification algorithms and compare these algorithms on the basis of different performance metrics such as classifiers, classification methodology or principles and vehicle detection ratio etc. After comparing these parameters we concluded that Hybrid Dynamic Bayesian Network (HDBN Classification algorithm is far better than the other algorithms due to its nature of estimating the simplest features of vehicles from different videos. HDBN detects vehicles by following important stages of feature extraction, selection and classification. It extracts the rear view information of vehicles rather than other information such as distance between the wheels and height of wheel etc.

  3. Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

    Science.gov (United States)

    Kachach, Redouane; Cañas, José María

    2016-05-01

    Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

  4. Unsupervised automatic music genre classification

    OpenAIRE

    Barreira, Luís Filipe Marques

    2010-01-01

    Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática In this study we explore automatic music genre recognition and classification of digital music. Music has always been a reflection of culture di erences and an influence in our society. Today’s digital content development triggered the massive use of digital music. Nowadays,digital music is manually labeled without following a universa...

  5. Image-based Vehicle Classification System

    CERN Document Server

    Ng, Jun Yee

    2012-01-01

    Electronic toll collection (ETC) system has been a common trend used for toll collection on toll road nowadays. The implementation of electronic toll collection allows vehicles to travel at low or full speed during the toll payment, which help to avoid the traffic delay at toll road. One of the major components of an electronic toll collection is the automatic vehicle detection and classification (AVDC) system which is important to classify the vehicle so that the toll is charged according to the vehicle classes. Vision-based vehicle classification system is one type of vehicle classification system which adopt camera as the input sensing device for the system. This type of system has advantage over the rest for it is cost efficient as low cost camera is used. The implementation of vision-based vehicle classification system requires lower initial investment cost and very suitable for the toll collection trend migration in Malaysia from single ETC system to full-scale multi-lane free flow (MLFF). This project ...

  6. Autoclass: An automatic classification system

    Science.gov (United States)

    Stutz, John; Cheeseman, Peter; Hanson, Robin

    1991-01-01

    The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

  7. Vehicle Classification by Lane Allowance

    Directory of Open Access Journals (Sweden)

    Vishakha Gaikwad

    2014-12-01

    Full Text Available Classification of vehicles from video is used for analysis of traffic, self-driving systems or security systems. This analysis is based on shape, size, velocity and track of vehicles. These features characterize vehicle in background subtraction and feature extraction methods. Extraction is done by active contours and morphological operations. Extracted vehicles are classified by applying various classification techniques. The combination of features and classification techniques varies with the application. Proposed system, Uses combination of K Nearest Neighbor (KNN and Decision Tree techniques to overcome constraints. These constraints are instances of an object, overlapping of objects, and scaling factor. KNN is utilized to classify vehicle by size and lane. Decision tree manipulates the combination of these two features to classify accurately which results increased performance. This system classifies objects into three classes. These classes are four wheeler, bikers and heavy duty vehicle extracted from video.

  8. Automatic Classification of Marine Mammals with Speaker Classification Methods.

    Science.gov (United States)

    Kreimeyer, Roman; Ludwig, Stefan

    2016-01-01

    We present an automatic acoustic classifier for marine mammals based on human speaker classification methods as an element of a passive acoustic monitoring (PAM) tool. This work is part of the Protection of Marine Mammals (PoMM) project under the framework of the European Defense Agency (EDA) and joined by the Research Department for Underwater Acoustics and Geophysics (FWG), Bundeswehr Technical Centre (WTD 71) and Kiel University. The automatic classification should support sonar operators in the risk mitigation process before and during sonar exercises with a reliable automatic classification result.

  9. Experiments in Automatic Library of Congress Classification.

    Science.gov (United States)

    Larson, Ray R.

    1992-01-01

    Presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records from a test database at the University of California at Berkeley Library School library. Classification clustering and matching techniques are described. (44 references) (LRW)

  10. A Novel Vehicle Classification Using Embedded Strain Gauge Sensors

    Directory of Open Access Journals (Sweden)

    Qi Wang

    2008-11-01

    Full Text Available Abstract: This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle’s speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves

  11. Towards Automatic Classification of Neurons

    OpenAIRE

    Armañanzas, Rubén; Ascoli, Giorgio A.

    2015-01-01

    The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting information growth of morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and availability of suitable data and resources, highlighting prominent challenge...

  12. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  13. Towards automatic classification of all WISE sources

    CERN Document Server

    Kurcz, Agnieszka; Solarz, Aleksandra; Krupa, Magdalena; Pollo, Agnieszka; Małek, Katarzyna

    2016-01-01

    The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Here we aim at obtaining comprehensive and reliable star, galaxy and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those reliably measured for a majority of sources. For the automatic classification we applied the support vector machines (SVM) algorithm, which requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset for that purpose. By calibrating the classifier on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnit...

  14. Real time automatic scene classification

    NARCIS (Netherlands)

    Israël, Menno; Broek, van den Egon L.; Putten, van der Peter; Uyl, den Marten J.; Verbrugge, R.; Taatgen, N.; Schomaker, L.

    2004-01-01

    This work has been done as part of the EU VICAR (IST) project and the EU SCOFI project (IAP). The aim of the first project was to develop a real time video indexing classification annotation and retrieval system. For our systems, we have adapted the approach of Picard and Minka [3], who categorized

  15. Automatic Classification of Attacks on IP Telephony

    Directory of Open Access Journals (Sweden)

    Jakub Safarik

    2013-01-01

    Full Text Available This article proposes an algorithm for automatic analysis of attack data in IP telephony network with a neural network. Data for the analysis is gathered from variable monitoring application running in the network. These monitoring systems are a typical part of nowadays network. Information from them is usually used after attack. It is possible to use an automatic classification of IP telephony attacks for nearly real-time classification and counter attack or mitigation of potential attacks. The classification use proposed neural network, and the article covers design of a neural network and its practical implementation. It contains also methods for neural network learning and data gathering functions from honeypot application.

  16. Automatic modulation classification principles, algorithms and applications

    CERN Document Server

    Zhu, Zhechen

    2014-01-01

    Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algo

  17. Automatic classification of blank substrate defects

    Science.gov (United States)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  18. PASTEC: an automatic transposable element classification tool.

    Directory of Open Access Journals (Sweden)

    Claire Hoede

    Full Text Available SUMMARY: The classification of transposable elements (TEs is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system. It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats, rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus. AVAILABILITY: PASTEC is available as a REPET module or standalone software (http://urgi.versailles.inra.fr/download/repet/REPET_linux-x64-2.2.tar.gz. It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque, and the other of which is not.

  19. Towards automatic classification of all WISE sources

    Science.gov (United States)

    Kurcz, A.; Bilicki, M.; Solarz, A.; Krupa, M.; Pollo, A.; Małek, K.

    2016-07-01

    Context. The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. Classifying them reliably is, however, a challenging task owing to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Simple colour cuts are often not sufficient; for satisfactory levels of completeness and purity, more sophisticated classification methods are needed. Aims: Here we aim to obtain comprehensive and reliable star, galaxy, and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those which are reliably measured for the majority of sources. Methods: For the automatic classification we applied a supervised machine learning algorithm, support vector machines (SVM). It requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset (DR10) for that purpose. We tested the performance of two kernels used by the classifier, and determined the minimum number of sources in the training set required to achieve stable classification, as well as the minimum dimension of the parameter space. We also tested SVM classification accuracy as a function of extinction and apparent magnitude. Thus, the calibrated classifier was finally applied to all-sky WISE data, flux-limited to 16 mag (Vega) in the 3.4 μm channel. Results: By calibrating on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnitude, W1-W2 colour, and a differential aperture magnitude) we obtained very good classification efficiency in all the tests. At the bright end, the completeness for stars and galaxies reaches ~95%, deteriorating to ~80% at W1 = 16 mag, while for quasars it stays at a level of

  20. Automatic image classification for the urinoculture screening.

    Science.gov (United States)

    Andreini, Paolo; Bonechi, Simone; Bianchini, Monica; Garzelli, Andrea; Mecocci, Alessandro

    2016-03-01

    Urinary tract infections (UTIs) are considered to be the most common bacterial infection and, actually, it is estimated that about 150 million UTIs occur world wide yearly, giving rise to roughly $6 billion in healthcare expenditures and resulting in 100,000 hospitalizations. Nevertheless, it is difficult to carefully assess the incidence of UTIs, since an accurate diagnosis depends both on the presence of symptoms and on a positive urinoculture, whereas in most outpatient settings this diagnosis is made without an ad hoc analysis protocol. On the other hand, in the traditional urinoculture test, a sample of midstream urine is put onto a Petri dish, where a growth medium favors the proliferation of germ colonies. Then, the infection severity is evaluated by a visual inspection of a human expert, an error prone and lengthy process. In this paper, we propose a fully automated system for the urinoculture screening that can provide quick and easily traceable results for UTIs. Based on advanced image processing and machine learning tools, the infection type recognition, together with the estimation of the bacterial load, can be automatically carried out, yielding accurate diagnoses. The proposed AID (Automatic Infection Detector) system provides support during the whole analysis process: first, digital color images of Petri dishes are automatically captured, then specific preprocessing and spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate classification of the infections and their severity evaluation are performed. The AID system speeds up the analysis, contributes to the standardization of the process, allows result repeatability, and reduces the costs. Moreover, the continuous transition between sterile and external environments (typical of the standard analysis procedure) is completely avoided. PMID:26780249

  1. Automatic Approach to Vhr Satellite Image Classification

    Science.gov (United States)

    Kupidura, P.; Osińska-Skotak, K.; Pluto-Kossakowska, J.

    2016-06-01

    In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step

  2. AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  3. Classification of Dynamic Vehicle Routing Systems

    DEFF Research Database (Denmark)

    Larsen, Allan; Madsen, Oli B.G.; Solomon, Marius M.

    2007-01-01

    to classify dynamic vehicle routing systems. Methods for evaluation of the performance of algorithms that solve on-line routing problems are discussed and we list some of the most important issues to include in the system objective. Finally, we provide a three-echelon classification of dynamic vehicle routing......This chapter discusses important characteristics seen within dynamic vehicle routing problems. We discuss the differences between the traditional static vehicle routing problems and its dynamic counterparts. We give an in-depth introduction to the degree of dynamism measure which can be used...

  4. Automatic classification of eclipsing binaries light curves using neural networks

    CERN Document Server

    Sarro, L M; Giménez, A

    2005-01-01

    In this work we present a system for the automatic classification of the light curves of eclipsing binaries. This system is based on a classification scheme that aims to separate eclipsing binary sistems according to their geometrical configuration in a modified version of the traditional classification scheme. The classification is performed by a Bayesian ensemble of neural networks trained with {\\em Hipparcos} data of seven different categories including eccentric binary systems and two types of pulsating light curve morphologies.

  5. Automatic web services classification based on rough set theory

    Institute of Scientific and Technical Information of China (English)

    陈立; 张英; 宋自林; 苗壮

    2013-01-01

    With development of web services technology, the number of existing services in the internet is growing day by day. In order to achieve automatic and accurate services classification which can be beneficial for service related tasks, a rough set theory based method for services classification was proposed. First, the services descriptions were preprocessed and represented as vectors. Elicited by the discernibility matrices based attribute reduction in rough set theory and taking into account the characteristic of decision table of services classification, a method based on continuous discernibility matrices was proposed for dimensionality reduction. And finally, services classification was processed automatically. Through the experiment, the proposed method for services classification achieves approving classification result in all five testing categories. The experiment result shows that the proposed method is accurate and could be used in practical web services classification.

  6. Automatic segmentation of diatom images for classification

    NARCIS (Netherlands)

    Jalba, Andrei C.; Wilkinson, Michael H.F.; Roerdink, Jos B.T.M.

    2004-01-01

    A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult p

  7. Super pixel density based clustering automatic image classification method

    Science.gov (United States)

    Xu, Mingxing; Zhang, Chuan; Zhang, Tianxu

    2015-12-01

    The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.

  8. Semantic Annotation to Support Automatic Taxonomy Classification

    DEFF Research Database (Denmark)

    Kim, Sanghee; Ahmed, Saeema; Wallace, Ken

    2006-01-01

    This paper presents a new taxonomy classification method that generates classification criteria from a small number of important sentences identified through semantic annotations, e.g. cause-effect. Rhetorical Structure Theory (RST) is used to discover the semantics (Mann et al. 1988). Specifically...

  9. Automatic Medical Image Classification and Abnormality Detection Using KNearest Neighbour

    Directory of Open Access Journals (Sweden)

    Dr. R. J. Ramteke , Khachane Monali Y.

    2012-12-01

    Full Text Available This research work presents a method for automatic classification of medical images in two classes Normal and Abnormal based on image features and automatic abnormality detection. Our proposed system consists of four phases Preprocessing, Feature extraction, Classification, and Post processing. Statistical texture feature set is derived from normal and abnormal images. We used the KNN classifier for classifying image. The KNN classifier performance compared with kernel based SVM classifier (Linear and RBF. The confusion matrix computed and result shows that KNN obtain 80% classification rate which is more than SVM classification rate. So we choose KNN algorithm for classification of images. If image classified as abnormal then post processing step applied on the image and abnormal region is highlighted on the image. The system has been tested on the number of real CT scan brain images.

  10. Towards the automatic classification of neurons.

    Science.gov (United States)

    Armañanzas, Rubén; Ascoli, Giorgio A

    2015-05-01

    The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration. PMID:25765323

  11. Vehicle classification in WAMI imagery using deep network

    Science.gov (United States)

    Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

    2016-05-01

    Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep

  12. AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES

    Directory of Open Access Journals (Sweden)

    Hernández-Tamames Juan Antonio

    2010-12-01

    Full Text Available This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.

  13. Automatic breast density classification using neural network

    Science.gov (United States)

    Arefan, D.; Talebpour, A.; Ahmadinejhad, N.; Kamali Asl, A.

    2015-12-01

    According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network.

  14. Automatic breast density classification using neural network

    International Nuclear Information System (INIS)

    According to studies, the risk of breast cancer directly associated with breast density. Many researches are done on automatic diagnosis of breast density using mammography. In the current study, artifacts of mammograms are removed by using image processing techniques and by using the method presented in this study, including the diagnosis of points of the pectoral muscle edges and estimating them using regression techniques, pectoral muscle is detected with high accuracy in mammography and breast tissue is fully automatically extracted. In order to classify mammography images into three categories: Fatty, Glandular, Dense, a feature based on difference of gray-levels of hard tissue and soft tissue in mammograms has been used addition to the statistical features and a neural network classifier with a hidden layer. Image database used in this research is the mini-MIAS database and the maximum accuracy of system in classifying images has been reported 97.66% with 8 hidden layers in neural network

  15. Personality in speech assessment and automatic classification

    CERN Document Server

    Polzehl, Tim

    2015-01-01

    This work combines interdisciplinary knowledge and experience from research fields of psychology, linguistics, audio-processing, machine learning, and computer science. The work systematically explores a novel research topic devoted to automated modeling of personality expression from speech. For this aim, it introduces a novel personality assessment questionnaire and presents the results of extensive labeling sessions to annotate the speech data with personality assessments. It provides estimates of the Big 5 personality traits, i.e. openness, conscientiousness, extroversion, agreeableness, and neuroticism. Based on a database built on the questionnaire, the book presents models to tell apart different personality types or classes from speech automatically.

  16. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  17. Google started to build automatic driving vehicle

    Institute of Scientific and Technical Information of China (English)

    2014-01-01

    <正>The car will have a stop-go button but no controls,steering wheel or pedals.Pictures of the Google vehicle show it looks like a city car with a"friendly"face,designed to make it seem non-threatening and help people accept self-driving technology.Co-founder Sergey Brin revealed the plans at a conference in California."We’re really excited about this vehicle

  18. Semi-automatic classification of textures in thoracic CT scans

    Science.gov (United States)

    Kockelkorn, Thessa T. J. P.; de Jong, Pim A.; Schaefer-Prokop, Cornelia M.; Wittenberg, Rianne; Tiehuis, Audrey M.; Gietema, Hester A.; Grutters, Jan C.; Viergever, Max A.; van Ginneken, Bram

    2016-08-01

    The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64% for the first slice to 13% for the second half of the scan. Overall, 21% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20%, decreasing from 56% in the first slice to 13% in the second half of the scan.

  19. Automatic lung nodule classification with radiomics approach

    Science.gov (United States)

    Ma, Jingchen; Wang, Qian; Ren, Yacheng; Hu, Haibo; Zhao, Jun

    2016-03-01

    Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA) which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project. Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide improvement in decision-support with much lower cost.

  20. Automatic Pipeline Surveillance Air-Vehicle

    OpenAIRE

    Alqaan, Hani

    2016-01-01

    This thesis presents the developments of a vision-based system for aerial pipeline Right-of-Way surveillance using optical/Infrared sensors mounted on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly automated, on-board system for detecting and following the pipelines; while simultaneously detecting any third-party interference. The proposed approach of using a UAV platform could potentially reduce the cost of monitoring and surveying pipelines when...

  1. Automatic Working Area Classification in Peripheral Blood Smears

    OpenAIRE

    Xiong, Wei; Ong, S. H.; Lim, Joo-Hwee; Chiong, Kelvin Foong Weng; LIU, JIANG; Racoceanu, Daniel; Chong, Alvin; Tan, Kevin,

    2010-01-01

    Cell enumeration and diagnosis using peripheral blood smears are routine tasks in many biological and pathological examinations. Not every area in the smear is appropriate for such tasks due to severe cell clumping or sparsity. Manual working area selection is slow, subjective, inconsistent and statistically biased. Automatic working area classification can reproducibly identify appropriate working smear areas. However, very little research has been reported in the literature. With the aim of...

  2. AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA

    International Nuclear Information System (INIS)

    We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same

  3. Automatic Vehicle Speed Reduction System Using Rf Technology

    Directory of Open Access Journals (Sweden)

    Deepa B Chavan

    2014-04-01

    Full Text Available For vehicle safety and safety for passengers in vehicle is an important parameter. Most of the vehicles get accident because no proper safety measures are taken especially at curves and hair pin bends humps and any obstacles in front of the vehicle. This system can be used for the prevention of such a problem by indicating a pre indication and also reducing the speed of vehicles by reducing the fuel rate of vehicle. As the action is in terms of fuel rate so the vehicle automatically goes to control and avoids the accidents. At curves and hair pin bends the line of sight is not possible for the drivers so the special kind of transmitter which is tuned at a frequency of 433MHZ are mounted as these transmitters continuously radiate a RF signal for some particular area. As the vehicle come within this radiation the receiver in the vehicle gets activate. The transmitter used here is a coded transmitter which is encoded with encoder. The encoder provides a 4 bit binary data which is serially transmitted to transmitter. The transmitter used here is ASK type (amplitude shift keying which emits the RF radiation.

  4. Automatic classification of manual snow profiles by snow structure

    Directory of Open Access Journals (Sweden)

    F. Techel

    2013-12-01

    Full Text Available Manual snowpack observations are an important component of avalanche hazard assessment for the Swiss avalanche forecasting service. Approximately 900 snow profiles are observed each winter, in flat study plots or on representative slopes. So far, these profiles are manually classified combining both information on snow stability (e.g. Rutschblock test and snowpack structure (e.g. layering, hardness. To separate the classification of snowpack stability and structure, and also to reduce inconsistencies in ratings between forecasters, we developed and tested an automatic approach to classify profiles by snowpack structure during two winters. The automatic classification is based on a calculated index, which consists of three components: properties of (1 the slab (thickness, (2 weakest layer interface and (3 the percentage of the snowpack which is soft, coarse-grained and consists of persistent grain types. The latter two indices are strongly based on criteria described in the threshold sum approach. The new snowpack structure index allows a consistent comparison of snowpack structure to detect regional patterns, seasonal or inter-annual differences but may also supplement snow-climate classifications.

  5. Desktop calibration of automatic transmission for passenger vehicle

    Institute of Scientific and Technical Information of China (English)

    FANG Chi; SHI Jian-peng; WANG Jun

    2014-01-01

    Desktop calibration of automatic transmission (AT) is a method which can reduce cost, enhance efficiency and shorten the development periods of a vehicle effectively. We primary introduced the principle and approach of desktop calibration of AT based on the condition of coupling characteristics between engine and torque converter and obtained right point exactly. It is shown to agree with experimental measurements reasonably well. It was used in different applications abroad based on AT technology and achieved a good performance of the vehicle compared with traditional AT technology which primary focuses on the drivability, performance and fuel consumption.

  6. Objective classification of vehicle seat discomfort.

    Science.gov (United States)

    Le, Peter; Rose, Joseph; Knapik, Gregory; Marras, William S

    2014-01-01

    The objective of this study was to identify how physiological measures relate to self-reported vehicle seating discomfort. Twelve subjects of varied anthropometric characteristics were enrolled in the study. Subjects sat in two seats over a 2-h period and were evaluated via three physiological measures (near-infrared spectroscopy, electromyography and pressure mapping) yielding six testing sessions. Subjective discomfort surveys were recorded before and after each session for nine regions of the body. Conditional classification discomfort models were developed through dichotomised physiological responses and anthropometry to predict subjective discomfort in specific body locations. Models revealed that subjects taller than 171 cm with reduced blood oxygenation in the biceps femoris or constant, low-level muscle activity in the trapezius tended to report discomfort in the lower extremities or neck, respectively. Subjects weighing less than 58 kg with reduced blood oxygenation in the biceps femoris or unevenly distributed pressure patterns tended to report discomfort in the buttocks. The sensitivities and specificities of cross-validated models ranged between 0.69 and 1.00. PMID:24606493

  7. Implementation of a remote computer controlled automatic guided vehicle

    OpenAIRE

    Lu, Roberto F.

    1994-01-01

    The effectiveness of a material handling system is essential to a competitive manufacturing environment. Automatic Guided Vehicles (AGVs) are an irnportant technology within today's modern manufacturing facility. Academic programs in manufacturing and industrial engineering must find ways to include this technology in their instructional and research programs to provide the students with sufficient knowledge to address material handling systems design. This project was a fir...

  8. Signal shape feature for automatic snore and breathing sounds classification

    International Nuclear Information System (INIS)

    Snore analysis techniques have recently been developed for sleep studies. Most snore analysis techniques require reliable methods for the automatic classification of snore and breathing sounds in the sound recording. In this study we focus on this problem and propose an automated method to classify snore and breathing sounds based on the novel feature, ‘positive/negative amplitude ratio (PNAR)’, to measure the shape of the sound signal. The performance of the proposed method was evaluated using snore and breathing recordings (snore: 22 643 episodes and breathing: 4664 episodes) from 40 subjects. Receiver operating characteristic (ROC) analysis showed that the proposed method achieved 0.923 sensitivity with 0.918 specificity for snore and breathing sound classification on test data. PNAR has substantial potential as a feature in the front end of a non-contact snore/breathing-based technology for sleep studies. (paper)

  9. Automatic Classification of Offshore Wind Regimes With Weather Radar Observations

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2014-01-01

    fluctuations. The information they provide could then be integrated into an advanced prediction system for improving offshore wind power predictability and controllability. In this paper, we address the automatic classification of offshore wind regimes (i.e., wind fluctuations with specific frequency...... systems are extracted from weather radar images. These attributes describe the global intensity, spatial continuity and motion of precipitation echoes on the images. Finally, a CART classification tree is used to find the broad relationships between precipitation attributes and wind regimes......Weather radar observations are called to play an important role in offshore wind energy. In particular, they can enable the monitoring of weather conditions in the vicinity of large-scale offshore wind farms and thereby notify the arrival of precipitation systems associated with severe wind...

  10. Evolutionary synthesis of automatic classification on astroinformatic big data

    Science.gov (United States)

    Kojecky, Lumir; Zelinka, Ivan; Saloun, Petr

    2016-06-01

    This article describes the initial experiments using a new approach to automatic identification of Be and B[e] stars spectra in large archives. With enormous amount of these data it is no longer feasible to analyze it using classical approaches. We introduce an evolutionary synthesis of the classification by means of analytic programming, one of methods of symbolic regression. By this method, we synthesize the most suitable mathematical formulas that approximate chosen samples of the stellar spectra. As a result is then selected the category whose formula has the lowest difference compared to the particular spectrum. The results show us that classification of stellar spectra by means of analytic programming is able to identify different shapes of the spectra.

  11. Improve mask inspection capacity with Automatic Defect Classification (ADC)

    Science.gov (United States)

    Wang, Crystal; Ho, Steven; Guo, Eric; Wang, Kechang; Lakkapragada, Suresh; Yu, Jiao; Hu, Peter; Tolani, Vikram; Pang, Linyong

    2013-09-01

    As optical lithography continues to extend into low-k1 regime, resolution of mask patterns continues to diminish. The adoption of RET techniques like aggressive OPC, sub-resolution assist features combined with the requirements to detect even smaller defects on masks due to increasing MEEF, poses considerable challenges for mask inspection operators and engineers. Therefore a comprehensive approach is required in handling defects post-inspections by correctly identifying and classifying the real killer defects impacting the printability on wafer, and ignoring nuisance defect and false defects caused by inspection systems. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at the SMIC mask shop for the 40nm technology node. Traditionally, each defect is manually examined and classified by the inspection operator based on a set of predefined rules and human judgment. At SMIC mask shop due to the significant total number of detected defects, manual classification is not cost-effective due to increased inspection cycle time, resulting in constrained mask inspection capacity, since the review has to be performed while the mask stays on the inspection system. Luminescent Technologies Automated Defect Classification (ADC) product offers a complete and systematic approach for defect disposition and classification offline, resulting in improved utilization of the current mask inspection capability. Based on results from implementation of ADC in SMIC mask production flow, there was around 20% improvement in the inspection capacity compared to the traditional flow. This approach of computationally reviewing defects post mask-inspection ensures no yield loss by qualifying reticles without the errors associated with operator mis-classification or human error. The ADC engine retrieves the high resolution inspection images and uses a decision-tree flow to classify a given defect. Some identification mechanisms adopted by ADC to

  12. Automatic comparison of striation marks and automatic classification of shoe prints

    Science.gov (United States)

    Geradts, Zeno J.; Keijzer, Jan; Keereweer, Isaac

    1995-09-01

    A database for toolmarks (named TRAX) and a database for footwear outsole designs (named REBEZO) have been developed on a PC. The databases are filled with video-images and administrative data about the toolmarks and the footwear designs. An algorithm for the automatic comparison of the digitized striation patterns has been developed for TRAX. The algorithm appears to work well for deep and complete striation marks and will be implemented in TRAX. For REBEZO some efforts have been made to the automatic classification of outsole patterns. The algorithm first segments the shoeprofile. Fourier-features are selected for the separate elements and are classified with a neural network. In future developments information on invariant moments of the shape and rotation angle will be included in the neural network.

  13. Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

    Directory of Open Access Journals (Sweden)

    Francisco Rovira-Más

    2010-12-01

    Full Text Available The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical way.

  14. Research on Fuzzy Control for Automatic Transmission of Tracked Vehicles

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A principle of fuzzy control for tracked vehicles is proposed to make its automatic transmission system be able to adapt complex running conditions, and a model of its power train is established to be used in simulation. Based on the fuzzy control method, a fuzzy shift control system composed of a basic shift strategy and a fuzzy modification module is developed to improve the dynamic characteristics and cross-country maneuverability. Simulation results show that the fuzzy shift strategy can improve the shift quality under manifold driving conditions and avoid cycled shift effectively. Therefore,the proposed fuzzy shift strategies are proved to be feasible and practicable.

  15. Automatic classification of penicillin-induced epileptic EEG spikes.

    Science.gov (United States)

    Kortelainen, Jukka; Silfverhuth, Minna; Suominen, Kalervo; Sonkajarvi, Eila; Alahuhta, Seppo; Jantti, Ville; Seppanen, Tapio

    2010-01-01

    Penicillin-induced focal epilepsy is a well-known model in epilepsy research. In this model, epileptic activity is generated by delivering penicillin focally to the cortex. The drug induces interictal electroencephalographic (EEG) spikes which evolve in time and may later change to ictal discharges. This paper proposes a method for automatic classification of these interictal epileptic spikes using iterative K-means clustering. The method is shown to be able to detect different spike waveforms and describe their characteristic occurrence in time during penicillin-induced focal epilepsy. The study offers potential for future research by providing a method to objectively and quantitatively analyze the time sequence of interictal epileptic activity. PMID:21096740

  16. An Approach for Automatic Classification of Radiology Reports in Spanish.

    Science.gov (United States)

    Cotik, Viviana; Filippo, Darío; Castaño, José

    2015-01-01

    Automatic detection of relevant terms in medical reports is useful for educational purposes and for clinical research. Natural language processing (NLP) techniques can be applied in order to identify them. In this work we present an approach to classify radiology reports written in Spanish into two sets: the ones that indicate pathological findings and the ones that do not. In addition, the entities corresponding to pathological findings are identified in the reports. We use RadLex, a lexicon of English radiology terms, and NLP techniques to identify the occurrence of pathological findings. Reports are classified using a simple algorithm based on the presence of pathological findings, negation and hedge terms. The implemented algorithms were tested with a test set of 248 reports annotated by an expert, obtaining a best result of 0.72 F1 measure. The output of the classification task can be used to look for specific occurrences of pathological findings. PMID:26262128

  17. Automatic music genres classification as a pattern recognition problem

    Science.gov (United States)

    Ul Haq, Ihtisham; Khan, Fauzia; Sharif, Sana; Shaukat, Arsalan

    2013-12-01

    Music genres are the simplest and effect descriptors for searching music libraries stores or catalogues. The paper compares the results of two automatic music genres classification systems implemented by using two different yet simple classifiers (K-Nearest Neighbor and Naïve Bayes). First a 10-12 second sample is selected and features are extracted from it, and then based on those features results of both classifiers are represented in the form of accuracy table and confusion matrix. An experiment carried out on test 60 taken from middle of a song represents the true essence of its genre as compared to the samples taken from beginning and ending of a song. The novel techniques have achieved an accuracy of 91% and 78% by using Naïve Bayes and KNN classifiers respectively.

  18. A multimodal temporal panorama approach for moving vehicle detection, reconstruction, and classification

    Science.gov (United States)

    Wang, Tao; Zhu, Zhigang

    2012-06-01

    Moving vehicle detection and classification using multimodal data is a challenging task in data collection, audio-visual alignment, data labeling and feature selection under uncontrolled environments with occlusions, motion blurs, varying image resolutions and perspective distortions. In this work, we propose an effective multimodal temporal panorama approach for the task using a novel long-range audio-visual sensing system. A new audio-visual vehicle (AVV) dataset for moving vehicle detection and classification is created, which features automatic vehicle detection and audio-visual alignment, accurate vehicle extraction and reconstruction, and efficient data labeling. In particular, vehicles' visual images are reconstructed once detected in order to remove most of the occlusions, motion blurs, and variations of perspective views. Multimodal audio-visual features are extracted, including global geometric features (aspect ratios, profiles), local structure features (HOGs), as well various audio features (MFCCs, etc). Using radial-based SVMs, the effectiveness of the integration of these multimodal features is thoroughly and systemically studied. The concept of MTP may not be only limited to visual, motion and audio modalities; it could also be applicable to other sensing modalities that can obtain data in the temporal domain.

  19. Exploring Sound Signature for Vehicle Detection and Classification Using ANN

    Directory of Open Access Journals (Sweden)

    Jobin George

    2013-06-01

    Full Text Available This paper attempts to explore the possibility of using sound signatures for vehicle detection andclassification purposes. Sound emitted by vehicles are captured for a two lane undivided road carryingmoderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound ofhorns, random but identifiable back ground noises, continuous high energy noises on the back ground arethe different challenges encountered in the data collection. Different features were explored out of whichsmoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Mel-frequency ceptral coefficients extracted from fixed regions around the detected peaks along with themanual vehicle labels are utilised to train an Artificial Neural Network (ANN. The classifier for fourbroad classes heavy, medium, light and horns was trained. The ANN classifier developed was able topredict categories well.

  20. Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

    Science.gov (United States)

    Vasuki, Perumal; Roomi, S. Mohamed Mansoor

    2013-01-01

    Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

  1. Deep transfer learning for automatic target classification: MWIR to LWIR

    Science.gov (United States)

    Ding, Zhengming; Nasrabadi, Nasser; Fu, Yun

    2016-05-01

    Publisher's Note: This paper, originally published on 5/12/2016, was replaced with a corrected/revised version on 5/18/2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. When dealing with sparse or no labeled data in the target domain, transfer learning shows its appealing performance by borrowing the supervised knowledge from external domains. Recently deep structure learning has been exploited in transfer learning due to its attractive power in extracting effective knowledge through multi-layer strategy, so that deep transfer learning is promising to address the cross-domain mismatch. In general, cross-domain disparity can be resulted from the difference between source and target distributions or different modalities, e.g., Midwave IR (MWIR) and Longwave IR (LWIR). In this paper, we propose a Weighted Deep Transfer Learning framework for automatic target classification through a task-driven fashion. Specifically, deep features and classifier parameters are obtained simultaneously for optimal classification performance. In this way, the proposed deep structures can extract more effective features with the guidance of the classifier performance; on the other hand, the classifier performance is further improved since it is optimized on more discriminative features. Furthermore, we build a weighted scheme to couple source and target output by assigning pseudo labels to target data, therefore we can transfer knowledge from source (i.e., MWIR) to target (i.e., LWIR). Experimental results on real databases demonstrate the superiority of the proposed algorithm by comparing with others.

  2. Automatic segmentation and classification of multiple sclerosis in multichannel MRI.

    Science.gov (United States)

    Akselrod-Ballin, Ayelet; Galun, Meirav; Gomori, John Moshe; Filippi, Massimo; Valsasina, Paola; Basri, Ronen; Brandt, Achi

    2009-10-01

    We introduce a multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are then fed into a decision forest classifier, trained with data labeled by experts, enabling the detection of lesions at all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments on two types of real MR images: a multichannel proton-density-, T2-, and T1-weighted dataset of 25 MS patients and a single-channel fluid attenuated inversion recovery (FLAIR) dataset of 16 MS patients. Comparing our results with lesion delineation by a human expert and with previously extensively validated results shows the promise of the approach. PMID:19758850

  3. Neural Network based Vehicle Classification for Intelligent Traffic Control

    Directory of Open Access Journals (Sweden)

    Saeid Fazli

    2012-06-01

    Full Text Available Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve controlling and urban management and increase confidence index in roads and highways. The goal of thisarticle is vehicles classification base on neural networks. In this research, it has been used a immovable camera which is located in nearly close height of the road surface to detect and classify the vehicles. The algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic situations by using some techniques included image processing and remove background of the images and performing edge detection and morphology operations. In the second phase, vehicles near the camera areselected and the specific features are processed and extracted. These features apply to the neural networks as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the algorithm and its highly functional level.

  4. OPTIMAL CONTROL APPLIED IN AUTOMATIC CLUTCH ENGAGEMENTS OF VEHICLES

    Institute of Scientific and Technical Information of China (English)

    Sun Chengshun; Zhang Jianwu

    2004-01-01

    Start-up working condition is the key to the research of optimal engagement of automatic clutch for AMT.In order to guarantee an ideal dynamic performance of the clutch engagement,an optimal controller is designed by considering throttle angle,engine speed,gear ratio,vehicle acceleration and road condition.The minimum value principle is also introduced to achieve an optimal dynamic performance of the nonlinear system compromised in friction plate wear and vehicle drive quality.The optimal trajectory of the clutch engagement can be described in the form of explicit and analytical expressions and characterized by the deterministic and accurate control strategy in stead of indeterministic and soft control techniques which need thousands of experiments.For validation of the controller,test work is carried out for the automated clutch engagements in a commercial car with an traditional mechanical transmission,a hydraulic actuator,a group of sensors and a portable computer system.It is shown through experiments that dynamic behaviors of the clutch engagement operated by the optimal control are more effective and efficient than those by fuzzy control.

  5. Automatic classification of protein structures relying on similarities between alignments

    Directory of Open Access Journals (Sweden)

    Santini Guillaume

    2012-09-01

    Full Text Available Abstract Background Identification of protein structural cores requires isolation of sets of proteins all sharing a same subset of structural motifs. In the context of an ever growing number of available 3D protein structures, standard and automatic clustering algorithms require adaptations so as to allow for efficient identification of such sets of proteins. Results When considering a pair of 3D structures, they are stated as similar or not according to the local similarities of their matching substructures in a structural alignment. This binary relation can be represented in a graph of similarities where a node represents a 3D protein structure and an edge states that two 3D protein structures are similar. Therefore, classifying proteins into structural families can be viewed as a graph clustering task. Unfortunately, because such a graph encodes only pairwise similarity information, clustering algorithms may include in the same cluster a subset of 3D structures that do not share a common substructure. In order to overcome this drawback we first define a ternary similarity on a triple of 3D structures as a constraint to be satisfied by the graph of similarities. Such a ternary constraint takes into account similarities between pairwise alignments, so as to ensure that the three involved protein structures do have some common substructure. We propose hereunder a modification algorithm that eliminates edges from the original graph of similarities and gives a reduced graph in which no ternary constraints are violated. Our approach is then first to build a graph of similarities, then to reduce the graph according to the modification algorithm, and finally to apply to the reduced graph a standard graph clustering algorithm. Such method was used for classifying ASTRAL-40 non-redundant protein domains, identifying significant pairwise similarities with Yakusa, a program devised for rapid 3D structure alignments. Conclusions We show that filtering

  6. Towards Automatic Trunk Classification on Young Conifers

    DEFF Research Database (Denmark)

    Petri, Stig; Immerkær, John

    2009-01-01

    In the garden nursery industry providing young Nordmann firs for Christmas tree plantations, there is a rising interest in automatic classification of their products to ensure consistently high quality and reduce the cost of manual labor. This paper describes a fully automatic single-view algorit...... performance of the algorithm by incorporating color information into the data considered by the dynamic programming algorithm....

  7. Shift Control System of Heavy-duty Vehicle Automatic Transmission

    Directory of Open Access Journals (Sweden)

    Yan Zhang

    2013-12-01

    Full Text Available Heavy-duty vehicle hydrodynamic mechanical automatic transmission shifting operation system was designed, mathematical model of its simplified hydraulic system was established and simulation model of shifting operation system was established with AMESim, the simulation experiment was carried out, then oil pressure curves of each clutch hydraulic cylinder were obtained when giving forward gear or reverse gear signals. The simulation results show that shifting operating system meets the design requirements, and verify the correctness of the model. The shift timing is correct, and there is no power interruption or gear overlap during the shift transition process. Joint oil pressure of designed system is stable, and shifting shock is small. The research results are providing the basis for further study of shifting operation system and a reasonable platform for the studying of shift schedule and quality. The theoretical design method and dynamic simulation experiment will be feasible for the real industrial applications. The research results can be used in design and optimization of hydraulic system

  8. Measuring Service Reliability Using Automatic Vehicle Location Data

    Directory of Open Access Journals (Sweden)

    Zhenliang Ma

    2014-01-01

    Full Text Available Bus service reliability has become a major concern for both operators and passengers. Buffer time measures are believed to be appropriate to approximate passengers' experienced reliability in the context of departure planning. Two issues with regard to buffer time estimation are addressed, namely, performance disaggregation and capturing passengers’ perspectives on reliability. A Gaussian mixture models based method is applied to disaggregate the performance data. Based on the mixture models distribution, a reliability buffer time (RBT measure is proposed from passengers’ perspective. A set of expected reliability buffer time measures is developed for operators by using different spatial-temporal levels combinations of RBTs. The average and the latest trip duration measures are proposed for passengers that can be used to choose a service mode and determine the departure time. Using empirical data from the automatic vehicle location system in Brisbane, Australia, the existence of mixture service states is verified and the advantage of mixture distribution model in fitting travel time profile is demonstrated. Numerical experiments validate that the proposed reliability measure is capable of quantifying service reliability consistently, while the conventional ones may provide inconsistent results. Potential applications for operators and passengers are also illustrated, including reliability improvement and trip planning.

  9. Gaussian process classification using automatic relevance determination for SAR target recognition

    Science.gov (United States)

    Zhang, Xiangrong; Gou, Limin; Hou, Biao; Jiao, Licheng

    2010-10-01

    In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on Gaussian process (GP) classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target recognition by using GP classification with automatic relevance determination (ARD) function. Compared with k-Nearest Neighbor, Naïve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that our algorithm is self-tuning and has better recognition accuracy as well.

  10. Automatic learning for the classification of chemical reactions and in statistical thermodynamics

    OpenAIRE

    Latino, Diogo Alexandre Rosa Serra

    2008-01-01

    This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of ...

  11. Analysis of vehicle and driving condition influences on road classification from vehicle signals

    NARCIS (Netherlands)

    Jansen, S.T.H.; Schmeitz, R.M.T.; Wouters, A.P.; Teerhuis, A.P.

    2014-01-01

    Suspension settings generally are a compromise for performance on different road roughness and stability requirements. With a priori information of the road roughness a superior performance can be achieved, and this information can be obtained from vehicle based road classification methods that use

  12. Fault Diagnosis and Classification in Urban Rail Vehicle Auxiliary Inverter Based on Wavelet Packet and Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Dechen Yao

    2013-01-01

    Full Text Available In this paper we present a novel method in fault recognition and classification in urban rail vehicle auxiliary inverter based on wavelet packet and Elman neural network. First, the original fault voltage signals are decomposed by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are used as Elman neural network input parameters to realize intelligent fault diagnosis. The result shows that the Elman neural network is better than BP neural network, it is effective to distinguish the state of the urban rail vehicle auxiliary inverter.

  13. Automatic classification of protein structures using physicochemical parameters.

    Science.gov (United States)

    Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

    2014-09-01

    Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

  14. Towards an automatic classification of protein structural domains based on structural similarity

    Directory of Open Access Journals (Sweden)

    Gibrat Jean-Francois

    2008-01-01

    Full Text Available Abstract Background Formal classification of a large collection of protein structures aids the understanding of evolutionary relationships among them. Classifications involving manual steps, such as SCOP and CATH, face the challenge of increasing volume of available structures. Automatic methods such as FSSP or Dali Domain Dictionary, yield divergent classifications, for reasons not yet fully investigated. One possible reason is that the pairwise similarity scores used in automatic classification do not adequately reflect the judgments made in manual classification. Another possibility is the difference between manual and automatic classification procedures. We explore the degree to which these two factors might affect the final classification. Results We use DALI, SHEBA and VAST pairwise scores on the SCOP C class domains, to investigate a variety of hierarchical clustering procedures. The constructed dendrogram is cut in a variety of ways to produce a partition, which is compared to the SCOP fold classification. Ward's method dendrograms led to partitions closest to the SCOP fold classification. Dendrogram- or tree-cutting strategies fell into four categories according to the similarity of resulting partitions to the SCOP fold partition. Two strategies which optimize similarity to SCOP, gave an average of 72% true positives rate (TPR, at a 1% false positive rate. Cutting the largest size cluster at each step gave an average of 61% TPR which was one of the best strategies not making use of prior knowledge of SCOP. Cutting the longest branch at each step produced one of the worst strategies. We also developed a method to detect irreducible differences between the best possible automatic partitions and SCOP, regardless of the cutting strategy. These differences are substantial. Visual examination of hard-to-classify proteins confirms our previous finding, that global structural similarity of domains is not the only criterion used in the SCOP

  15. Automatic Vehicle Detection during Nighttime Using Bright Pixel Segment with Spatial Temporal Technique

    Directory of Open Access Journals (Sweden)

    S.Nandhini

    2012-06-01

    Full Text Available The paper proposes an effective Traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic. It identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis technique. By preprocessing noise is removed using median filter. Morphological operation is used to extract candidate headlight objects and then perform shape analysis. Template matching or pattern classification to find the paired headlight of moving vehicles. Salient points are used to represent local properties of image classification. It carries information about image content. Gabor derivation is used for edge detection and feature extraction.

  16. A REVIEW OF COMPUTER VISION SYSTEM FOR THE VEHICLE IDENTIFICATION AND CLASSIFICATION FROM ONLINE AND OFFLINE VIDEOS

    Directory of Open Access Journals (Sweden)

    Baljit Singh Mokha

    2015-10-01

    Full Text Available The traffic on the roads is increasing day by day. There is dire need of developing an automation system that can effectively manage and control the traffic on roads. The traffic data of multiple vehicle types on roads is also important for taking various decisions related to traffic. A video based traffic data collection system for multiple vehicle types is helpful for monitoring vehicles under homogenous and heterogeneous traffic conditions. In this paper, we have studied different methods for the identification, classification and counting vehicles from online and offline videos in India as well as other countries. The paper also discusses the various applications of video based automatic traffic control system. The various challenges faced by the researchers for developing such systems are also discussed.

  17. Automatic classification of trees from laser scanning point clouds

    NARCIS (Netherlands)

    Sirmacek, B.; Lindenbergh, R.C.

    2015-01-01

    Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatical

  18. Automatic structure classification of small proteins using random forest

    Directory of Open Access Journals (Sweden)

    Hirst Jonathan D

    2010-07-01

    Full Text Available Abstract Background Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs. An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs. Results Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP Class, Fold, Super-family or Family levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases. Conclusions The utility of random forest in classifying domains from the place-holder classes of SCOP to the true Class, Fold, Super-family or Family levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.

  19. Automatic subject classification of textual documents using limited or no training data

    OpenAIRE

    Joorabchi, Arash

    2010-01-01

    With the explosive growth in the number of electronic documents available on the internet, intranets, and digital libraries, there is a growing need for automatic systems capable of indexing and organising such large volumes of data more that ever. Automatic Text Classification (ATC) has become one of the principal means for enhancing the performance of information retrieval systems and organising digital libraries and other textual collections. Within this context, the use of ...

  20. A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

    Directory of Open Access Journals (Sweden)

    Dong Jiang

    Full Text Available Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1 images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization with convenience.

  1. Unattended vehicle detection for automatic traffic light control

    Science.gov (United States)

    Abdel Hady, Aya Salama; Moustafa, Mohamed

    2013-12-01

    Machine vision based traffic light control depends mainly on measuring traffic statistics at cross roads. Most of the previous studies have not taken unattended vehicles into consideration when calculating either the traffic density or the traffic flow. In this paper, we propose incorporating unattended vehicles into a new metric for measuring the traffic congestion. In addition to the vehicle motion analysis, opening the driver's side door is an important indicator that this vehicle is going to be unattended. Therefore, we focus in this paper on presenting how to detect this event for stationary vehicles from a live camera or a video feed. Through a set of experiments, we have found out that a Scale Invariant Feature Transform (SIFT) feature-descriptor with a Support Vector Machines (SVM) classifier was able to successfully classify open-door vehicles from closed-door ones in 96.7% of our test dataset.

  2. Automatic classification of trees from laser scanning point clouds

    OpenAIRE

    Sirmacek, B.; R. Lindenbergh

    2015-01-01

    Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into ’tree’ and ’non-tree’ classes. Our method uses the 3D coordinates of the laser scanning points as input and generates a new point cloud whic...

  3. Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

    Science.gov (United States)

    Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

    2016-09-01

    It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image

  4. Application of Machine Vision to Vehicle Automatic Collision Warning Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jiang-feng; GAO Feng; XU Guo-yan; YAO Sheng-zhuo

    2008-01-01

    Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system(CWS) can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver's driving behavior, algorithm's warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm(CWA) model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.

  5. Automatic Classification of Heartbeats Using Wavelet Neural Network

    OpenAIRE

    BENALI, Radhwane; Bereksi-Reguig, Fethi; HADJ SLIMANE, Zinedine

    2012-01-01

    The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WN...

  6. Automatic body flexibility classification using laser doppler flowmeter

    Science.gov (United States)

    Lien, I.-Chan; Li, Yung-Hui; Bau, Jian-Guo

    2015-10-01

    Body flexibility is an important indicator that can measure whether an individual is healthy or not. Traditionally, we need to prepare a protractor and the subject need to perform a pre-defined set of actions. The measurement takes place at the same time when the subject performs required action, which is clumsy and inconvenient. In this paper, we propose a statistical learning model using the technique of random forest. The proposed system can classify body flexibility based on LDF signals analyzed in the frequency domain. The reasons of using random forest are because of their efficiency (fast in classification), interpretable structures and their ability to filter out irrelevant features. In addition, using random forest can prevent the problem of over-fitting, and the output model will become more robust to noises. In our experiment, we use chirp Z-transform (CZT), to transform a LDF signal into its energy values in five frequency bands. Combining the power of the random forest algorithm and frequency band analysis methods, a maximum recognition rate of 66% is achieved. Compared to traditional flexibility measuring process, the proposed system shortens the long and tedious stages of measurement to a simple, fast and pre-defined activity set. The major contributions of our work include (1) a novel body flexibility classification scheme using non-invasive biomedical sensor; (2) a set of designed protocol which is easy to conduct and practice; (3) a high precision classification scheme which combines the power of spectrum analysis and machine learning algorithms.

  7. Search and Classification Using Multiple Autonomous Vehicles Decision-Making and Sensor Management

    CERN Document Server

    Wang, Yue

    2012-01-01

    Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-mak...

  8. Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

    Science.gov (United States)

    Gibbons, Steven J.; Kværna, Tormod; Harris, David B.; Dodge, Douglas A.

    2016-04-01

    Aftershock sequences following very large earthquakes present enormous challenges to near-realtime generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase association algorithms and a significant deterioration in the quality of underlying fully automatic event bulletins. Current processing pipelines were designed a generation ago and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams which are then scanned by a phase association algorithm to form event hypotheses. We consider the scenario where a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located using a separate specially targeted semi-automatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid search algorithm which may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove over half of the original detections which could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Further reductions in the number of detections in the parametric data streams are likely using correlation and subspace detectors and/or empirical matched

  9. Vehicle classification in WAMI imagery using deep network

    Science.gov (United States)

    Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

    2016-05-01

    Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our

  10. Construction of Cubic Dynamic and User-oriented Taxonomy forAutomatic Classification of Internet Information

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Based on the requirements of the development of Internet, thenecessity of establishing cubic dynamic and user-oriented taxonomy for automatic classification is presented. Then the basic algorithm to construct such taxonomy is discussed. The view is up to date in current world.

  11. Resource Optimization in Automatic web page classification using integrated feature selection and machine learning

    Directory of Open Access Journals (Sweden)

    Rajaram Ramasamy

    2009-01-01

    Full Text Available Increasing with the number of users, the need for automatic classification techniques with good classification accuracy increases as search engines depend on previously classified web pages stored in classified directories to retrieve the relevant results. Preprocessing is the important step in web page classification problem as most of the web pages contain more irrelevant information than relevant details useful for finding its category. For the classification purpose the representative words in the web page called as features are used for classification rather than the entire web page to reduce time and space requirements. The selection of relevant features which reduces the high dimensionality and redundancy is the current research topic. In this paper selecting relevant features from the pages is treated as an optimization problem and we propose an algorithm to find the optimal features for web page classification. Machine learning techniques for automatic classification gains more interest as the classifier improves its performance with experience. In this paper we use Naïve Bayes, Kstar, Random Forest and Bagging machine learning classifiers.

  12. Automatic Fault Classification of Rolling Element Bearing using Wavelet Packet Decomposition and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Manish Yadav

    2011-09-01

    Full Text Available In this work an automatic fault classification system is developed for bearing fault classification of three phase induction motor. The system uses the wavelet packet decomposition using ‘db8’ motherwavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The selection of best node of wavelet packet tree is performed by using best tree algorithmalong with minimum Shannon entropy criteria. The ten statistical features such as peak value, root mean square value (RMS, kurtosis, skewness etc. are extracted from the wavelet packet coefficient of optimal node. The extracted feature then was used to train and test neural network fault classification. The artificial neural network system was designed to classify the rolling element bearing condition: healthy bearing (HB rolling element fault (REF, inner race fault (IRF and Outer race fault (ORF for fault classification. The over all fault classification rate is 98.33% of the artificial neural network fault classifier.

  13. Poster Abstract: Automatic Calibration of Device Attitude in Inertial Measurement Unit Based Traffic Probe Vehicles

    KAUST Repository

    Mousa, Mustafa

    2016-04-28

    Probe vehicles consist in mobile traffic sensor networks that evolve with the flow of vehicles, transmitting velocity and position measurements along their path, generated using GPSs. To address the urban positioning issues of GPSs, we propose to replace them with inertial measurement units onboard vehicles, to estimate vehicle location and attitude using inertial data only. While promising, this technology requires one to carefully calibrate the orientation of the device inside the vehicle to be able to process the acceleration and rate gyro data. In this article, we propose a scheme that can perform this calibration automatically by leveraging the kinematic constraints of ground vehicles, and that can be implemented on low-end computational platforms. Preliminary testing shows that the proposed scheme enables one to accurately estimate the actual accelerations and rotation rates in the vehicle coordinates. © 2016 IEEE.

  14. Automatic Fault Characterization via Abnormality-Enhanced Classification

    Energy Technology Data Exchange (ETDEWEB)

    Bronevetsky, G; Laguna, I; de Supinski, B R

    2010-12-20

    Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help to identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.

  15. A Wireless Framework for Lecturers' Attendance System with Automatic Vehicle Identification (AVI Technology

    Directory of Open Access Journals (Sweden)

    Emammer Khamis Shafter

    2015-10-01

    Full Text Available Automatic Vehicle Identification (AVI technology is one type of Radio Frequency Identification (RFID method which can be used to significantly improve the efficiency of lecturers' attendance system. It provides the capability of automatic data capture for attendance records using mobile device equipped in users’ vehicle. The intent of this article is to propose a framework for automatic lecturers' attendance system using AVI technology. The first objective of this work involves gathering of requirements for Automatic Lecturers' Attendance System and to represent them using UML diagrams. The second objective is to put forward a framework that will provide guidelines for developing the system. A prototype has also been created as a pilot project.

  16. Building a robust vehicle detection and classification module

    Science.gov (United States)

    Grigoryev, Anton; Khanipov, Timur; Koptelov, Ivan; Bocharov, Dmitry; Postnikov, Vassily; Nikolaev, Dmitry

    2015-12-01

    The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.

  17. Ipsilateral coordination features for automatic classification of Parkinson's disease

    Science.gov (United States)

    Sarmiento, Fernanda; Atehortúa, Angélica; Martínez, Fabio; Romero, Eduardo

    2015-12-01

    A reliable diagnosis of the Parkinson Disease lies on the objective evaluation of different motor sub-systems. Discovering specific motor patterns associated to the disease is fundamental for the development of unbiased assessments that facilitate the disease characterization, independently of the particular examiner. This paper proposes a new objective screening of patients with Parkinson, an approach that optimally combines ipsilateral global descriptors. These ipsilateral gait features are simple upper-lower limb relationships in frequency and relative phase spaces. These low level characteristics feed a simple SVM classifier with a polynomial kernel function. The strategy was assessed in a binary classification task, normal against Parkinson, under a leave-one-out scheme in a population of 16 Parkinson patients and 7 healthy control subjects. Results showed an accuracy of 94;6% using relative phase spaces and 82;1% with simple frequency relations.

  18. AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

    Directory of Open Access Journals (Sweden)

    Hadi Hamad

    2014-06-01

    Full Text Available The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step, is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or crossovers depending on their geometrical and topological properties such as width, direction and connectivity of the surrounding segments. The proposed approach is applied to the public-domain DRIVE and STARE datasets and compared with the state-of-the-art methods using proper validation parameters. The method was successful in identifying the majority of the landmarks; the average correctly identified bifurcations in both DRIVE and STARE datasets for the recall and precision values are: 95.4% and 87.1% respectively; also for the crossovers, the recall and precision values are: 87.6% and 90.5% respectively; thus outperforming other studies.

  19. Automatic vehicle parking using an evolution-obtained neural controller

    OpenAIRE

    Ronchetti, Franco; Lanzarini, Laura Cristina

    2011-01-01

    Within the problems that can be solved with autonomous robots, automatic parking is an area of great interest, since it presents a complex scenario where the agent must go through a series of obstacles to reach its goal. Existing solutions usually require some kind of external mark for monitoring or global vision that indicates where the agent is at a given time. This article presents an evolutionary strategy to generate a robotic controller based on a neural network that successfully solves ...

  20. Automatic classification of retinal vessels into arteries and veins

    Science.gov (United States)

    Niemeijer, Meindert; van Ginneken, Bram; Abràmoff, Michael D.

    2009-02-01

    Separating the retinal vascular tree into arteries and veins is important for quantifying vessel changes that preferentially affect either the veins or the arteries. For example the ratio of arterial to venous diameter, the retinal a/v ratio, is well established to be predictive of stroke and other cardiovascular events in adults, as well as the staging of retinopathy of prematurity in premature infants. This work presents a supervised, automatic method that can determine whether a vessel is an artery or a vein based on intensity and derivative information. After thinning of the vessel segmentation, vessel crossing and bifurcation points are removed leaving a set of vessel segments containing centerline pixels. A set of features is extracted from each centerline pixel and using these each is assigned a soft label indicating the likelihood that it is part of a vein. As all centerline pixels in a connected segment should be the same type we average the soft labels and assign this average label to each centerline pixel in the segment. We train and test the algorithm using the data (40 color fundus photographs) from the DRIVE database1 with an enhanced reference standard. In the enhanced reference standard a fellowship trained retinal specialist (MDA) labeled all vessels for which it was possible to visually determine whether it was a vein or an artery. After applying the proposed method to the 20 images of the DRIVE test set we obtained an area under the receiver operator characteristic (ROC) curve of 0.88 for correctly assigning centerline pixels to either the vein or artery classes.

  1. Study on shift schedule saving energy of automatic transmission of ground vehicles

    Institute of Scientific and Technical Information of China (English)

    龚捷; 赵丁选; 陈鹰; 陈宁

    2004-01-01

    To improve ground vehicle efficiency,shift schedule energy saving was proposed for the ground vehicle automatic transmission by studying the function of the torque converter and transmission in the vehicular drivetrain.The shift schedule can keep the torque converter working in the high efficiency range under all the working conditions except in the low efficiency range on the left when the transmission worked at the lowest shift,and in the low efficiency range on the right when the transmission worked at the highest shift.The shift quality key factors were analysed.The automatic transmission's bench-test adopting this shift schedule was made on the automatic transmission's test-bed.The experimental results showed that the shift schedule was correct and that the shift quality was controllable.

  2. Study on shift schedule saving energy of automatic transmission of ground vehicles

    Institute of Scientific and Technical Information of China (English)

    龚捷; 赵丁选; 陈鹰; 陈宁

    2004-01-01

    To improve ground vehicle efficiency, shift schedule energy saving was proposed for the ground vehicle automatic transmission by studying the function of the torque converter and transmission in the vehicular drivetrain. The shift schedule can keep the torque converter working in the high efficiency range under all the working conditions except in the low efficiency range on the left when the transmission worked at the lowest shift, and in the low efficiency range on the right when the transmission worked at the highest shift. The shift quality key factors were analysed. The automatic trans-mission's bench-test adopting this shift schedule was made on the automatic transmission's test-bed. The experimental results showed that the shift schedule was correct and that the shift quality was controllable.

  3. A framework for evaluating automatic indexing or classification in the context of retrieval

    DEFF Research Database (Denmark)

    Golub, Korajlka; Soergel, Dagobert; Buchanan, George;

    2016-01-01

    Tools for automatic subject assignment help deal with scale and sustainability in creating and enriching metadata, establishing more connections across and between resources and enhancing consistency. While some software vendors and experimental researchers claim the tools can replace manual...... of the literature on indexing, classification and approaches: evaluating indexing quality directly through assessment by an evaluator or through comparison with a gold standard; evaluating the quality of computer-assisted indexing directly in the context of an indexing workflow, and evaluating indexing quality...

  4. Activity regimes inferred from automatic classification of volcanic tremor at Mt. Etna, Italy

    OpenAIRE

    Masotti, Matteo; Falsaperla, Susanna; Langer, Horst; Spampinato, Salvatore; Campanini, Renato

    2007-01-01

    A renewal of eruptive activity at Mt Etna started from the Southeast Crater on 14 July 2006, about 16 months after the end of the last effusive episode. This new eruption reiterated the importance of continuous volcanic monitoring as well as the need of automatic processing and classification of those signals which might be used to disclose such impending eruptive stages. Among seismic signals, volcanic tremor - the persistent background radiation continuously recorded on open ...

  5. Automatic classification of oranges using image processing and data mining techniques

    OpenAIRE

    Mercol, Juan Pablo; Gambini, María Juliana; Santos, Juan Miguel

    2008-01-01

    Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetables production lines, the quality assurance is done by trained people who inspect the fruits while they move in a conveyor belt, and classify them in several categories based on visual features. In this paper we present an automatic orange’s classification system, which us...

  6. Research on a Chnese Text Automatic Classification Method%一种中文文本自动分类方法的研究

    Institute of Scientific and Technical Information of China (English)

    尹桂秀

    2002-01-01

    This article introduces a Chinese text automatic classification method, including its principle and classification process. The article focuses on some key theoretical problems, such as word classification, keyword collection and keyword matching.

  7. Automatic Classification of coarse density LiDAR data in urban area

    Science.gov (United States)

    Badawy, H. M.; Moussa, A.; El-Sheimy, N.

    2014-06-01

    The classification of different objects in the urban area using airborne LIDAR point clouds is a challenging problem especially with low density data. This problem is even more complicated if RGB information is not available with the point clouds. The aim of this paper is to present a framework for the classification of the low density LIDAR data in urban area with the objective to identify buildings, vehicles, trees and roads, without the use of RGB information. The approach is based on several steps, from the extraction of above the ground objects, classification using PCA, computing the NDSM and intensity analysis, for which a correction strategy was developed. The airborne LIDAR data used to test the research framework are of low density (1.41 pts/m2) and were taken over an urban area in San Diego, California, USA. The results showed that the proposed framework is efficient and robust for the classification of objects.

  8. Internal combustion engine for vehicles with automatic gearbox. Brennkraftmaschine fuer Kraftfahrzeuge mit einem automatischen Getriebe

    Energy Technology Data Exchange (ETDEWEB)

    Hetmann, R.

    1982-04-19

    The invention refers to an internal combustion engine for vehicles with an automatic gearbox, where the internal combustion engine has a first group of cylinders and at least one second group of cylinders, and a device for affecting the fuel supply to the groups of cylinders, depending on the working parameters of the vehicle. The invention is characterised by the fact that the working parameters are the handbrake and footbrake of the vehicle, and that the device for affecting the fuel supply to the groups of cylinders when the footbrake or handbrake is operated makes it possible to supply fuel to only part of the groups of cylinders. The control switches of both braking systems are connected to the fuel supply control via a logic circuit. This arrangement of the system prevents damage when testing the braking speed of the automatic gearbox due to excessive loads.

  9. Automatic Vehicle License Recognition Based on Video Vehicular Detection System

    Institute of Scientific and Technical Information of China (English)

    YANG Zhaoxuan; CHEN Yang; HE Yinghua; WU Jun

    2006-01-01

    Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system.Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented.Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold.The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%.When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination.

  10. Triggering imagery with unattended seismic/magnetic sensing for vehicle classification

    Science.gov (United States)

    Knobler, Ronald A.

    2004-09-01

    Acoustic sensing has traditionally been the preferred method for the detection and classification of ground vehicles. However, environmental conditions such as wind and rain pose a great challenge to prevent false detections and misclassifications. The recent work of McQ System Innovations has demonstrated the ability to successfully detect and classify vehicles with the fusion of seismic and magnetic sensing without false detections and only a small percentage of misclassifications. The algorithms developed were designed to detect single vehicles as well as vehicles in a convoy. Based on the classification of each vehicle, an imager can be triggered to find the best frame of the target, and store the image in onboard memory to send back to an operator display. The methodology of the algorithms designed for seismic/magnetic detection and classification of vehicles is shown, as well as results of testing the algorithms running in a remote sensor.

  11. Controlling and Reducing of Speed for Vehicles Automatically By Using Rf Technology.

    Directory of Open Access Journals (Sweden)

    Y. Ravindra Babu,

    2014-11-01

    Full Text Available For vehicle safety and safety for passengers in vehicle is an important parameter. Most of the vehicles get accident because no proper safety measures are taken especially at curves and hair pin bends humps and any obstacles in front of the vehicle. This system can be used for the prevention of such a problem by indicating a pre indication and also reducing the speed of vehicles by reducing the fuel rate of vehicle. As the action is in terms of fuel rate so the vehicle automatically goes to control and avoids the accidents. At curves and hair pin bends the line of sight is not possible for the drivers so the special kind of transmitter which is tuned at a frequency of 433MHZ are mounted as these transmitters continuously radiate a RF signal for some particular area. As the vehicle come within this radiation the receiver in the vehicle gets activate. The transmitter used here is a coded transmitter which is encoded with encoder. The encoder provides a 4 bit binary data which is serially transmitted to transmitter. The transmitter used here is ASK type (amplitude shift keying which emits the RF radiation.

  12. Key issues in automatic classification of defects in post-inspection review process of photomasks

    Science.gov (United States)

    Pereira, Mark; Maji, Manabendra; Pai, Ravi R.; B. V. R., Samir; Seshadri, R.; Patil, Pradeepkumar

    2012-11-01

    The mask inspection and defect classification is a crucial part of mask preparation technology and consumes a significant amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly precise mask inspection system with high detection sensitivity becomes greater. However, due to the high sensitivity, in addition to the detection of smaller defects on finer geometries, the inspection machine could report large number of false defects. The total number of defects becomes significantly high and the manual classification of these defects, where the operator should review each of the defects and classify them, may take huge amount of time. Apart from false defects, many of the very small real defects may not print on the wafer and user needs to spend time on classifying them as well. Also, sometimes, manual classification done by different operators may not be consistent. So, need for an automatic, consistent and fast classification tool becomes more acute in more advanced nodes. Automatic Defect Classification tool (NxADC) which is in advanced stage of development as part of NxDAT1, can automatically classify defects accurately and consistently in very less amount of time, compared to a human operator. Amongst the prospective defects as detected by the Mask Inspection System, NxADC identifies several types of false defects such as false defects due to registration error, false defects due to problems with CCD, noise, etc. It is also able to automatically classify real defects such as, pin-dot, pin-hole, clear extension, multiple-edges opaque, missing chrome, chrome-over-MoSi, etc. We faced a large set of algorithmic challenges during the course of the development of our NxADC tool. These include selecting the appropriate image alignment algorithm to detect registration errors (especially when there are sub-pixel registration errors or misalignment in repetitive patterns such as line space), differentiating noise from

  13. An atlas-based fuzzy connectedness method for automatic tissue classification in brain MRI

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yongxin; BAI Jing

    2006-01-01

    A framework incorporating a subject-registered atlas into the fuzzy connectedness (FC) method is proposed for the automatic tissue classification of 3D images of brain MRI. The pre-labeled atlas is first registered onto the subject to provide an initial approximate segmentation. The initial segmentation is used to estimate the intensity histograms of gray matter and white matter. Based on the estimated intensity histograms, multiple seed voxels are assigned to each tissue automatically. The normalized intensity histograms are utilized in the FC method as the intensity probability density function (PDF) directly. Relative fuzzy connectedness technique is adopted in the final classification of gray matter and white matter. Experimental results based on the 20 data sets from IBSR are included, as well as comparisons of the performance of our method with that of other published methods. This method is fully automatic and operator-independent. Therefore, it is expected to find wide applications, such as 3D visualization, radiation therapy planning, and medical database construction.

  14. Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

    Directory of Open Access Journals (Sweden)

    Dan Stowell

    2014-07-01

    Full Text Available Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and “unsupervised”, meaning it requires no manual data labelling, yet it can improve performance on “supervised” tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique, using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased

  15. Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

    Directory of Open Access Journals (Sweden)

    Wenyu Zhang

    2014-10-01

    Full Text Available Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

  16. Street-side vehicle detection, classification and change detection using mobile laser scanning data

    Science.gov (United States)

    Xiao, Wen; Vallet, Bruno; Schindler, Konrad; Paparoditis, Nicolas

    2016-04-01

    Statistics on street-side car parks, e.g. occupancy rates, parked vehicle types, parking durations, are of great importance for urban planning and policy making. Related studies, e.g. vehicle detection and classification, mostly focus on static images or video. Whereas mobile laser scanning (MLS) systems are increasingly utilized for urban street environment perception due to their direct 3D information acquisition, high accuracy and movability. In this paper, we design a complete system for car park monitoring, including vehicle recognition, localization, classification and change detection, from laser scanning point clouds. The experimental data are acquired by an MLS system using high frequency laser scanner which scans the streets vertically along the system's moving trajectory. The point clouds are firstly classified as ground, building façade, and street objects which are then segmented using state-of-the-art methods. Each segment is treated as an object hypothesis, and its geometric features are extracted. Moreover, a deformable vehicle model is fitted to each object. By fitting an explicit model to the vehicle points, detailed information, such as precise position and orientation, can be obtained. The model parameters are also treated as vehicle features. Together with the geometric features, they are applied to a supervised learning procedure for vehicle or non-vehicle recognition. The classes of detected vehicles are also investigated. Whether vehicles have changed across two datasets acquired at different times is detected to estimate the durations. Here, vehicles are trained pair-wisely. Two same or different vehicles are paired up as training samples. As a result, the vehicle recognition, classification and change detection accuracies are 95.9%, 86.0% and 98.7%, respectively. Vehicle modelling improves not only the recognition rate, but also the localization precision compared to bounding boxes.

  17. Computational text analysis and reading comprehension exam complexity towards automatic text classification

    CERN Document Server

    Liontou, Trisevgeni

    2014-01-01

    This book delineates a range of linguistic features that characterise the reading texts used at the B2 (Independent User) and C1 (Proficient User) levels of the Greek State Certificate of English Language Proficiency exams in order to help define text difficulty per level of competence. In addition, it examines whether specific reader variables influence test takers' perceptions of reading comprehension difficulty. The end product is a Text Classification Profile per level of competence and a formula for automatically estimating text difficulty and assigning levels to texts consistently and re

  18. Automatic training sample selection for a multi-evidence based crop classification approach

    DEFF Research Database (Denmark)

    Chellasamy, Menaka; Ferre, Ty; Greve, Mogens Humlekrog

    three Multi-Layer Perceptron (MLP) neural networks trained separately with spectral, texture and vegetation indices; classification labels were then assigned based on Endorsement Theory. The present study proposes an approach to feed this ensemble classifier with automatically selected training samples....... The available vector data representing crop boundaries with corresponding crop codes are used as a source for training samples. These vector data are created by farmers to support subsidy claims and are, therefore, prone to errors such as mislabeling of crop codes and boundary digitization errors. The proposed...

  19. Using fuzzy logic for automatic control: Case study of a problem of cereals samples classification

    Directory of Open Access Journals (Sweden)

    Lakhoua Najeh Mohamed

    2009-01-01

    Full Text Available The aim of this paper is to present the use of fuzzy logic for automatic control of industrial systems particularly the way to approach a problem of classification. We present a case study of a grading system of cereals that allows us to determine the price of transactions of cereals in Tunisia. Our contribution in this work consists in proposing not only an application of the fuzzy logic on the grading system of cereals but also a methodology enabling the proposing of a new grading system based on the concept of 'Grade' while using the fuzzy logic techniques. .

  20. Automatic detection and classification of obstacles with applications in autonomous mobile robots

    Science.gov (United States)

    Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

    2016-04-01

    Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

  1. Automatically inferred Markov network models for classification of chromosomal band pattern structures.

    Science.gov (United States)

    Granum, E; Thomason, M G

    1990-01-01

    A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard

  2. Automatic classification of delphinids based on the representative frequencies of whistles.

    Science.gov (United States)

    Lin, Tzu-Hao; Chou, Lien-Siang

    2015-08-01

    Classification of odontocete species remains a challenging task for passive acoustic monitoring. Classifiers that have been developed use spectral features extracted from echolocation clicks and whistle contours. Most of these contour-based classifiers require complete contours to reduce measurement errors. Therefore, overlapping contours and partially detected contours in an automatic detection algorithm may increase the bias for contour-based classifiers. In this study, classification was conducted on each recording section without extracting individual contours. The local-max detector was used to extract representative frequencies of delphinid whistles and each section was divided into multiple non-overlapping fragments. Three acoustical parameters were measured from the distribution of representative frequencies in each fragment. By using the statistical features of the acoustical parameters and the percentage of overlapping whistles, correct classification rate of 70.3% was reached for the recordings of seven species (Tursiops truncatus, Delphinus delphis, Delphinus capensis, Peponocephala electra, Grampus griseus, Stenella longirostris longirostris, and Stenella attenuata) archived in MobySound.org. In addition, correct classification rate was not dramatically reduced in various simulated noise conditions. This algorithm can be employed in acoustic observatories to classify different delphinid species and facilitate future studies on the community ecology of odontocetes. PMID:26328716

  3. Heavy automatic guided vehicle contributing to automatic physical distribution; Butsuryu no jidoka no waku wo hirogeta juryobutsu mujin hansosha

    Energy Technology Data Exchange (ETDEWEB)

    Tanaka, H. [Meidensha Corp., Tokyo (Japan)

    1997-06-30

    The high-performance automatic guided vehicle (AGV) for heavy loads was put on the market. The AGV of 20t at maximum carrying capacity, nearly 7.2m in overall length and nearly 1.5m in overall width is the three-wheel magnetic guided vehicle of front wheel steering/driving. The AGV is also equipped with a hydraulic lifter type transfer equipment of 100mm in stroke, and allows the maximum traveling speed as high as 40m/min and continuous operation time as long as 8 hours. Main features of this AGV are as follows: (1) The energy saving platform of a load/dead load ratio as high as 3.5 and a height as low as 420mm including a lifter, (2) The small spin turn function for accurate cargo handling in limited places regardless of the large platform, (3) The all- weather outdoor type platform coated with salt damage resistant paint, maintenance/inspection work possible on the platform, and sealed grease lubrication, and (4) The wireless centralized control system for waiting control at crossings and command control of traveling routes. 7 figs., 3 tabs.

  4. Regenerative braking control strategy in mild hybrid electric vehicles equipped with automatic manual transmission

    Institute of Scientific and Technical Information of China (English)

    QIN Datong; YE Ming; LIU Zhenjun

    2007-01-01

    The actual regenerative braking force of an integrated starter/generator (ISG),which is varied with desired braking deceleration and vehicle speed,is calculated based on an analysis of the required deceleration,maximum braking force of ISG,engine braking force and state of charge (SOC) of battery.Braking force distribution strategies are presented according to the actual regenerative braking force of ISG.To recover the vehicle's kinetic energy maximally,braking shift rules for a mild hybrid electric vehicle (HEV) equipped with automatic manual transmission (AMT) are brought forward and effects of transmission ratios are considered.A test-bed is built up and regenerative braking tests are carried out.The results show that power recovered by the braking shift rules is more than that recovered by the normal braking control rules.

  5. Toward a multi-sensor-based approach to automatic text classification

    Energy Technology Data Exchange (ETDEWEB)

    Dasigi, V.R. [Sacred Heart Univ., Fairfield, CT (United States); Mann, R.C. [Oak Ridge National Lab., TN (United States)

    1995-10-01

    Many automatic text indexing and retrieval methods use a term-document matrix that is automatically derived from the text in question. Latent Semantic Indexing is a method, recently proposed in the Information Retrieval (IR) literature, for approximating a large and sparse term-document matrix with a relatively small number of factors, and is based on a solid mathematical foundation. LSI appears to be quite useful in the problem of text information retrieval, rather than text classification. In this report, we outline a method that attempts to combine the strength of the LSI method with that of neural networks, in addressing the problem of text classification. In doing so, we also indicate ways to improve performance by adding additional {open_quotes}logical sensors{close_quotes} to the neural network, something that is hard to do with the LSI method when employed by itself. The various programs that can be used in testing the system with TIPSTER data set are described. Preliminary results are summarized, but much work remains to be done.

  6. Automatic generation of 3D motifs for classification of protein binding sites

    Directory of Open Access Journals (Sweden)

    Herzyk Pawel

    2007-08-01

    Full Text Available Abstract Background Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands. Results Our new approach was validated by generating automatically 3D patterns for the main adenine based ligands, i.e. AMP, ADP and ATP. Out of the 18 detected patterns, only one, the ADP4 pattern, is not associated with well defined structural patterns. Moreover, most of the patterns could be classified as binding site 3D motifs. Literature research revealed that the ADP4 pattern actually corresponds to structural features which show complex evolutionary links between ligases and transferases. Therefore, all of the generated patterns prove to be meaningful. Each pattern was used to query all PDB proteins which bind either purine based or guanine based ligands, in order to evaluate the classification and annotation properties of the pattern. Overall, our 3D patterns matched 31% of proteins with adenine based ligands and 95.5% of them were classified correctly. Conclusion A new metric has been introduced allowing the classification of proteins according to the similarity of atomic environment of binding sites, and a methodology has been developed to automatically produce 3D patterns from that classification. A study of proteins binding adenine based ligands showed that

  7. Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields.

    Science.gov (United States)

    Fischell, Erin M; Schmidt, Henrik

    2015-12-01

    One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7-9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834-875 (2010)]. PMID:26723332

  8. Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image

    Science.gov (United States)

    Khazendar, Shan; Farren, Jessica; Al-Assam, Hisham; Sayasneh, Ahmed; Du, Hongbo; Bourne, Tom; Jassim, Sabah A.

    2014-05-01

    Ultrasound is an effective multipurpose imaging modality that has been widely used for monitoring and diagnosing early pregnancy events. Technology developments coupled with wide public acceptance has made ultrasound an ideal tool for better understanding and diagnosing of early pregnancy. The first measurable signs of an early pregnancy are the geometric characteristics of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurement involves multiple subjective decisions, in which dimensions are taken in three planes to establish what is known as Mean Sac Diameter (MSD). The manual measurement results in inter- and intra-observer variations, which may lead to difficulties in diagnosis. This paper proposes a fully automated diagnosis solution to accurately identify miscarriage cases in the first trimester of pregnancy based on automatic quantification of the MSD. Our study shows a strong positive correlation between the manual and the automatic MSD estimations. Our experimental results based on a dataset of 68 ultrasound images illustrate the effectiveness of the proposed scheme in identifying early miscarriage cases with classification accuracies comparable with those of domain experts using K nearest neighbor classifier on automatically estimated MSDs.

  9. Automatic Training Site Selection for Agricultural Crop Classification: a Case Study on Karacabey Plain, Turkey

    Science.gov (United States)

    Ozdarici Ok, A.; Akyurek, Z.

    2011-09-01

    This study implements a traditional supervised classification method to an optical image composed of agricultural crops by means of a unique way, selecting the training samples automatically. Panchromatic (1m) and multispectral (4m) Kompsat-2 images (July 2008) of Karacabey Plain (~100km2), located in Marmara region, are used to evaluate the proposed approach. Due to the characteristic of rich, loamy soils combined with reasonable weather conditions, the Karacabey Plain is one of the most valuable agricultural regions of Turkey. Analyses start with applying an image fusion algorithm on the panchromatic and multispectral image. As a result of this process, 1m spatial resolution colour image is produced. In the next step, the four-band fused (1m) image and multispectral (4m) image are orthorectified. Next, the fused image (1m) is segmented using a popular segmentation method, Mean- Shift. The Mean-Shift is originally a method based on kernel density estimation and it shifts each pixel to the mode of clusters. In the segmentation procedure, three parameters must be defined: (i) spatial domain (hs), (ii) range domain (hr), and (iii) minimum region (MR). In this study, in total, 176 parameter combinations (hs, hr, and MR) are tested on a small part of the area (~10km2) to find an optimum segmentation result, and a final parameter combination (hs=18, hr=20, and MR=1000) is determined after evaluating multiple goodness measures. The final segmentation output is then utilized to the classification framework. The classification operation is applied on the four-band multispectral image (4m) to minimize the mixed pixel effect. Before the image classification, each segment is overlaid with the bands of the image fused, and several descriptive statistics of each segment are computed for each band. To select the potential homogeneous regions that are eligible for the selection of training samples, a user-defined threshold is applied. After finding those potential regions, the

  10. Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones.

    Science.gov (United States)

    Reyes, Bersain A; Reljin, Natasa; Kong, Youngsun; Nam, Yunyoung; Ha, Sangho; Chon, Ki H

    2016-09-01

    Correct labeling of breath phases is useful in the automatic analysis of respiratory sounds, where airflow or volume signals are commonly used as temporal reference. However, such signals are not always available. The development of a smartphone-based respiratory sound analysis system has received increased attention. In this study, we propose an optical approach that takes advantage of a smartphone's camera and provides a chest movement signal useful for classification of the breath phases when simultaneously recording tracheal sounds. Spirometer and smartphone-based signals were acquired from N = 13 healthy volunteers breathing at different frequencies, airflow and volume levels. We found that the smartphone-acquired chest movement signal was highly correlated with reference volume (ρ = 0.960 ± 0.025, mean ± SD). A simple linear regression on the chest signal was used to label the breath phases according to the slope between consecutive onsets. 100% accuracy was found for the classification of the analyzed breath phases. We found that the proposed classification scheme can be used to correctly classify breath phases in more challenging breathing patterns, such as those that include non-breath events like swallowing, talking, and coughing, and alternating or irregular breathing. These results show the feasibility of developing a portable and inexpensive phonopneumogram for the analysis of respiratory sounds based on smartphones. PMID:26847825

  11. Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones.

    Science.gov (United States)

    Reyes, Bersain A; Reljin, Natasa; Kong, Youngsun; Nam, Yunyoung; Ha, Sangho; Chon, Ki H

    2016-09-01

    Correct labeling of breath phases is useful in the automatic analysis of respiratory sounds, where airflow or volume signals are commonly used as temporal reference. However, such signals are not always available. The development of a smartphone-based respiratory sound analysis system has received increased attention. In this study, we propose an optical approach that takes advantage of a smartphone's camera and provides a chest movement signal useful for classification of the breath phases when simultaneously recording tracheal sounds. Spirometer and smartphone-based signals were acquired from N = 13 healthy volunteers breathing at different frequencies, airflow and volume levels. We found that the smartphone-acquired chest movement signal was highly correlated with reference volume (ρ = 0.960 ± 0.025, mean ± SD). A simple linear regression on the chest signal was used to label the breath phases according to the slope between consecutive onsets. 100% accuracy was found for the classification of the analyzed breath phases. We found that the proposed classification scheme can be used to correctly classify breath phases in more challenging breathing patterns, such as those that include non-breath events like swallowing, talking, and coughing, and alternating or irregular breathing. These results show the feasibility of developing a portable and inexpensive phonopneumogram for the analysis of respiratory sounds based on smartphones.

  12. Automatic classification of background EEG activity in healthy and sick neonates

    Science.gov (United States)

    Löfhede, Johan; Thordstein, Magnus; Löfgren, Nils; Flisberg, Anders; Rosa-Zurera, Manuel; Kjellmer, Ingemar; Lindecrantz, Kaj

    2010-02-01

    The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.

  13. Neural Fuzzy Techniques In Vehicle Acoustic Signal Classification

    OpenAIRE

    Sampan, Somkiat

    1998-01-01

    Vehicle acoustic signals have long been considered as unwanted traffic noise. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify its type. Circular arrays of microphones were designed and built to detect desired signals and suppress unwanted ones. Circular arrays with multiple rings have an interesting and important property that is constant sidelobe levels. A modified genetic algorithm that can work directly with real numbers is u...

  14. Correlation analysis-based image segmentation approach for automatic agriculture vehicle

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    It is important to segment image correctly to extract guidance information for automatic agriculture vehicle. If we can make the computer know where the crops are, we can extract the guidance line easily. Images were divided into some rectangle small windows, then a pair of 1-D arrays was constructed in each small windows. The correlation coefficients of every small window constructed the features to segment images. The results showed that correlation analysis is a potential approach for processing complex farmland for guidance system, and more correlation analysis methods must be researched.

  15. Automatic classification of endoscopic images for premalignant conditions of the esophagus

    Science.gov (United States)

    Boschetto, Davide; Gambaretto, Gloria; Grisan, Enrico

    2016-03-01

    Barrett's esophagus (BE) is a precancerous complication of gastroesophageal reflux disease in which normal stratified squamous epithelium lining the esophagus is replaced by intestinal metaplastic columnar epithelium. Repeated endoscopies and multiple biopsies are often necessary to establish the presence of intestinal metaplasia. Narrow Band Imaging (NBI) is an imaging technique commonly used with endoscopies that enhances the contrast of vascular pattern on the mucosa. We present a computer-based method for the automatic normal/metaplastic classification of endoscopic NBI images. Superpixel segmentation is used to identify and cluster pixels belonging to uniform regions. From each uniform clustered region of pixels, eight features maximizing differences among normal and metaplastic epithelium are extracted for the classification step. For each superpixel, the three mean intensities of each color channel are firstly selected as features. Three added features are the mean intensities for each superpixel after separately applying to the red-channel image three different morphological filters (top-hat filtering, entropy filtering and range filtering). The last two features require the computation of the Grey-Level Co-Occurrence Matrix (GLCM), and are reflective of the contrast and the homogeneity of each superpixel. The classification step is performed using an ensemble of 50 classification trees, with a 10-fold cross-validation scheme by training the classifier at each step on a random 70% of the images and testing on the remaining 30% of the dataset. Sensitivity and Specificity are respectively of 79.2% and 87.3%, with an overall accuracy of 83.9%.

  16. OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

    Institute of Scientific and Technical Information of China (English)

    GU Yanchun; YIN Chengliang; ZHANG Jianwu

    2007-01-01

    In parallel hybrid electrical vehicle (PHEV) equipped with automatic mechanical transmission (AMT), the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving sinoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

  17. Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors

    KAUST Repository

    Warriach, Ehsan Ullah

    2013-01-01

    This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN-SVM significantly outperforms other algorithms in terms of classification accuracy. We also show that some of these algorithms could run in real time on the prototype system. Copyright © 2013 ACM.

  18. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

    Science.gov (United States)

    Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

    2014-02-15

    Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery.

  19. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

    Science.gov (United States)

    Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

    2014-02-15

    Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. PMID:24473345

  20. Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

    Science.gov (United States)

    Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

    2015-12-01

    This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

  1. A case-comparison study of automatic document classification utilizing both serial and parallel approaches

    International Nuclear Information System (INIS)

    A well-known problem faced by any organization nowadays is the high volume of data that is available and the required process to transform this volume into differential information. In this study, a case-comparison study of automatic document classification (ADC) approach is presented, utilizing both serial and parallel paradigms. The serial approach was implemented by adopting the RapidMiner software tool, which is recognized as the worldleading open-source system for data mining. On the other hand, considering the MapReduce programming model, the Hadoop software environment has been used. The main goal of this case-comparison study is to exploit differences between these two paradigms, especially when large volumes of data such as Web text documents are utilized to build a category database. In the literature, many studies point out that distributed processing in unstructured documents have been yielding efficient results in utilizing Hadoop. Results from our research indicate a threshold to such efficiency

  2. Automatic segmentation and classification of tendon nuclei from IHC stained images

    Science.gov (United States)

    Kuok, Chan-Pang; Wu, Po-Ting; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien

    2015-12-01

    Immunohistochemical (IHC) staining is commonly used for detecting cells in microscopy. It is used for analyzing many types of diseases, e.g. breast cancer. Dispersion problem often exist at cell staining which will affect the accuracy of automatic counting. In this paper, we introduce a new method to overcome this problem. Otsu's thresholding method is first applied to exclude the background, so that only cells with dispersed staining are left at foreground, and then refinement will be applied by local adaptive thresholding method according to the irregularity index of the segmented shape at foreground. The segmentation results are also compared to the refinement results using Otsu's thresholding method. Cell classification based on the shape and color indices obtained from the segmentation result is applied to determine the cell condition into normal, abnormal and suspected abnormal cases.

  3. A case-comparison study of automatic document classification utilizing both serial and parallel approaches

    Science.gov (United States)

    Wilges, B.; Bastos, R. C.; Mateus, G. P.; Dantas, M. A. R.

    2014-10-01

    A well-known problem faced by any organization nowadays is the high volume of data that is available and the required process to transform this volume into differential information. In this study, a case-comparison study of automatic document classification (ADC) approach is presented, utilizing both serial and parallel paradigms. The serial approach was implemented by adopting the RapidMiner software tool, which is recognized as the worldleading open-source system for data mining. On the other hand, considering the MapReduce programming model, the Hadoop software environment has been used. The main goal of this case-comparison study is to exploit differences between these two paradigms, especially when large volumes of data such as Web text documents are utilized to build a category database. In the literature, many studies point out that distributed processing in unstructured documents have been yielding efficient results in utilizing Hadoop. Results from our research indicate a threshold to such efficiency.

  4. Automatic Galaxy Classification via Machine Learning Techniques: Parallelized Rotation/Flipping INvariant Kohonen Maps (PINK)

    Science.gov (United States)

    Polsterer, K. L.; Gieseke, F.; Igel, C.

    2015-09-01

    In the last decades more and more all-sky surveys created an enormous amount of data which is publicly available on the Internet. Crowd-sourcing projects such as Galaxy-Zoo and Radio-Galaxy-Zoo used encouraged users from all over the world to manually conduct various classification tasks. The combination of the pattern-recognition capabilities of thousands of volunteers enabled scientists to finish the data analysis within acceptable time. For up-coming surveys with billions of sources, however, this approach is not feasible anymore. In this work, we present an unsupervised method that can automatically process large amounts of galaxy data and which generates a set of prototypes. This resulting model can be used to both visualize the given galaxy data as well as to classify so far unseen images.

  5. Automatic detection and classification of artifacts in single-channel EEG

    DEFF Research Database (Denmark)

    Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W.;

    2014-01-01

    Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single......-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different...... artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated...

  6. Automatic detection of photoresist residual layer in lithography using a neural classification approach

    KAUST Repository

    Gereige, Issam

    2012-09-01

    Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.

  7. Automatic sleep classification using a data-driven topic model reveals latent sleep states

    DEFF Research Database (Denmark)

    Koch, Henriette; Christensen, Julie Anja Engelhard; Frandsen, Rune;

    2014-01-01

    Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group......Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. New method: To meet the criticism and reveal the latent...... sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1 s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model...

  8. Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use.

    Science.gov (United States)

    Pineau, Joelle; Moghaddam, Athena K; Yuen, Hiu Kim; Archambault, Philippe S; Routhier, François; Michaud, François; Boissy, Patrick

    2014-01-01

    Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user's driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user's PW driving behavior. PMID:27170879

  9. A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG

    Directory of Open Access Journals (Sweden)

    Li Jie

    2015-01-01

    Full Text Available Sleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG. The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using electrooculogram (EOG and electromyography (EMG automatically. We extracted right and left EOG features and EMG feature in time domain, and classified them into strong, weak and none types through calculating self-adaptive threshold. Combination of the time features of EOG and EMG signals, we classified sleep stages into Wake, REM and NREM stages. The time domain features utilized in the method were Integrate Value, variance and energy. The experiment of 30 datasets showed a satisfactory result with the accuracy of 82.93% for Wake, NREM and REM stages classification, and the average accuracy of Wake stage classification was 83.29%, 82.11% for NREM stage and 76.73% for REM stage.

  10. Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE.

    Science.gov (United States)

    Relan, D; MacGillivray, T; Ballerini, L; Trucco, E

    2014-01-01

    It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate. PMID:25569917

  11. Automatic unsupervised classification of all SDSS/DR7 galaxy spectra

    CERN Document Server

    Almeida, J Sanchez; Munoz-Tunon, C; de Vicente, A

    2010-01-01

    Using the 'k-means' cluster analysis algorithm, we carry out an unsupervised classification of all galaxy spectra in the seventh and final Sloan Digital Sky Survey data release (SDSS/DR7). Except for the shift to restframe wavelengths, and the normalization to the g-band flux, no manipulation is applied to the original spectra. The algorithm guarantees that galaxies with similar spectra belong to the same class. We find that 99 % of the galaxies can be assigned to only 17 major classes, with 11 additional minor classes including the remaining 1%. The classification is not unique since many galaxies appear in between classes, however, our rendering of the algorithm overcomes this weakness with a tool to identify borderline galaxies. Each class is characterized by a template spectrum, which is the average of all the spectra of the galaxies in the class. These low noise template spectra vary smoothly and continuously along a sequence labeled from 0 to 27, from the reddest class to the bluest class. Our Automatic...

  12. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    Science.gov (United States)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support

  13. Vehicle Detection and Classification Using Passive Infrared Sensing

    KAUST Repository

    Oudat, Enas

    2015-10-19

    We propose a new sensing device that can simultaneously monitor urban traffic congestion and another phenomenon of interest (flash floods on the present case). This sensing device is based on the combination of an ultrasonic rangefinder with one or multiple remote temperature sensors. We show an implementation of this device, and illustrate its performance in both traffic flow sensing. Field data shows that the sensor can detect vehicles with a 99% accuracy, in addition to estimating their speed and classifying them in function of their length. The same sensor can also monitor urban water levels with an accuracy of less than 2 cm.

  14. A wireless sensor network design and implementation for vehicle detection, classification, and tracking

    Science.gov (United States)

    Aljaafreh, A.; Al Assaf, A.

    2013-05-01

    Vehicle intrusion is considered a significant threat for critical zones specially the militarized zones and therefore vehicles monitoring has a great importance. In this paper a small wireless sensor network for vehicle intrusion monitoring consists of a five inexpensive sensor nodes distributed over a small area and connected with a gateway using star topology has been designed and implemented. The system is able to detect a passage of an intrusive vehicle, classify it either wheeled or tracked, and track the direction of its movement. The approach is based on Vehicle's ground vibrations for detection, vehicle's acoustic signature for classification and the Energy- based target localization for tracking. Detection and classification are implemented by using different algorithms and techniques including Analog to Digital Conversion, Fast Fourier Transformation (FFT) and Neural Network .All of these algorithms and techniques are implemented locally in the sensor node using Microchip dsPIC digital signal controller. Results are sent from the sensor node to the gateway using ZigBee technology and then from the gateway to a web server using GPRS technology.

  15. Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

    Directory of Open Access Journals (Sweden)

    Jixian Zhang

    2014-09-01

    Full Text Available Automatic vehicle extraction from an airborne laser scanning (ALS point cloud is very useful for many applications, such as digital elevation model generation and 3D building reconstruction. In this article, an object-based point cloud analysis (OBPCA method is proposed for vehicle extraction from an ALS point cloud. First, a segmentation-based progressive TIN (triangular irregular network densification is employed to detect the ground points, and the potential vehicle points are detected based on the normalized heights of the non-ground points. Second, 3D connected component analysis is performed to group the potential vehicle points into segments. At last, vehicle segments are detected based on three features, including area, rectangularity and elongatedness. Experiments suggest that the proposed method is capable of achieving higher accuracy than the exiting mean-shift-based method for vehicle extraction from an ALS point cloud. Moreover, the larger the point density is, the higher the achieved accuracy is.

  16. Sensor network based vehicle classification and license plate identification system

    Energy Technology Data Exchange (ETDEWEB)

    Frigo, Janette Rose [Los Alamos National Laboratory; Brennan, Sean M [Los Alamos National Laboratory; Rosten, Edward J [Los Alamos National Laboratory; Raby, Eric Y [Los Alamos National Laboratory; Kulathumani, Vinod K [WEST VIRGINIA UNIV.

    2009-01-01

    Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform. Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.

  17. Improvement in accuracy of defect size measurement by automatic defect classification

    Science.gov (United States)

    Samir, Bhamidipati; Pereira, Mark; Paninjath, Sankaranarayanan; Jeon, Chan-Uk; Chung, Dong-Hoon; Yoon, Gi-Sung; Jung, Hong-Yul

    2015-10-01

    The blank mask defect review process involves detailed analysis of defects observed across a substrate's multiple preparation stages, such as cleaning and resist-coating. The detailed knowledge of these defects plays an important role in the eventual yield obtained by using the blank. Defect knowledge predominantly comprises of details such as the number of defects observed, and their accurate sizes. Mask usability assessment at the start of the preparation process, is crudely based on number of defects. Similarly, defect size gives an idea of eventual wafer defect printability. Furthermore, monitoring defect characteristics, specifically size and shape, aids in obtaining process related information such as cleaning or coating process efficiencies. Blank mask defect review process is largely manual in nature. However, the large number of defects, observed for latest technology nodes with reducing half-pitch sizes; and the associated amount of information, together make the process increasingly inefficient in terms of review time, accuracy and consistency. The usage of additional tools such as CDSEM may be required to further aid the review process resulting in increasing costs. Calibre® MDPAutoClassify™ provides an automated software alternative, in the form of a powerful analysis tool for fast, accurate, consistent and automatic classification of blank defects. Elaborate post-processing algorithms are applied on defect images generated by inspection machines, to extract and report significant defect information such as defect size, affecting defect printability and mask usability. The algorithm's capabilities are challenged by the variety and complexity of defects encountered, in terms of defect nature, size, shape and composition; and the optical phenomena occurring around the defect [1]. This paper mainly focuses on the results from the evaluation of Calibre® MDPAutoClassify™ product. The main objective of this evaluation is to assess the capability of

  18. AGV技术发展综述%Automatic Guided Vehicles System & Its Application

    Institute of Scientific and Technical Information of China (English)

    张正义

    2005-01-01

    @@ 定义 自动导引车系统AGVS(Automatic GuidedVehicles System)是指由自动导引车AGV和地面导引系统组成的、进行物料搬运作业的光机电信息技术一体化的系统.原美国物流协会对AGV的定义是:装备有电磁或光学等自动导引装置,能够沿规定的导引路径行驶,具有安全保护以及各种移载功能的运输车辆.

  19. New fully automatic transmissions for commercial vehicles from Daimler-Benz

    Energy Technology Data Exchange (ETDEWEB)

    Mueller, W.; Kraft, K.F.; Paulsen, L.

    1986-12-01

    To fulfill different demands of commercial vehicle usage, Daimler-Benz has developed a new series of fully automatic transmissions for buses, medium and heavy-duty trucks. Using proven technology, the transmission consists of a hydrodynamic torque converter with a lock up clutch, a retarder on the gearbox input side and power shifted planetary gearsets with different numbers of speeds, designed in a modular form. Compared to the earlier transmission, many improvements have been made. In particular, the powerrating of the retarder has been increased, a new cooling system developed, and a highly adaptive electronic-hydraulic shifting system realized, which enables economic operation with precisely and individually determined shift points. With regard to truck usage this series opens up new possibilities of application. Apart from the general design, the three and four speed transmissions are described in detail. Additional transmissions in this series will be dealt with at a later date.

  20. Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

    Science.gov (United States)

    Boschetto, Davide; Di Claudio, Gianluca; Mirzaei, Hadis; Leong, Rupert; Grisan, Enrico

    2016-03-01

    Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Naive-Bayes quadratic approach and a standard quadratic analysis, all validated with a ten-fold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Naive Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

  1. Clutch fill control of an automatic transmission for heavy-duty vehicle applications

    Science.gov (United States)

    Meng, Fei; Chen, Huiyan; Zhang, Tao; Zhu, Xiaoyuan

    2015-12-01

    In this paper an integrated clutch filling phase control for gearshifts on wet clutch transmissions is developed. In a clutch-to-clutch shift of an automatic transmission, in order to obtain smooth gearshift, it should synchronize the oncoming clutch and the off-going clutch timely as well as precise pressure control for the engagement of the oncoming clutch. However, before the oncoming clutch pressure starts to increase, the initial cavity of the clutch chamber has to be filled first. The filling time and stability of the fill phase are very important for the clutch control. In order to improve the shift quality of the automatic transmission which is equipped on heavy-duty vehicles, the electro-hydraulic clutch actuation system is analysed and modelled. A new fill phase control strategy is proposed based on the system analysis as well as the control parameters are optimized according to the variation of the oil temperature and engine speed. The designed strategy is validated by a simulation work. The results demonstrate that the proposed control strategy and parameters modified method can transit the shift process from the fill phase to the torque phase effectively.

  2. Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm

    Science.gov (United States)

    Provost, Floriane; Hibert, Clément; Malet, Jean-Philippe; Stumpf, André; Doubre, Cécile

    2016-04-01

    Different studies have shown the presence of microseismic activity in soft-rock landslides. The seismic signals exhibit significantly different features in the time and frequency domains which allow their classification and interpretation. Most of the classes could be associated with different mechanisms of deformation occurring within and at the surface (e.g. rockfall, slide-quake, fissure opening, fluid circulation). However, some signals remain not fully understood and some classes contain few examples that prevent any interpretation. To move toward a more complete interpretation of the links between the dynamics of soft-rock landslides and the physical processes controlling their behaviour, a complete catalog of the endogeneous seismicity is needed. We propose a multi-class detection method based on the random forests algorithm to automatically classify the source of seismic signals. Random forests is a supervised machine learning technique that is based on the computation of a large number of decision trees. The multiple decision trees are constructed from training sets including each of the target classes. In the case of seismic signals, these attributes may encompass spectral features but also waveform characteristics, multi-stations observations and other relevant information. The Random Forest classifier is used because it provides state-of-the-art performance when compared with other machine learning techniques (e.g. SVM, Neural Networks) and requires no fine tuning. Furthermore it is relatively fast, robust, easy to parallelize, and inherently suitable for multi-class problems. In this work, we present the first results of the classification method applied to the seismicity recorded at the Super-Sauze landslide between 2013 and 2015. We selected a dozen of seismic signal features that characterize precisely its spectral content (e.g. central frequency, spectrum width, energy in several frequency bands, spectrogram shape, spectrum local and global maxima

  3. Classification Of Road Accidents From The Perspective Of Vehicle Safety Systems

    Directory of Open Access Journals (Sweden)

    Jirovský Václav

    2015-11-01

    Full Text Available Modern road accident investigation and database structures are focused on accident analysis and classification from the point of view of the accident itself. The presented article offers a new approach, which will describe the accident from the point of view of integrated safety vehicle systems. Seven main categories have been defined to specify the level of importance of automated system intervention. One of the proposed categories is a new approach to defining the collision probability of an ego-vehicle with another object. This approach focuses on determining a 2-D reaction space, which describes all possible positions of the vehicle or other moving object in the specified amount of time in the future. This is to be used for defining the probability of the vehicles interacting - when the intersection of two reaction spaces exists, an action has to be taken on the side of ego-vehicle. The currently used 1-D quantity of TTC (time-to-collision can be superseded by the new reaction space variable. Such new quantity, whose basic idea is described in the article, enables the option of counting not only with necessary braking time, but mitigation by changing direction is then easily feasible. Finally, transparent classification measures of a probable accident are proposed. Their application is highly effective not only during basic accident comparison, but also for an on-board safety system.

  4. Automatic Classification of the Vestibulo-Ocular Reflex Nystagmus: Integration of Data Clustering and System Identification.

    Science.gov (United States)

    Ranjbaran, Mina; Smith, Heather L H; Galiana, Henrietta L

    2016-04-01

    The vestibulo-ocular reflex (VOR) plays an important role in our daily activities by enabling us to fixate on objects during head movements. Modeling and identification of the VOR improves our insight into the system behavior and improves diagnosis of various disorders. However, the switching nature of eye movements (nystagmus), including the VOR, makes dynamic analysis challenging. The first step in such analysis is to segment data into its subsystem responses (here slow and fast segment intervals). Misclassification of segments results in biased analysis of the system of interest. Here, we develop a novel three-step algorithm to classify the VOR data into slow and fast intervals automatically. The proposed algorithm is initialized using a K-means clustering method. The initial classification is then refined using system identification approaches and prediction error statistics. The performance of the algorithm is evaluated on simulated and experimental data. It is shown that the new algorithm performance is much improved over the previous methods, in terms of higher specificity. PMID:26357393

  5. Automatic Classification of the Vestibulo-Ocular Reflex Nystagmus: Integration of Data Clustering and System Identification.

    Science.gov (United States)

    Ranjbaran, Mina; Smith, Heather L H; Galiana, Henrietta L

    2016-04-01

    The vestibulo-ocular reflex (VOR) plays an important role in our daily activities by enabling us to fixate on objects during head movements. Modeling and identification of the VOR improves our insight into the system behavior and improves diagnosis of various disorders. However, the switching nature of eye movements (nystagmus), including the VOR, makes dynamic analysis challenging. The first step in such analysis is to segment data into its subsystem responses (here slow and fast segment intervals). Misclassification of segments results in biased analysis of the system of interest. Here, we develop a novel three-step algorithm to classify the VOR data into slow and fast intervals automatically. The proposed algorithm is initialized using a K-means clustering method. The initial classification is then refined using system identification approaches and prediction error statistics. The performance of the algorithm is evaluated on simulated and experimental data. It is shown that the new algorithm performance is much improved over the previous methods, in terms of higher specificity.

  6. Automatic classification of atherosclerotic plaques imaged with intravascular OCT (Conference Presentation)

    Science.gov (United States)

    Rico-Jimenez, Jose D.; Campos-Delgado, Daniel U.; Villiger, Martin; Bouma, Brett; Jo, Javier A.

    2016-03-01

    A novel computational method for plaque tissue characterization based on Intravascular Optical Coherence Tomography (IV-OCT) is presented. IV-OCT is becoming a powerful tool for the clinical evaluation of atherosclerotic plaques; however, it requires a trained expert for visual assessment and interpretation of the imaged plaques. Moreover, due to the inherit effect of speckle and the scattering attenuation of the optical scheme the direct interpretation of OCT images is limited. To overcome these difficulties, we propose to automatically identify the A-line profiles of the most significant plaque types (normal, fibrotic, or lipid-rich) and their respective abundance by using a probabilistic framework and blind alternated least squares to achieve the optimal decomposition. In this context, we present preliminary results of this novel probabilistic classification tool for intravascular OCT that relies on two steps. First, the B-scan is pre-processed to remove catheter artifacts, segment the lumen, select the region of interest (ROI), flatten the tissue surface, and reduce the speckle effect by a spatial entropy filter. Next, the resulting image is decomposed and its A-lines are classified by an automated strategy based on alternating-least-squares optimization. Our early results are encouraging and suggest that the proposed methodology can identify normal tissue, fibrotic and lipid-rich plaques from IV-OCT images.

  7. Progress toward automatic classification of human brown adipose tissue using biomedical imaging

    Science.gov (United States)

    Gifford, Aliya; Towse, Theodore F.; Walker, Ronald C.; Avison, Malcom J.; Welch, E. B.

    2015-03-01

    Brown adipose tissue (BAT) is a small but significant tissue, which may play an important role in obesity and the pathogenesis of metabolic syndrome. Interest in studying BAT in adult humans is increasing, but in order to quantify BAT volume in a single measurement or to detect changes in BAT over the time course of a longitudinal experiment, BAT needs to first be reliably differentiated from surrounding tissue. Although the uptake of the radiotracer 18F-Fluorodeoxyglucose (18F-FDG) in adipose tissue on positron emission tomography (PET) scans following cold exposure is accepted as an indication of BAT, it is not a definitive indicator, and to date there exists no standardized method for segmenting BAT. Consequently, there is a strong need for robust automatic classification of BAT based on properties measured with biomedical imaging. In this study we begin the process of developing an automated segmentation method based on properties obtained from fat-water MRI and PET-CT scans acquired on ten healthy adult subjects.

  8. FOUR-PARAMETER AUTOMATIC TRANSMISSION TECHNOLOGY FOR CONSTRUCTION VEHICLE BASED ON ELMAN RECURSIVE NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hongyan; ZHAO Dingxuan; TANG Xinxing; Ding Chunfeng

    2008-01-01

    From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.

  9. Study on dynamic optimization of the double railway suspended vehicle for automatic transportation in the welding shop

    Institute of Scientific and Technical Information of China (English)

    Huang Dawei; Gao Xiuhua; Xing Hao; Liu Hongxue; Han Yanhe

    2007-01-01

    The 2DOF dynamic equations of the double railway suspended vehicle for automatic transportation in the welding shop are established. The sensitivities are analyzed. The parameter design is researched in ADAMS in terms of the inner railway radius, wheelbase, gauge, girder length of the double railway suspended vehicle for automatic transportation in the welding product line. The mutual-restriction among the design variables is discussed and the selective ranges of the variables are confirmed. The result shows that the stability of the double railway suspended vehicle for automatic transportation in the welding product line depends on parameters of the inner railway radius, wheelbase, gauge, girder length. The optimal results of the optimal objective and design variables have research significance for the virtual prototype of the double suspension railway automation vehicle. The optimal results are input into the simulation model iteratively and the simulation results are fed back to the physical prototype. The veracity and reliability of performance forecast are improved so that the manufacture cost of the double suspension railway automation vehicle is reduced significantly.

  10. Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.

    Science.gov (United States)

    Haleem, Muhammad Salman; Han, Liangxiu; Hemert, Jano van; Fleming, Alan; Pasquale, Louis R; Silva, Paolo S; Song, Brian J; Aiello, Lloyd Paul

    2016-06-01

    Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4 % and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %. PMID:27086033

  11. Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia

    Science.gov (United States)

    Ohrnberger, Matthias

    2001-07-01

    Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test

  12. Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning

    Science.gov (United States)

    Diaz-Hernandez, R.; Ortiz-Esquivel, A.; Peregrina-Barreto, H.; Altamirano-Robles, L.; Gonzalez-Bernal, J.

    2016-06-01

    The observation of celestial objects in the sky is a practice that helps astronomers to understand the way in which the Universe is structured. However, due to the large number of observed objects with modern telescopes, the analysis of these by hand is a difficult task. An important part in galaxy research is the morphological structure classification based on the Hubble sequence. In this research, we present an approach to solve the morphological galaxy classification problem in an automatic way by using the Sparse Representation technique and dictionary learning with K-SVD. For the tests in this work, we use a database of galaxies extracted from the Principal Galaxy Catalog (PGC) and the APM Equatorial Catalogue of Galaxies obtaining a total of 2403 useful galaxies. In order to represent each galaxy frame, we propose to calculate a set of 20 features such as Hu's invariant moments, galaxy nucleus eccentricity, gabor galaxy ratio and some other features commonly used in galaxy classification. A stage of feature relevance analysis was performed using Relief-f in order to determine which are the best parameters for the classification tests using 2, 3, 4, 5, 6 and 7 galaxy classes making signal vectors of different length values with the most important features. For the classification task, we use a 20-random cross-validation technique to evaluate classification accuracy with all signal sets achieving a score of 82.27 % for 2 galaxy classes and up to 44.27 % for 7 galaxy classes.

  13. Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

    Science.gov (United States)

    Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

    2016-03-01

    In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. PMID:26722989

  14. Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

    Science.gov (United States)

    Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

    2016-03-01

    In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.

  15. Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization

    Science.gov (United States)

    Birge, B.

    2013-01-01

    A high fidelity simulation using a PC based Trick framework has been developed for Johnson Space Center's Morpheus test bed flight vehicle. There is an iterative development loop of refining and testing the hardware, refining the software, comparing the software simulation to hardware performance and adjusting either or both the hardware and the simulation to extract the best performance from the hardware as well as the most realistic representation of the hardware from the software. A Particle Swarm Optimization (PSO) based technique has been developed that increases speed and accuracy of the iterative development cycle. Parameters in software can be automatically tuned to make the simulation match real world subsystem data from test flights. Special considerations for scale, linearity, discontinuities, can be all but ignored with this technique, allowing fast turnaround both for simulation tune up to match hardware changes as well as during the test and validation phase to help identify hardware issues. Software models with insufficient control authority to match hardware test data can be immediately identified and using this technique requires very little to no specialized knowledge of optimization, freeing model developers to concentrate on spacecraft engineering. Integration of the PSO into the Morpheus development cycle will be discussed as well as a case study highlighting the tool's effectiveness.

  16. Research of Patent Automatic Classification Based on RBFNN%基于RBFNN的专利自动分类研究

    Institute of Scientific and Technical Information of China (English)

    马芳

    2011-01-01

    为减少人工分类的不确定性和分类错误,将文本分类技术引入专利自动分类系统,采用径向基函数神经网络(RBFNN)算法完成专利文本的训练和分类,并进行相关测试分析。实验结果表明,采用RBFNN分类器在专利文本自动分类中具有较理想的性能,测试平均F1值在70%以上。%In order to reduce the poor consistency and the errors in manual patent classification, this article introduces text classification technology into patent auto -classification system. It uses the radial basis function neural network algo- rithm to realize the automatic classification of patent text, and analyses the test samples. The experiment results show that this new system has a better classification results, and the average F1 value is higher than 70%

  17. Automatic Detection, Segmentation and Classification of Retinal Horizontal Neurons in Large-scale 3D Confocal Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Karakaya, Mahmut [ORNL; Kerekes, Ryan A [ORNL; Gleason, Shaun Scott [ORNL; Martins, Rodrigo [St. Jude Children' s Research Hospital; Dyer, Michael [St. Jude Children' s Research Hospital

    2011-01-01

    Automatic analysis of neuronal structure from wide-field-of-view 3D image stacks of retinal neurons is essential for statistically characterizing neuronal abnormalities that may be causally related to neural malfunctions or may be early indicators for a variety of neuropathies. In this paper, we study classification of neuron fields in large-scale 3D confocal image stacks, a challenging neurobiological problem because of the low spatial resolution imagery and presence of intertwined dendrites from different neurons. We present a fully automated, four-step processing approach for neuron classification with respect to the morphological structure of their dendrites. In our approach, we first localize each individual soma in the image by using morphological operators and active contours. By using each soma position as a seed point, we automatically determine an appropriate threshold to segment dendrites of each neuron. We then use skeletonization and network analysis to generate the morphological structures of segmented dendrites, and shape-based features are extracted from network representations of each neuron to characterize the neuron. Based on qualitative results and quantitative comparisons, we show that we are able to automatically compute relevant features that clearly distinguish between normal and abnormal cases for postnatal day 6 (P6) horizontal neurons.

  18. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

    NARCIS (Netherlands)

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-01-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is

  19. Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

    Directory of Open Access Journals (Sweden)

    Jingfang Liu

    2014-11-01

    Full Text Available User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews.We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure.In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier.By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

  20. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

    Directory of Open Access Journals (Sweden)

    Tangermann Michael

    2011-08-01

    Full Text Available Abstract Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI or for Mental State Monitoring. While hand-optimized selection of source components derived from Independent Component Analysis (ICA to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM. The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT study, n = 12 that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP paradigm, n = 18; motor imagery BCI paradigm, n = 80 that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (10% Mean Squared Error (MSE on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of

  1. Automatic surface classification for retrieving areas which are highly endangered by extreme rain

    Science.gov (United States)

    Fischer, P.; Krauß, T.; Peters, T.

    2014-09-01

    In this case study, an approach for finding regions endangered by extreme rain is presented. The approach is based on the assumption that sinks in the surface are more endangered than their surroundings. The surface data, which are the source for the classification, are generated using a Cartosat stereo scene. The classification is performed by using an algorithm for retrieving the terrain positioning index. Different classification schemes are possible, therefore a set of input parameters is iteratively computed. The classification results are then evaluated. For validating the classification stock data of an insurance are used. We compare the position of the reported damages caused by extreme rain with our classification. By doing so we got the confirmation of the assumption.

  2. Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone.

    Science.gov (United States)

    Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

    2014-10-31

    The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

  3. Automatic Classification of the Sub-Techniques (Gears Used in Cross-Country Ski Skating Employing a Mobile Phone

    Directory of Open Access Journals (Sweden)

    Thomas Stöggl

    2014-10-01

    Full Text Available The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC ski-skating gears (G using Smartphone accelerometer data. Eleven XC skiers (seven men, four women with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01 with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

  4. Back-and-Forth Methodology for Objective Voice Quality Assessment: From/to Expert Knowledge to/from Automatic Classification of Dysphonia

    Directory of Open Access Journals (Sweden)

    Corinne Fredouille

    2009-01-01

    Full Text Available This paper addresses voice disorder assessment. It proposes an original back-and-forth methodology involving an automatic classification system as well as knowledge of the human experts (machine learning experts, phoneticians, and pathologists. The goal of this methodology is to bring a better understanding of acoustic phenomena related to dysphonia. The automatic system was validated on a dysphonic corpus (80 female voices, rated according to the GRBAS perceptual scale by an expert jury. Firstly, focused on the frequency domain, the classification system showed the interest of 0–3000 Hz frequency band for the classification task based on the GRBAS scale. Later, an automatic phonemic analysis underlined the significance of consonants and more surprisingly of unvoiced consonants for the same classification task. Submitted to the human experts, these observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia severity validated by a preliminary statistical analysis.

  5. Back-and-Forth Methodology for Objective Voice Quality Assessment: From/to Expert Knowledge to/from Automatic Classification of Dysphonia

    Science.gov (United States)

    Fredouille, Corinne; Pouchoulin, Gilles; Ghio, Alain; Revis, Joana; Bonastre, Jean-François; Giovanni, Antoine

    2009-12-01

    This paper addresses voice disorder assessment. It proposes an original back-and-forth methodology involving an automatic classification system as well as knowledge of the human experts (machine learning experts, phoneticians, and pathologists). The goal of this methodology is to bring a better understanding of acoustic phenomena related to dysphonia. The automatic system was validated on a dysphonic corpus (80 female voices), rated according to the GRBAS perceptual scale by an expert jury. Firstly, focused on the frequency domain, the classification system showed the interest of 0-3000 Hz frequency band for the classification task based on the GRBAS scale. Later, an automatic phonemic analysis underlined the significance of consonants and more surprisingly of unvoiced consonants for the same classification task. Submitted to the human experts, these observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia severity validated by a preliminary statistical analysis.

  6. An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

    Science.gov (United States)

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2016-01-01

    Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods. PMID:27322421

  7. An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

    Directory of Open Access Journals (Sweden)

    Liansheng Wang

    Full Text Available Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS method improved by fully utilizing three dimensional (3D information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods.

  8. An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images

    Science.gov (United States)

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2016-01-01

    Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods. PMID:27322421

  9. RendezVous sensor for automatic guidance of transfer vehicles to ISS concept of the operational modes depending on actual optical and geometrical-dynamical conditions

    Science.gov (United States)

    Moebius, Bettina G.; Kolk, Karl-Hermann

    2000-10-01

    Based on an ATV RendezVous Predevelopment Program initiated by ESTEC, an automatically operating Rendez Vous Sensor has been developed. The sensor--a Scanning Tele-Goniometer--shall guide docking and retreat of the European Automatic Transfer Vehicle as well as berthing and retreat of the Japanese H-II Transfer Vehicle. The sensor performance will be strongly connected with the properties of cooperative targets, consisting of an arrangement of retro reflectors mounted on ISS each.

  10. Neuronal Spectral Analysis of EEG and Expert Knowledge Integration for Automatic Classification of Sleep Stages

    OpenAIRE

    Kerkeni, Nizar; Alexandre, Frédéric; Bedoui, Mohamed Hédi; Bougrain, Laurent; Dogui, Mohamed

    2005-01-01

    http://www.wseas.org Being able to analyze and interpret signal coming from electroencephalogram (EEG) recording can be of high interest for many applications including medical diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able to extract from this signal many hints related to physiological as well as cognitive states of the recorded subject and it would be very interesting to perform such task automatically but today no completely automatic system exists. In pre...

  11. The Automatic Method of EEG State Classification by Using Self-Organizing Map

    Science.gov (United States)

    Tamura, Kazuhiro; Shimada, Takamasa; Saito, Yoichi

    In psychiatry, the sleep stage is one of the most important evidence for diagnosing mental disease. However, when doctor diagnose the sleep stage, much labor and skill are required, and a quantitative and objective method is required for more accurate diagnosis. For this reason, an automatic diagnosis system must be developed. In this paper, we propose an automatic sleep stage diagnosis method by using Self Organizing Maps (SOM). Neighborhood learning of SOM makes input data which has similar feature output closely. This function is effective to understandable classifying of complex input data automatically. We applied Elman-type feedback SOM to EEG of not only normal subjects but also subjects suffer from disease. The spectrum of characteristic waves in EEG of disease subjects is often different from it of normal subjects. So, it is difficult to classifying EEG of disease subjects with the rule for normal subjects. On the other hand, Elman-type feedback SOM Classifies the EEG with features which data include and classifying rule is made automatically, so even the EEG with disease subjects is able to be classified automatically. And this Elman-type feedback SOM has context units for diagnosing sleep stages considering contextual information of EEG. Experimental results indicate that the proposed method is able to achieve sleep stage judgment along with doctor's diagnosis.

  12. 自动文本分类中的智能处理技术%The Intelligent Treatment Technology in Automatic Text Classification

    Institute of Scientific and Technical Information of China (English)

    孙晋文; 肖建国

    2003-01-01

    Text automatic classification has become an important technology along with development of Internet and the increment of information ,because of the complexity of text ,it is very difficult to achieve better effect only depend-ing on the different classification methods,it need to use multi- ways to resolve. Based on the retrospection of xtclassification,this paper gives a comprehensive ways to enhance the performance of text classification,which will pro-vide good instruction to the application of text classification.

  13. Comparative analysis of different implementations of a parallel algorithm for automatic target detection and classification of hyperspectral images

    Science.gov (United States)

    Paz, Abel; Plaza, Antonio; Plaza, Javier

    2009-08-01

    Automatic target detection in hyperspectral images is a task that has attracted a lot of attention recently. In the last few years, several algoritms have been developed for this purpose, including the well-known RX algorithm for anomaly detection, or the automatic target detection and classification algorithm (ATDCA), which uses an orthogonal subspace projection (OSP) approach to extract a set of spectrally distinct targets automatically from the input hyperspectral data. Depending on the complexity and dimensionality of the analyzed image scene, the target/anomaly detection process may be computationally very expensive, a fact that limits the possibility of utilizing this process in time-critical applications. In this paper, we develop computationally efficient parallel versions of both the RX and ATDCA algorithms for near real-time exploitation of these algorithms. In the case of ATGP, we use several distance metrics in addition to the OSP approach. The parallel versions are quantitatively compared in terms of target detection accuracy, using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center in New York, five days after the terrorist attack of September 11th, 2001, and also in terms of parallel performance, using a massively Beowulf cluster available at NASA's Goddard Space Flight Center in Maryland.

  14. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle

    NARCIS (Netherlands)

    Viazzi, S.; Schlageter Tello, A.A.; Hertem, van T.; Romanini, C.E.B.; Pluk, A.; Halachmi, I.; Lokhorst, C.; Berckmans, D.

    2013-01-01

    Currently, diagnosis of lameness at an early stage in dairy cows relies on visual observation by the farmer, which is time consuming and often omitted. Many studies have tried to develop automatic cow lameness detection systems. However, those studies apply thresholds to the whole population to dete

  15. ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification

    CERN Document Server

    Tramacere, A; Dubath, P; Kneib, J -P; Courbin, F

    2016-01-01

    We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional ones related to segments of spiral arms. We use two different supervised ensemble classification algorithms, Random Forest,...

  16. Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

    Science.gov (United States)

    Hernandez-Contreras, D.; Peregrina-Barreto, H.; Rangel-Magdaleno, J.; Ramirez-Cortes, J.; Renero-Carrillo, F.

    2015-11-01

    This paper presents a novel approach to characterize and identify patterns of temperature in thermographic images of the human foot plant in support of early diagnosis and follow-up of diabetic patients. Composed feature vectors based on 3D morphological pattern spectrum (pecstrum) and relative position, allow the system to quantitatively characterize and discriminate non-diabetic (control) and diabetic (DM) groups. Non-linear classification using neural networks is used for that purpose. A classification rate of 94.33% in average was obtained with the composed feature extraction process proposed in this paper. Performance evaluation and obtained results are presented.

  17. Towards automatic lithological classification from remote sensing data using support vector machines

    Science.gov (United States)

    Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

    2010-05-01

    Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

  18. Investigation of Matlab® as Platform in Navigation and Control of an Automatic Guided Vehicle Utilising an Omnivision Sensor

    Directory of Open Access Journals (Sweden)

    Ben Kotze

    2014-08-01

    Full Text Available Automatic Guided Vehicles (AGVs are navigated utilising multiple types of sensors for detecting the environment. In this investigation such sensors are replaced and/or minimized by the use of a single omnidirectional camera picture stream. An area of interest is extracted, and by using image processing the vehicle is navigated on a set path. Reconfigurability is added to the route layout by signs incorporated in the navigation process. The result is the possible manipulation of a number of AGVs, each on its own designated colour-signed path. This route is reconfigurable by the operator with no programming alteration or intervention. A low resolution camera and a Matlab® software development platform are utilised. The use of Matlab® lends itself to speedy evaluation and implementation of image processing options on the AGV, but its functioning in such an environment needs to be assessed.

  19. Investigation of Matlab® as platform in navigation and control of an Automatic Guided Vehicle utilising an omnivision sensor.

    Science.gov (United States)

    Kotze, Ben; Jordaan, Gerrit

    2014-08-25

    Automatic Guided Vehicles (AGVs) are navigated utilising multiple types of sensors for detecting the environment. In this investigation such sensors are replaced and/or minimized by the use of a single omnidirectional camera picture stream. An area of interest is extracted, and by using image processing the vehicle is navigated on a set path. Reconfigurability is added to the route layout by signs incorporated in the navigation process. The result is the possible manipulation of a number of AGVs, each on its own designated colour-signed path. This route is reconfigurable by the operator with no programming alteration or intervention. A low resolution camera and a Matlab® software development platform are utilised. The use of Matlab® lends itself to speedy evaluation and implementation of image processing options on the AGV, but its functioning in such an environment needs to be assessed.

  20. RendezVous sensor for automatic guidance of transfer vehicles to the International Space Station

    Science.gov (United States)

    Kolk, Karl-Hermann; Moebius, Bettina G.

    2000-10-01

    Based on a Predevelopment Program, initiated by the European Space Agency, an automatically operating RendezVous Sensor (RVS) is currently developed. This paper describes in more detail the RVS concept emphasizing the electro-optical elements of the sensor.

  1. Automatic GPR image classification using a Support Vector Machine Pre-screener with Hidden Markov Model confirmation

    Science.gov (United States)

    Williams, R. M.; Ray, L. E.

    2012-12-01

    This paper presents methods to automatically classify ground penetrating radar (GPR) images of crevasses on ice sheets for use with a completely autonomous robotic system. We use a combination of support vector machines (SVM) and hidden Markov models (HMM) with appropriate un-biased processing that is suitable for real-time analysis and detection. We tested and evaluated three processing schemes on 96 examples of Antarctic GPR imagery from 2010 and 104 examples of Greenland imagery from 2011, collected by our robot and a Pisten Bully tractor. The Antarctic and Greenland data were collected in the shear zone near McMurdo Station and between Thule Air Base and Summit Station, respectively. Using a modified cross validation technique, we correctly classified 86 of the Antarctic examples and 90 of the Greenland examples with a radial basis kernel SVM trained and evaluated on down-sampled and texture-mapped GPR images of crevasses, compared to 60% classification rate using raw data. In order to reduce false positives, we use the SVM classification results as pre-screener flags that mark locations in the GPR files to evaluate with two gaussian HMMs, and evaluate our results with a similar modified cross validation technique. The combined SVM pre-screen-HMM confirm method retains all the correct classifications by the SVM, and reduces the false positive rate to 4%. This method also reduces the computational burden in classifying GPR traces because the HMM is only being evaluated on select pre-screened traces. Our experiments demonstrate the promise, robustness and reliability of real-time crevasse detection and classification with robotic GPR surveys.

  2. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    Science.gov (United States)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  3. On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations

    DEFF Research Database (Denmark)

    Sturm, Bob L.; Noorzad, Pardis

    2012-01-01

    A recent system combining sparse representation classification (SRC) and a perceptually-based acoustic feature (ATM) \\cite{Panagakis2009,Panagakis2009b,Panagakis2010c}, outperforms by a significant margin the state of the art in music genre recognition, e.g., \\cite{Bergstra2006}. With genre so...

  4. A framework for evaluating automatic indexing or classification in the context of retrieval

    OpenAIRE

    Golub, K.; Soergel, D.; Buchanan, G.; Tudhope, D.; Lykke, M.; Hiom, D.

    2016-01-01

    Tools for automatic subject assignment help deal with scale and sustainability in creating and enriching metadata, establishing more connections across and between resources and enhancing consistency. While some software vendors and experimental researchers claim the tools can replace manual subject indexing, hard scientific evidence of their performance in operating information environments is scarce. A major reason for this is that research is usually conducted in laboratory conditions, exc...

  5. Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

    Directory of Open Access Journals (Sweden)

    Behnam Torabi

    2014-10-01

    Full Text Available Automatic speech segmentation as an important part of speech recognition system (ASR is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker’s face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker’s image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.

  6. Food Safety by Using Machine Learning for Automatic Classification of Seeds of the South-American Incanut Plant

    International Nuclear Information System (INIS)

    The following paper deals with the classification of seeds and seed components of the South-American Incanut plant and the modification of a machine to handle this task. Initially the state of the art is being illustrated. The research was executed in Germany and with a relevant part in Peru and Ecuador. Theoretical considerations for the solution of an automatically analysis of the Incanut seeds were specified. The optimization of the analyzing software and the separation unit of the mechanical hardware are carried out with recognition results. In a final step the practical application of the analysis of the Incanut seeds is held on a trial basis and rated on the bases of statistic values

  7. Algorithms for the Automatic Classification and Sorting of Conifers in the Garden Nursery Industry

    DEFF Research Database (Denmark)

    Petri, Stig

    with the classification and sorting of plants using machine vision have been discussed as an introduction to the work reported here. The use of Nordmann firs as a basis for evaluating the developed algorithms naturally introduces a bias towards this species in the algorithms, but steps have been taken throughout...... was used as the basis for evaluating the constructed feature extraction algorithms. Through an analysis of the construction of a machine vision system suitable for classifying and sorting plants, the needs with regard to physical frame, lighting system, camera and software algorithms have been uncovered...... classification performance. A total of six feature extraction algorithms are reported in this work. These include algorithms that record the image data directly, describe the border of the plant object, describe the color characteristics of the plant, or attempts to extract and describe the trunk structure...

  8. Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles

    CERN Document Server

    Rahaman, G M Atiqur

    2009-01-01

    Quality control is an important issue in the ceramic tile industry. On the other hand maintaining the rate of production with respect to time is also a major issue in ceramic tile manufacturing. Again, price of ceramic tiles also depends on purity of texture, accuracy of color, shape etc. Considering this criteria, an automated defect detection and classification technique has been proposed in this report that can have ensured the better quality of tiles in manufacturing process as well as production rate. Our proposed method plays an important role in ceramic tiles industries to detect the defects and to control the quality of ceramic tiles. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time and also to decide about the recovery process so that the defected tiles may not be mixed with the fresh tiles.

  9. Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

    Science.gov (United States)

    Park, Jong-Uk; Lee, Hyo-Ki; Lee, Junghun; Urtnasan, Erdenebayar; Kim, Hojoong; Lee, Kyoung-Joung

    2015-09-01

    This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter. PMID:26261097

  10. Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

    Science.gov (United States)

    Park, Jong-Uk; Lee, Hyo-Ki; Lee, Junghun; Urtnasan, Erdenebayar; Kim, Hojoong; Lee, Kyoung-Joung

    2015-09-01

    This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter.

  11. AUTOMATIC CLASSIFICATION OF X-RATED VIDEOS USING OBSCENE SOUND ANALYSIS BASED ON A REPEATED CURVE-LIKE SPECTRUM FEATURE

    Directory of Open Access Journals (Sweden)

    JaeDeok Lim

    2011-11-01

    Full Text Available This paper addresses the automatic classification of X-rated videos by analyzing its obscene sounds. In this paper, obscene sounds refer to audio signals generated from sexual moans and screams during sexual scenes. By analyzing various sound samples, we determined the distinguishable characteristics of obscene sounds and propose a repeated curve-like spectrum feature that represents the characteristics of such sounds. We constructed 6,269 audio clips to evaluate the proposed feature, and separately constructed 1,200 X-rated and general videos for classification. The proposed feature has an F1-score, precision, and recall rate of 96.6%, 98.2%, and 95.2%, respectively, for the original dataset, and 92.6%, 97.6%, and 88.0% for a noisy dataset of 5dB SNR. And, in classifying videos, the feature has more than a 90% F1- score, 97% precision, and an 84% recall rate. From the measured performance, X-rated videos can be classified with only the audio features and the repeated curve-like spectrum feature is suitable to detect obscene sounds.

  12. Automatic Classification of X-rated Videos using Obscene Sound Analysis based on a Repeated Curve-like Spectrum Feature

    CERN Document Server

    Lim, JaeDeok; Han, SeungWan; Lee, ChoelHoon

    2011-01-01

    This paper addresses the automatic classification of X-rated videos by analyzing its obscene sounds. In this paper, obscene sounds refer to audio signals generated from sexual moans and screams during sexual scenes. By analyzing various sound samples, we determined the distinguishable characteristics of obscene sounds and propose a repeated curve-like spectrum feature that represents the characteristics of such sounds. We constructed 6,269 audio clips to evaluate the proposed feature, and separately constructed 1,200 X-rated and general videos for classification. The proposed feature has an F1-score, precision, and recall rate of 96.6%, 98.2%, and 95.2%, respectively, for the original dataset, and 92.6%, 97.6%, and 88.0% for a noisy dataset of 5dB SNR. And, in classifying videos, the feature has more than a 90% F1-score, 97% precision, and an 84% recall rate. From the measured performance, X-rated videos can be classified with only the audio features and the repeated curve-like spectrum feature is suitable to...

  13. Automatic Generation of Data Types for Classification of Deep Web Sources

    Energy Technology Data Exchange (ETDEWEB)

    Ngu, A H; Buttler, D J; Critchlow, T J

    2005-02-14

    A Service Class Description (SCD) is an effective meta-data based approach for discovering Deep Web sources whose data exhibit some regular patterns. However, it is tedious and error prone to create an SCD description manually. Moreover, a manually created SCD is not adaptive to the frequent changes of Web sources. It requires its creator to identify all the possible input and output types of a service a priori. In many domains, it is impossible to exhaustively list all the possible input and output data types of a source in advance. In this paper, we describe machine learning approaches for automatic generation of the data types of an SCD. We propose two different approaches for learning data types of a class of Web sources. The Brute-Force Learner is able to generate data types that can achieve high recall, but with low precision. The Clustering-based Learner generates data types that have a high precision rate, but with a lower recall rate. We demonstrate the feasibility of these two learning-based solutions for automatic generation of data types for citation Web sources and presented a quantitative evaluation of these two solutions.

  14. Automatic segmentation and classification of the reflected laser dots during analytic measurement of mirror surfaces

    Science.gov (United States)

    Wang, ZhenZhou

    2016-08-01

    In the past research, we have proposed a one-shot-projection method for analytic measurement of the shapes of the mirror surfaces, which utilizes the information of two captured laser dots patterns to reconstruct the mirror surfaces. Yet, the automatic image processing algorithms to extract the laser dots patterns have not been addressed. In this paper, a series of automatic image processing algorithms are proposed to segment and classify the projected laser dots robustly and efficiently during measuring the shapes of mirror surfaces and each algorithm is indispensible for the finally achieved accuracy. Firstly, the captured image is modeled and filtered by the designed frequency domain filter. Then, it is segmented by a robust threshold selection method. A novel iterative erosion method is proposed to separate connected dots. Novel methods to remove noise blob and retrieve missing dots are also proposed. An effective registration method is used to help to select the used SNF laser and the dot generation pattern by analyzing if the dot pattern obeys the principle of central projection well. Experimental results verified the effectiveness of all the proposed algorithms.

  15. Multistation alarm system for eruptive activity based on the automatic classification of volcanic tremor: specifications and performance

    Science.gov (United States)

    Langer, Horst; Falsaperla, Susanna; Messina, Alfio; Spampinato, Salvatore

    2015-04-01

    With over fifty eruptive episodes (Strombolian activity, lava fountains, and lava flows) between 2006 and 2013, Mt Etna, Italy, underscored its role as the most active volcano in Europe. Seven paroxysmal lava fountains at the South East Crater occurred in 2007-2008 and 46 at the New South East Crater between 2011 and 2013. Month-lasting lava emissions affected the upper eastern flank of the volcano in 2006 and 2008-2009. On this background, effective monitoring and forecast of volcanic phenomena are a first order issue for their potential socio-economic impact in a densely populated region like the town of Catania and its surroundings. For example, explosive activity has often formed thick ash clouds with widespread tephra fall able to disrupt the air traffic, as well as to cause severe problems at infrastructures, such as highways and roads. For timely information on changes in the state of the volcano and possible onset of dangerous eruptive phenomena, the analysis of the continuous background seismic signal, the so-called volcanic tremor, turned out of paramount importance. Changes in the state of the volcano as well as in its eruptive style are usually concurrent with variations of the spectral characteristics (amplitude and frequency content) of tremor. The huge amount of digital data continuously acquired by INGV's broadband seismic stations every day makes a manual analysis difficult, and techniques of automatic classification of the tremor signal are therefore applied. The application of unsupervised classification techniques to the tremor data revealed significant changes well before the onset of the eruptive episodes. This evidence led to the development of specific software packages related to real-time processing of the tremor data. The operational characteristics of these tools - fail-safe, robustness with respect to noise and data outages, as well as computational efficiency - allowed the identification of criteria for automatic alarm flagging. The

  16. Using Position Uncertainty in Recursive Automatic Target Classification of Radar Tracks

    DEFF Research Database (Denmark)

    Jochumsen, Lars Wurtz; Nielsen, Esben; Østergaard, Jan;

    2015-01-01

    . The uncertainty is sensor dependent and will therefore need to be taken into account when classifying based on training data from multiple different sources. Including the uncertainty in the radar plot positions, leads to an improved estimate of the probability of a target belonging to any given class in a list......In this paper, we show how radar plot uncertainty can be included leading to a more robust classification of targets observed by a rotating 2D radar. Targets far from the radar will have a greater uncertainty in the position and therefore the estimated speed of the targets will be more uncertain...

  17. Parameter design and performance analysis of shift actuator for a two-speed automatic mechanical transmission for pure electric vehicles

    Directory of Open Access Journals (Sweden)

    Jianjun Hu

    2016-08-01

    Full Text Available Recent developments of pure electric vehicles have shown that pure electric vehicles equipped with two-speed or multi-speed gearbox possess higher energy efficiency by ensuring the drive motor operates at its peak performance range. This article presents the design, analysis, and control of a two-speed automatic mechanical transmission for pure electric vehicles. The shift actuator is based on a motor-controlled camshaft where a special geometric groove is machined, and the camshaft realizes the axial positions of the synchronizer sleeve for gear engaging, disengaging, and speed control of the drive motor. Based on the force analysis of shift process, the parameters of shift actuator and shift motor are designed. The drive motor’s torque control strategy before shifting, speed governing control strategy before engaging, shift actuator’s control strategy during gear engaging, and drive motor’s torque recovery strategy after shift process are proposed and implemented with a prototype. To validate the performance of the two-speed gearbox, a test bed was developed based on dSPACE that emulates various operation conditions. The experimental results indicate that the shift process with the proposed shift actuator and control strategy could be accomplished within 1 s under various operation conditions, with shift smoothness up to passenger car standard.

  18. Statistical methods of automatic spectral classification and their application to the Hamburg/ESO Survey

    CERN Document Server

    Christlieb, N; Grasshoff, G; Christlieb, Norbert; Wisotzki, Lutz; Grasshoff, Gerd

    2002-01-01

    We employ classical statistical methods of multivariate classification for the exploitation of the stellar content of the Hamburg/ESO objective prism survey (HES). In a simulation study we investigate the precision of a three-dimensional classification (Teff, log g, [Fe/H]) achievable in the HES for stars in the effective temperature range 520010 (typically corresponding to B_J<16.5). The accuracies in log g and [Fe/H] are better than 0.68dex in the same S/N range. These precisions allow for a very efficient selection of metal-poor stars in the HES. We present a minimum cost rule for compilation of complete samples of objects of a given class, and a rejection rule for identification of corrupted or peculiar spectra. The algorithms we present are being used for the identification of other interesting objects in the HES data base as well, and they are applicable to other existing and future large data sets, such as those to be compiled by the DIVA and GAIA missions.

  19. Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach based on geomorphometry

    Science.gov (United States)

    Pedersen, G. B. M.

    2016-02-01

    A new object-oriented approach is developed to classify glaciovolcanic landforms (Procedure A) and their landform elements boundaries (Procedure B). It utilizes the principle that glaciovolcanic edifices are geomorphometrically distinct from lava shields and plains (Pedersen and Grosse, 2014), and the approach is tested on data from Reykjanes Peninsula, Iceland. The outlined procedures utilize slope and profile curvature attribute maps (20 m/pixel) and the classified results are evaluated quantitatively through error matrix maps (Procedure A) and visual inspection (Procedure B). In procedure A, the highest obtained accuracy is 94.1%, but even simple mapping procedures provide good results (> 90% accuracy). Successful classification of glaciovolcanic landform element boundaries (Procedure B) is also achieved and this technique has the potential to delineate the transition from intraglacial to subaerial volcanic activity in orthographic view. This object-oriented approach based on geomorphometry overcomes issues with vegetation cover, which has been typically problematic for classification schemes utilizing spectral data. Furthermore, it handles complex edifice outlines well and is easily incorporated into a GIS environment, where results can be edited or fused with other mapping results. The approach outlined here is designed to map glaciovolcanic edifices within the Icelandic neovolcanic zone but may also be applied to similar subaerial or submarine volcanic settings, where steep volcanic edifices are surrounded by flat plains.

  20. Design and construction of an automatic system for minimizing the risk of sinking of water vehicle

    Science.gov (United States)

    Sutradhar, Amit; Rashid, Md. Mahbubur; Helal-An-Nahiyan, Md.; Mandal, Manash Kumar

    2016-07-01

    This paper focuses on the reduction of the risk of water vehicle like launch, ferry, ship and boat from sinking which is a burning problem of Bangladesh now-a-days. Every year death toll is rising by leaps and bounds due to this unexpected phenomenon. The sinking mostly occurs due to overloading and lack of consciousness. That's why, an automated system is introduced here to make the travelers warned about the overloading situation through raising alarm before the vehicle starts to move on. The tolerance limit of the vehicle is determined based on the theory of buoyancy and floatation. Moreover, while moving on the water, the vehicle may get victim of sinking due to rough weather, low visibility or machineries breakdown. So water level indicator is used to determine the safe level of water. When water level rises up to the safe limit or just before crossing the safe limit, another alarm will warn the passengers which will sound quite different from the first alarm as stated before. And at once the on board GPS sensor will record the current position of the vehicle and transmit the location to the nearest rescue authority via GSM module in the form of text message which will help them to take necessary steps for the rescue of the passengers as soon as possible. Effective implementation of this method can reduce the accident as well as this research can also be a helpful tool to organize further researches in this field for the sake of humanity.

  1. Automatic recognition of light source from color negative films using sorting classification techniques

    Science.gov (United States)

    Sanger, Demas S.; Haneishi, Hideaki; Miyake, Yoichi

    1995-08-01

    This paper proposed a simple and automatic method for recognizing the light sources from various color negative film brands by means of digital image processing. First, we stretched the image obtained from a negative based on the standardized scaling factors, then extracted the dominant color component among red, green, and blue components of the stretched image. The dominant color component became the discriminator for the recognition. The experimental results verified that any one of the three techniques could recognize the light source from negatives of any film brands and all brands greater than 93.2 and 96.6% correct recognitions, respectively. This method is significant for the automation of color quality control in color reproduction from color negative film in mass processing and printing machine.

  2. 基于RFID的车辆自动化智能管理系统研究%Research of vehicle automatic management system based on RFID

    Institute of Scientific and Technical Information of China (English)

    张丽然; 沈胜利

    2012-01-01

    In order to solve the current residential parking problem,the residential vehicle automatic management system is designed based on ETC technology. With the analysis of the actual demand, the system includes three parts: vehicle out and in management, vehicle positioning management and vehicle parking management. The vehicle out and in management system could identify and confirm the vehicles going into the community automatically; vehicle positioning management system is responsible for tracking and positioning the vehicle in the community; vehicle parking management system would assign and unlock the parking space for the vehicle automatically. After practices, it proves that the system has good performance and practical value.%基于解决当前小区停车难问题的目的,采用ETC电子不停车收费相关技术,设计了小区车辆自动化管理系统;通过对实际需求的分析,所设计的系统主要包括3个部分:车辆出入管理、定位管理以及停车管理。其中,车辆出入管理系统对进入的车辆进行身份的自动识别和确认;车辆定位管理系统负责对在小区申行驶的车辆进行追踪定位;车辆停车管理系统则为进入的车辆自动分配车住和开启车位锁。经过实践的证明,本系统性能良好,具有较好的实用价值。

  3. Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks.

    Science.gov (United States)

    Queiroz, Vinícius; Luz, Eduardo; Moreira, Gladston; Guarda, Álvaro; Menotti, David

    2015-01-01

    This paper intends to bring new insights in the methods for extracting features for cardiac arrhythmia detection and classification systems. We explore the possibility for utilizing vectorcardiograms (VCG) along with electrocardiograms (ECG) to get relevant informations from the heartbeats on the MIT-BIH database. For this purpose, we apply complex networks to extract features from the VCG. We follow the ANSI/AAMI EC57:1998 standard, for classifying the beats into 5 classes (N, V, S, F and Q), and de Chazal's scheme for dataset division into training and test set, with 22 folds validation setup for each set. We used the Support Vector Machinhe (SVM) classifier and the best result we chose had a global accuracy of 84.1%, while still obtaining relatively high Sensitivities and Positive Predictive Value and low False Positive Rates, when compared to other papers that follows the same evaluation methodology that we do. PMID:26737464

  4. Automatic control system of the radiometric system for inspection of large-scale vehicles and cargoes

    International Nuclear Information System (INIS)

    The automatic control system (ACS) is intended to control the equipment of the radiometric inspection system in the normal operating modes as well as during the preventive maintenance, maintenance/repair and adjustment works; for acquisition of the data on the status of the equipment, reliable protection of the personnel and equipment, acquisition, storage and processing of the results of operation and to ensure service maintenance.

  5. Driver head displacement during (automatic) vehicle braking tests with varying levels of distraction

    NARCIS (Netherlands)

    Rooij, L. van; Pauwelussen, J.; Camp, O.M.G.C. op den; Janssen, J.M.

    2013-01-01

    Vehicle occupant behavior in emergency driving conditions has a large effect on traffic safety. Distraction is estimated to be the cause of 15-20% of all crashes. Additionally, the posture of the occupants prior to the possibly unavoidable crash is known to have a large effect on the injury reducing

  6. A Communication Protocol and Monitoring Policy for Input/Output Vehicles in an Automatic Storage and Retrieval System

    Institute of Scientific and Technical Information of China (English)

    LI Li; LI Wenfeng; LIAO Xiaoping; SU Wengui; LIN Yizhong

    2006-01-01

    The acquisition and processing of equipment information is pivotal to control and management of the automated storage and retrieval system. The work of this paper is based on the automatic storage and retrieval experimental system of Wuhan University of Technology. First, the output/input flow and the control information of storage/retrieval vehicle are studied and the plotting finite state machine model of the stacking crane is established. Then, the communication protocol between the center control management computer and the PLC of stacking crane is designed. Finally, the stacking crane's monitoring data, which include operating time, running states and real-time position status, are gained by analyzing the communication protocol. The detailed program for the acquisition and processing of monitoring information is developed. This method is suitable for the equipment monitoring of the whole system, and provides a platform for studying the intelligent control and optimal scheduling strategies of AS/RS.

  7. Automatic guided vehicle contributing to physical distribution of automobile parts; Butsuryu no jidoka ni kokensuru jidosha buhin mujin hanso system

    Energy Technology Data Exchange (ETDEWEB)

    Inaba, E. [Meidensha Corp., Tokyo (Japan)

    1997-06-30

    This paper presents one example of the unmanned carrying systems using the small automatic guided vehicles (AGV) recently delivered by Meidensha Corp. This system carries hand trucks or buckets loaded with such small parts for automobile engines as sensor and connector by the AGVs. The following abilities were required in adoption of this system: (1) Indication of destinations to several AGVs coming from different lines, and monitoring of traveling conditions of every AGV, (2) The optimum traveling/waiting control between AGVs at crossings, and (3) Hand truck carrying in consideration of an importance of safety. This system allows integrated control of ten and several AGVs using the AGV control board through the stationary radio control station and radio equipment on AGVs. In addition, this system allows indicating communication of destinations to AGVs, and realtime control of AGV traveling conditions. Waiting control of entrance/exit by intercommunication between AGVs is also possible. 7 figs., 2 tabs.

  8. The Iqmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

    Science.gov (United States)

    Böhm, J.; Bredif, M.; Gierlinger, T.; Krämer, M.; Lindenberg, R.; Liu, K.; Michel, F.; Sirmacek, B.

    2016-06-01

    Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

  9. A Contribution for the Automatic Sleep Classification Based on the Itakura-Saito Spectral Distance

    Science.gov (United States)

    Cardoso, Eduardo; Batista, Arnaldo; Rodrigues, Rui; Ortigueira, Manuel; Bárbara, Cristina; Martinho, Cristina; Rato, Raul

    Sleep staging is a crucial step before the scoring the sleep apnoea, in subjects that are tested for this condition. These patients undergo a whole night polysomnography recording that includes EEG, EOG, ECG, EMG and respiratory signals. Sleep staging refers to the quantification of its depth. Despite the commercial sleep software being able to stage the sleep, there is a general lack of confidence amongst health practitioners of these machine results. Generally the sleep scoring is done over the visual inspection of the overnight patient EEG recording, which takes the attention of an expert medical practitioner over a couple of hours. This contributes to a waiting list of two years for patients of the Portuguese Health Service. In this work we have used a spectral comparison method called Itakura distance to be able to make a distinction between sleepy and awake epochs in a night EEG recording, therefore automatically doing the staging. We have used the data from 20 patients of Hospital Pulido Valente, which had been previously visually expert scored. Our technique results were promising, in a way that Itakura distance can, by itself, distinguish with a good degree of certainty the N2, N3 and awake states. Pre-processing stages for artefact reduction and baseline removal using Wavelets were applied.

  10. An object-based classification method for automatic detection of lunar impact craters from topographic data

    Science.gov (United States)

    Vamshi, Gasiganti T.; Martha, Tapas R.; Vinod Kumar, K.

    2016-05-01

    Identification of impact craters is a primary requirement to study past geological processes such as impact history. They are also used as proxies for measuring relative ages of various planetary or satellite bodies and help to understand the evolution of planetary surfaces. In this paper, we present a new method using object-based image analysis (OBIA) technique to detect impact craters of wide range of sizes from topographic data. Multiresolution image segmentation of digital terrain models (DTMs) available from the NASA's LRO mission was carried out to create objects. Subsequently, objects were classified into impact craters using shape and morphometric criteria resulting in 95% detection accuracy. The methodology developed in a training area in parts of Mare Imbrium in the form of a knowledge-based ruleset when applied in another area, detected impact craters with 90% accuracy. The minimum and maximum sizes (diameters) of impact craters detected in parts of Mare Imbrium by our method are 29 m and 1.5 km, respectively. Diameters of automatically detected impact craters show good correlation (R2 > 0.85) with the diameters of manually detected impact craters.

  11. Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm.

    Directory of Open Access Journals (Sweden)

    Owen R Bidder

    Full Text Available Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1 Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2 they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN. Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

  12. Automatic classification of unexploded ordnance applied to Spencer Range live site for 5x5 TEMTADS sensor

    Science.gov (United States)

    Sigman, John B.; Barrowes, Benjamin E.; O'Neill, Kevin; Shubitidze, Fridon

    2013-06-01

    This paper details methods for automatic classification of Unexploded Ordnance (UXO) as applied to sensor data from the Spencer Range live site. The Spencer Range is a former military weapons range in Spencer, Tennessee. Electromagnetic Induction (EMI) sensing is carried out using the 5x5 Time-domain Electromagnetic Multi-sensor Towed Array Detection System (5x5 TEMTADS), which has 25 receivers and 25 co-located transmitters. Every transmitter is activated sequentially, each followed by measuring the magnetic field in all 25 receivers, from 100 microseconds to 25 milliseconds. From these data target extrinsic and intrinsic parameters are extracted using the Differential Evolution (DE) algorithm and the Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms, respectively. Namely, the inversion provides x, y, and z locations and a time series of the total ONVMS principal eigenvalues, which are intrinsic properties of the objects. The eigenvalues are fit to a power-decay empirical model, the Pasion-Oldenburg model, providing 3 coefficients (k, b, and g) for each object. The objects are grouped geometrically into variably-sized clusters, in the k-b-g space, using clustering algorithms. Clusters matching a priori characteristics are identified as Targets of Interest (TOI), and larger clusters are automatically subclustered. Ground Truths (GT) at the center of each class are requested, and probability density functions are created for clusters that have centroid TOI using a Gaussian Mixture Model (GMM). The probability functions are applied to all remaining anomalies. All objects of UXO probability higher than a chosen threshold are placed in a ranked dig list. This prioritized list is scored and the results are demonstrated and analyzed.

  13. [Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy].

    Science.gov (United States)

    Xu, Yonghong; Cui, Jie; Hong, Wenxue; Liang, Huijuan

    2015-04-01

    Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S. PMID:26211236

  14. Automatic braking system modification for the Advanced Transport Operating Systems (ATOPS) Transportation Systems Research Vehicle (TSRV)

    Science.gov (United States)

    Coogan, J. J.

    1986-01-01

    Modifications were designed for the B-737-100 Research Aircraft autobrake system hardware of the Advanced Transport Operating Systems (ATOPS) Program at Langley Research Center. These modifications will allow the on-board flight control computer to control the aircraft deceleration after landing to a continuously variable level for the purpose of executing automatic high speed turn-offs from the runway. A bread board version of the proposed modifications was built and tested in simulated stopping conditions. Test results, for various aircraft weights, turnoff speed, winds, and runway conditions show that the turnoff speeds are achieved generally with errors less than 1 ft/sec.

  15. BY USING BLUETOOTH TECHNOLOGY AUTOMATIC VEHICLE ACCIDENT DETECTION & LOCALIZATION OF AUTOMOBILE

    OpenAIRE

    Nitin Thakre; Nitin Raut; Shyam Dubey; Abdulla Shaikh

    2014-01-01

    Traffic accidents are one of the leading causes of fatalities in the world. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the location. Eliminating the time between when an accident occurs and when first responders are dispatched to the location decreases mortality rates by 6%. We propose an Android based application that location of the vehicle through an positive detectio...

  16. Automatic classification of long-term ambulatory ECG records according to type of ischemic heart disease

    Directory of Open Access Journals (Sweden)

    Smrdel Aleš

    2011-12-01

    Full Text Available Abstract Background Elevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG records appear generally in patients with transmural ischemia (e. g. Prinzmetal's angina while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina. Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions and classifies AECG records according to type of ischemic heart disease (Prinzmetal's angina; coronary artery diseases excluding patients with Prinzmetal's angina; other heart diseases. Methods The algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB. The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions, and then classifies records according to type of ischemic heart disease. Results Using 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes, the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetal's angina into the Prinzmetal's angina category (7 out of 8; majority of the records with other coronary artery diseases into the coronary artery diseases excluding patients with Prinzmetal's angina category (47 out of 55; and correctly

  17. TEXT CLASSIFICATION FOR AUTOMATIC DETECTION OF E-CIGARETTE USE AND USE FOR SMOKING CESSATION FROM TWITTER: A FEASIBILITY PILOT.

    Science.gov (United States)

    Aphinyanaphongs, Yin; Lulejian, Armine; Brown, Duncan Penfold; Bonneau, Richard; Krebs, Paul

    2016-01-01

    Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect e-cigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0.94 area under the curve in each category. These promising initial results form the foundation for further studies to realize the ideal surveillance solution.

  18. Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

    Science.gov (United States)

    Perry, Daniel; Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

    2012-03-01

    Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.

  19. Shifting Rule Modification Strategy of Automatic Transmission Based on Driver-vehicle-road Environment

    Institute of Scientific and Technical Information of China (English)

    WU Guangqiang; ZHANG Deming

    2010-01-01

    Accidental or frequent shift often occurs when the shifting rule is built based on traditional two parameters (I.e., velocity and throttle), because the speed of engine varies slower than change of throttle opening. Currently, modifying shift point velocity value or throttle by throttle change rate is one of common methods, but the results are not so satisfactory in some working condition such as uphill. The reason is that these methods merely consider throttle change rate which is not enough for a car driving in driver-vehicle-road environment system. So a novel fuzzy control modification strategy is proposed to avoid or reduce those abnormal shift actions. It can adjust shifting rule by the change rate of throttle, current gear position and road environment information, while different gear position and driving environment get corresponding modification value. In order to compare the results of shifting actions, fuel consumption and braking distance, emergent braking in level road and extra-urban driving cycle(EUDC) working conditions with fuzzy shifting schedule modification strategy are simulated digitally. Furthermore, a hardware-in-the-loop simulation platform is introduced to verify its effect in slope road condition according to the ON/OFF numbers of solenoid valve in hydraulic system. The simulation results show that the problem of unexpected shift in those working conditions may be resolved by fuzzy modification strategy. At last, it is concluded that although there is some slight decline in power performance in uphill situation, this fuzzy modification strategy could correctly identify slope of road, decrease braking distance, improve vehicle comfort and fuel economy effectively and prolong the life of clutch system. So, this fuzzy logic shifting strategy provides important References for vehicle intelligent shifting schedule.

  20. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  1. AUTOMATIC ANALYSIS AND CLASSIFICATION OF THE ROOF SURFACES FOR THE INSTALLATION OF SOLAR PANELS USING A MULTI-DATA SOURCE AND MULTI-SENSOR AERIAL PLATFORM

    OpenAIRE

    López, L.; Lagüela, S.; Picon, I.; D. González-Aguilera

    2015-01-01

    A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbour solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the surfaces, slo...

  2. Large Scale Automatic Analysis and Classification of Roof Surfaces for the Installation of Solar Panels Using a Multi-Sensor Aerial Platform

    OpenAIRE

    Luis López-Fernández; Susana Lagüela; Inmaculada Picón; Diego González-Aguilera

    2015-01-01

    A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbor solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the areas, tilts, ...

  3. Automatic Generation of Overlays and Offset Values Based on Visiting Vehicle Telemetry and RWS Visuals

    Science.gov (United States)

    Dunne, Matthew J.

    2011-01-01

    The development of computer software as a tool to generate visual displays has led to an overall expansion of automated computer generated images in the aerospace industry. These visual overlays are generated by combining raw data with pre-existing data on the object or objects being analyzed on the screen. The National Aeronautics and Space Administration (NASA) uses this computer software to generate on-screen overlays when a Visiting Vehicle (VV) is berthing with the International Space Station (ISS). In order for Mission Control Center personnel to be a contributing factor in the VV berthing process, computer software similar to that on the ISS must be readily available on the ground to be used for analysis. In addition, this software must perform engineering calculations and save data for further analysis.

  4. Semi-automatic classification of skeletal morphology in genetically altered mice using flat-panel volume computed tomography.

    Directory of Open Access Journals (Sweden)

    Christian Dullin

    2007-07-01

    Full Text Available Rapid progress in exploring the human and mouse genome has resulted in the generation of a multitude of mouse models to study gene functions in their biological context. However, effective screening methods that allow rapid noninvasive phenotyping of transgenic and knockout mice are still lacking. To identify murine models with bone alterations in vivo, we used flat-panel volume computed tomography (fpVCT for high-resolution 3-D imaging and developed an algorithm with a computational intelligence system. First, we tested the accuracy and reliability of this approach by imaging discoidin domain receptor 2- (DDR2- deficient mice, which display distinct skull abnormalities as shown by comparative landmark-based analysis. High-contrast fpVCT data of the skull with 200 microm isotropic resolution and 8-s scan time allowed segmentation and computation of significant shape features as well as visualization of morphological differences. The application of a trained artificial neuronal network to these datasets permitted a semi-automatic and highly accurate phenotype classification of DDR2-deficient compared to C57BL/6 wild-type mice. Even heterozygous DDR2 mice with only subtle phenotypic alterations were correctly determined by fpVCT imaging and identified as a new class. In addition, we successfully applied the algorithm to classify knockout mice lacking the DDR1 gene with no apparent skull deformities. Thus, this new method seems to be a potential tool to identify novel mouse phenotypes with skull changes from transgenic and knockout mice on the basis of random mutagenesis as well as from genetic models. However for this purpose, new neuronal networks have to be created and trained. In summary, the combination of fpVCT images with artificial neuronal networks provides a reliable, novel method for rapid, cost-effective, and noninvasive primary screening tool to detect skeletal phenotypes in mice.

  5. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

    Science.gov (United States)

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-12-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

  6. Nonlinear automatic landing control of unmanned aerial vehicles on moving platforms via a 3D laser radar

    Energy Technology Data Exchange (ETDEWEB)

    Hervas, Jaime Rubio; Tang, Hui [School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798 (Singapore); Reyhanoglu, Mahmut [Physical Sciences Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114 (United States)

    2014-12-10

    This paper presents a motion tracking and control system for automatically landing Unmanned Aerial Vehicles (UAVs) on an oscillating platform using Laser Radar (LADAR) observations. The system itself is assumed to be mounted on a ship deck. A full nonlinear mathematical model is first introduced for the UAV. The ship motion is characterized by a Fourier transform based method which includes a realistic characterization of the sea waves. LADAR observation models are introduced and an algorithm to process those observations for yielding the relative state between the vessel and the UAV is presented, from which the UAV's state relative to an inertial frame can be obtained and used for feedback purposes. A sliding mode control algorithm is derived for tracking a landing trajectory defined by a set of desired waypoints. An extended Kalman filter (EKF) is proposed to account for process and observation noises in the design of a state estimator. The effectiveness of the control algorithm is illustrated through a simulation example.

  7. Nonlinear automatic landing control of unmanned aerial vehicles on moving platforms via a 3D laser radar

    International Nuclear Information System (INIS)

    This paper presents a motion tracking and control system for automatically landing Unmanned Aerial Vehicles (UAVs) on an oscillating platform using Laser Radar (LADAR) observations. The system itself is assumed to be mounted on a ship deck. A full nonlinear mathematical model is first introduced for the UAV. The ship motion is characterized by a Fourier transform based method which includes a realistic characterization of the sea waves. LADAR observation models are introduced and an algorithm to process those observations for yielding the relative state between the vessel and the UAV is presented, from which the UAV's state relative to an inertial frame can be obtained and used for feedback purposes. A sliding mode control algorithm is derived for tracking a landing trajectory defined by a set of desired waypoints. An extended Kalman filter (EKF) is proposed to account for process and observation noises in the design of a state estimator. The effectiveness of the control algorithm is illustrated through a simulation example

  8. Semi-automatic characterization of fractured rock masses using 3D point clouds: discontinuity orientation, spacing and SMR geomechanical classification

    Science.gov (United States)

    Riquelme, Adrian; Tomas, Roberto; Abellan, Antonio; Cano, Miguel; Jaboyedoff, Michel

    2015-04-01

    Investigation of fractured rock masses for different geological applications (e.g. fractured reservoir exploitation, rock slope instability, rock engineering, etc.) requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in 3D data acquisition using photogrammetric and/or LiDAR techniques currently allow a quick and an accurate characterization of rock mass discontinuities. This contribution presents a methodology for: (a) use of 3D point clouds for the identification and analysis of planar surfaces outcropping in a rocky slope; (b) calculation of the spacing between different discontinuity sets; (c) semi-automatic calculation of the parameters that play a capital role in the Slope Mass Rating geomechanical classification. As for the part a) (discontinuity orientation), our proposal identifies and defines the algebraic equations of the different discontinuity sets of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test. Additionally, the procedure finds principal orientations by Kernel Density Estimation and identifies clusters (Riquelme et al., 2014). As a result of this analysis, each point is classified with a discontinuity set and with an outcrop plane (cluster). Regarding the part b) (discontinuity spacing) our proposal utilises the previously classified point cloud to investigate how different outcropping planes are linked in space. Discontinuity spacing is calculated for each pair of linked clusters within the same discontinuity set, and then spacing values are analysed calculating their statistic values. Finally, as for the part c) the previous results are used to calculate parameters F_1, F2 and F3 of the Slope Mass Rating geomechanical classification. This analysis is carried out for each discontinuity set using their respective orientation extracted in part a). The open access tool SMRTool (Riquelme et al., 2014) is then used to calculate F1 to F3 correction

  9. Kardashev's Classification at 50+: A Fine Vehicle with Room for Improvement

    CERN Document Server

    Cirkovic, Milan M

    2016-01-01

    We review the history and status of the famous classification of extraterrestrial civilizations given by the great Russian astrophysicist Nikolai Semenovich Kardashev, roughly half a century after it has been proposed. While Kardashev's classification (or Kardashev's scale) has often been seen as oversimplified, and multiple improvements, refinements, and alternatives to it have been suggested, it is still one of the major tools for serious theoretical investigation of SETI issues. During these 50+ years, several attempts at modifying or reforming the classification have been made; we review some of them here, together with presenting some of the scenarios which present difficulties to the standard version. Recent results in both theoretical and observational SETI studies, especially the G-hat infrared survey (2014-2015), have persuasively shown that the emphasis on detectability inherent in Kardashev's classification obtains new significance and freshness. Several new movements and conceptual frameworks, suc...

  10. Real-time, resource-constrained object classification on a micro-air vehicle

    Science.gov (United States)

    Buck, Louis; Ray, Laura

    2013-12-01

    A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

  11. Kardashev's Classification at 50+: A Fine Vehicle With Room for Improvement

    Science.gov (United States)

    Ćirković, M. M.

    2015-12-01

    We review the history and status of the famous classification of extraterrestrial civilizations given by the great Russian astrophysicist Nikolai Semenovich Kardashev, roughly half a century after it has been proposed. While Kardashev's classification (or Kardashev's scale) has often been seen as oversimplified, and multiple improvements, refinements, and alternatives to it have been suggested, it is still one of the major tools for serious theoretical investigation of SETI issues. During these 50+ years, several attempts at modifying or reforming the classification have been made; we review some of them here, together with presenting some of the scenarios which present difficulties to the standard version. Recent results in both theoretical and observational SETI studies, especially the {Ĝ infrared survey (2014-2015), have persuasively shown that the emphasis on detectability inherent in Kardashev's classification obtains new significance and freshness. Several new movements and conceptual frameworks, such as the Dysonian SETI, tally extremely well with these developments. So, the apparent simplicity of the classification is highly deceptive: Kardashev's work offers a wealth of still insufficiently studied methodological and epistemological ramifications and it remains, in both letter and spirit, perhaps the worthiest legacy of the SETI "founding fathers".

  12. Development and Testing of an Automatic Transmission Shift Schedule Algorithm for Vehicle Simulation (SAE Paper 2015-01-1142)

    Science.gov (United States)

    The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) modeling tool was created by EPA to estimate greenhouse gas (GHG) emissions of light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle type...

  13. Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring

    Science.gov (United States)

    In this paper, we examine the potential of using a small unmanned aerial vehicle (UAV) for rangeland inventory, assessment and monitoring. Imagery with 8-cm resolution was acquired over 290 ha in southwestern Idaho. We developed a semi-automated orthorectification procedure suitable for handling lar...

  14. An automatic segmentation method for building facades from vehicle-borne LiDAR point cloud data based on fundamental geographical data

    Science.gov (United States)

    Li, Yongqiang; Mao, Jie; Cai, Lailiang; Zhang, Xitong; Li, Lixue

    2016-03-01

    In this paper, the author proposed a segmentation method based on the fundamental geographic data, the algorithm describes as following: Firstly, convert the coordinate system of fundamental geographic data to that of vehicle- borne LiDAR point cloud though some data preprocessing work, and realize the coordinate system between them; Secondly, simplify the feature of fundamental geographic data, extract effective contour information of the buildings, then set a suitable buffer threshold value for building contour, and segment out point cloud data of building facades automatically; Thirdly, take a reasonable quality assessment mechanism, check and evaluate of the segmentation results, control the quality of segmentation result. Experiment shows that the proposed method is simple and effective. The method also has reference value for the automatic segmentation for surface features of other types of point cloud.

  15. Automatic classification of transient ischaemic and transient non-ischaemic heart-rate related ST segment deviation episodes in ambulatory ECG records

    International Nuclear Information System (INIS)

    In ambulatory ECG records, besides transient ischaemic ST segment deviation episodes, there are also transient non-ischaemic heart-rate related ST segment deviation episodes present, which appear only due to a change in heart rate and thus complicate automatic detection of true ischaemic episodes. The goal of this work was to automatically classify these two types of episodes. The tested features to classify the ST segment deviation episodes were changes of heart rate, changes of the Mahalanobis distance of the first five Karhunen–Loève transform (KLT) coefficients of the QRS complex, changes of time-domain morphologic parameters of the ST segment and changes of the Legendre orthonormal polynomial coefficients of the ST segment. We chose Legendre basis functions because they best fit typical shapes of the ST segment morphology, thus allowing direct insight into the ST segment morphology changes through the feature space. The classification was performed with the help of decision trees. We tested the classification method using all records of the Long-Term ST Database on all ischaemic and all non-ischaemic heart-rate related deviation episodes according to annotation protocol B. In order to predict the real-world performance of the classification we used second-order aggregate statistics, gross and average statistics, and the bootstrap method. We obtained the best performance when we combined the heart-rate features, the Mahalanobis distance and the Legendre orthonormal polynomial coefficient features, with average sensitivity of 98.1% and average specificity of 85.2%

  16. Design of Automatic Guide Vehicle Based on AT89S52%基于AT89S52的自动导引车设计

    Institute of Scientific and Technical Information of China (English)

    何裕源; 何晓晖

    2013-01-01

    介绍一种自动导引车(Automatic Guided Vehicle)的设计方法,小车采用红外传感器为导引装置,直流电机为驱动装置,以AT89S52为主控核心,指导自动导引车自动识别正确轨迹并实现运行.该系统电气原理简单,可靠性能高,为自动导引车在工业中应用开发提供参考.

  17. Automatic classification of gammas-gamma coincidence matrices; Clasificacion automatizada de matrices de coincidencias Gamma-Gamma

    Energy Technology Data Exchange (ETDEWEB)

    Los Arcos Merino, J. M.; Gonzalez, J. A.

    1978-07-01

    The information obtained during a coincidence experiment, recorded on magnetic tape by a MULTI-8 minicomputer, is transferred to a new tape in 36 bit words, using the program LEC0M8. The classification in two dimensional matrix form is carried out off-line, on a magnetic disk file, by the program CLAFI. On finishing classification one obtains a copy of the coincidence matrix on the second magnetic tape. Both programs are written to be processed in that order with the UNIVAC 1106 computer of J.E.N. (Author) 4 refs.

  18. MODELING OF UNIFORM MOVING OF A VEHICLE EQUIPPED WITH AUTOMATICAL TRANSMISSION, BASED ON THE GEAR-LEVER VARIATOR

    OpenAIRE

    Ternyuk, N.; Krasnoshtan, A.

    2006-01-01

    The analyses of a uniform drive of a vehicle equipped with a continuonsly variable transmission fased on the gear-lever vibrator has been done a functional dependence of the parameters of a uniform drive (speed) on construction a vehicle have been analysed. The basic functional dependence between these parameters have been presented.

  19. Real-time adaptive off-road vehicle navigation and terrain classification

    Science.gov (United States)

    Muller, Urs A.; Jackel, Lawrence D.; LeCun, Yann; Flepp, Beat

    2013-05-01

    We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au­tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

  20. Large Scale Automatic Analysis and Classification of Roof Surfaces for the Installation of Solar Panels Using a Multi-Sensor Aerial Platform

    Directory of Open Access Journals (Sweden)

    Luis López-Fernández

    2015-09-01

    Full Text Available A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbor solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the areas, tilts, orientations and the existence of obstacles to locate the optimal zones inside each roof surface for the installation of solar panels. This information is complemented with the estimation of the solar irradiation received by each surface. This way, large areas may be efficiently analyzed obtaining as final result the optimal locations for the placement of solar panels as well as the information necessary (location, orientation, tilt, area and solar irradiation to estimate the productivity of a solar panel from its technical characteristics.

  1. Emotion in the singing voice-a deeper look at acoustic features in the light of automatic classification

    OpenAIRE

    Eyben, Florian; Salomao, Glaucia Laís; Sundberg, Johan; Scherer, Klaus R.; Schuller, Bjorn W.

    2015-01-01

    We investigate the automatic recognition of emotions in the singing voice and study the worth and role of a variety of relevant acoustic parameters. The data set contains phrases and vocalises sung by eight renowned professional opera singers in ten different emotions and a neutral state. The states are mapped to ternary arousal and valence labels. We propose a small set of relevant acoustic features basing on our previous findings on the same data and compare it with a large-scale state-of-t...

  2. Automatic classification of lung tumour heterogeneity according to a visual-based score system in dynamic contrast enhanced CT sequences

    Science.gov (United States)

    Bevilacqua, Alessandro; Baiocco, Serena

    2016-03-01

    Computed tomography (CT) technologies have been considered for a long time as one of the most effective medical imaging tools for morphological analysis of body parts. Contrast Enhanced CT (CE-CT) also allows emphasising details of tissue structures whose heterogeneity, inspected through visual analysis, conveys crucial information regarding diagnosis and prognosis in several clinical pathologies. Recently, Dynamic CE-CT (DCE-CT) has emerged as a promising technique to perform also functional hemodynamic studies, with wide applications in the oncologic field. DCE-CT is based on repeated scans over time performed after intravenous administration of contrast agent, in order to study the temporal evolution of the tracer in 3D tumour tissue. DCE-CT pushes towards an intensive use of computers to provide automatically quantitative information to be used directly in clinical practice. This requires that visual analysis, representing the gold-standard for CT image interpretation, gains objectivity. This work presents the first automatic approach to quantify and classify the lung tumour heterogeneities based on DCE-CT image sequences, so as it is performed through visual analysis by experts. The approach developed relies on the spatio-temporal indices we devised, which also allow exploiting temporal data that enrich the knowledge of the tissue heterogeneity by providing information regarding the lesion status.

  3. 自动情感文本分类研究综述%A Literature Review on Automatic Sentiment Classification

    Institute of Scientific and Technical Information of China (English)

    夏火松; 彭柳艳; 余梦麟

    2011-01-01

    Sentiment classification and its applications have been witnessed a booming interest in nowadays research,it is a cross research of natural language processing, machine learning and psychology, and has practical applications in many fields, such as product reputation analysis, public opinion tracking, blogger interests analysis, and so on. This paper gives an overview of the current study on sentiment classification of domestic and international, divides research directions of existing literature into four categories, then describe these four categories in detail, and analyzes the key issues in this field, then proposes a general framework for sentiment classification, finally, discuss the shortcomings of current study, and predicts the development trend.%情感分类及其应用是目前研究的一个热点,是自然语言处理,机器学习和心理学等多学科交叉的研究课题,在很多领域都有实际的应用,如产品的声誉分析,舆情跟踪,博客兴趣分析等.论文对情感分类目前国内外的研究概貌进行了分析,将现有文献中的研究方向分为四个类别,并对这四个类别分别进行了描述,对情感分类中的关键问题进行了研究,提出了情感分类的一般框架,最后对目前研究中存在的不足进行了讨论,对情感分类研究的发展方向进行了展望.

  4. 农业机械自动导航技术研究进展%Review of research on automatic guidance of agricultural vehicles

    Institute of Scientific and Technical Information of China (English)

    胡静涛; 高雷; 白晓平; 李逃昌; 刘晓光

    2015-01-01

    农业机械自动导航是精准农业技术体系中的一项核心关键技术,广泛应用于耕作、播种、施肥、喷药、收获等农业生产过程。农机位置测量方法、农机模型与导航路径跟踪控制方法是农业机械自动导航技术的研究重点,受到国内外科研人员的广泛关注。农机位置测量主要有相对测量和绝对测量二类方法,前者以基于机器视觉的测量方法为代表,主要利用图像处理技术识别作物行,进而确定导航基准线,实现农机与作物的相对位置与航向信息的测量;后者则以基于全球导航卫星系统的测量方法为代表,利用卫星定位技术实现农机位置的高精度测量,在农业生产中应用最为广泛;而面对复杂的田间环境变化,在位置测量中应用多传感器数据融合技术通常可以得到更好的测量结果。导航路径跟踪控制通常以农机运动学模型或动力学模型为核心,多采用最优控制、最优估计、自适应控制、人工神经网络、模糊控制、鲁棒控制等现代控制理论与方法;而无模型控制方法则可以避免建模不准确或者模型参数剧烈变化对农机路径跟踪控制性能所产生的负面影响。该文从上述2个方面综述分析了农业机械自动导航技术的研究现状及存在的问题,并对未来农机导航技术的发展做出了展望,指出采用卫星导航技术,开展农机地头自动转向控制、障碍物探测及主动避障、多机协同导航等高级导航技术研究,以及引入先进的物联网技术,是现代农机自动导航技术发展的主要趋势。%The automatic guidance of agriculture vehicles is a key technology in precision agriculture and widely used in agriculture production. A review of the recent research in agriculture vehicle automatic guidance is presented in this paper, focusing on the position measurement, agriculture models and path tracking. And

  5. Classification of Water Masses and Targeted Sampling of Ocean Plankton Populations by an Autonomous Underwater Vehicle

    Science.gov (United States)

    Zhang, Y.; Ryan, J. P.; Bellingham, J. G.; Harvey, J.; McEwen, R.; Chavez, F.; Scholin, C.

    2011-12-01

    Autonomous underwater vehicles (AUVs) are playing an increasingly active role in oceanographic surveys due to their mobility, efficiency, and growing intelligence. The Dorado AUV is equipped with a comprehensive suite of in situ sensors and ten 1.8-liter water samplers (called "gulpers"). During an October 2010 experiment in Monterey Bay, the AUV ran our autonomous peak-capture algorithm to acquire chlorophyll/backscatter peak samples from a phytoplankton bloom, allowing biologists to successfully monitor fluctuations in harmful microalgae (Psuedo-nitzschia spp.), the toxin they produce (domoic acid), and co-occurring zooplankton (invertebrate larvae and copepods) over space and time. For further investigations of the complex marine ecosystem in northern Monterey Bay, we set a more challenging goal: when the AUV flies from an upwelling shadow region (stratified water column) through an upwelling front into newly upwelled water, can it autonomously distinguish among water columns with different vertical structures and accordingly sample plankton populations on either side of, as well as within, the upwelling front? To achieve this goal, we have developed two new algorithms, one for distinguishing upwelling water columns from stratified water columns based on the vertical homogeneity of temperature, and the other for detecting an upwelling front based on the horizontal gradient of temperature. For acquiring targeted water samples, the 10 gulpers are appropriately allocated to the two distinct water columns and the front. Lockout time intervals between triggerings are set to prevent "dense triggerings". During our June 2011 experiment, the Dorado AUV flew westward from an upwelling shadow region (stratified water column) through an upwelling front, and into an upwelling water column. Three gulpers were allocated to the stratified water column, four to the front, and the remaining three to the upwelling water column. The AUV successfully detected and acquired targeted

  6. Automatic classification and robust identification of vestibulo-ocular reflex responses: from theory to practice: introducing GNL-HybELS.

    Science.gov (United States)

    Ghoreyshi, Atiyeh; Galiana, Henrietta

    2011-10-01

    The Vestibulo-Ocular Reflex (VOR) stabilizes images of the world on our retinae when our head moves. Basic daily activities are thus impaired if this reflex malfunctions. During the past few decades, scientists have modeled and identified this system mathematically to diagnose and treat VOR deficits. However, traditional methods do not analyze VOR data comprehensively because they disregard the switching nature of nystagmus; this can bias estimates of VOR dynamics. Here we propose, for the first time, an automated tool to analyze entire VOR responses (slow and fast phases), without a priori classification of nystagmus segments. We have developed GNL-HybELS (Generalized NonLinear Hybrid Extended Least Squares), an algorithmic tool to simultaneously classify and identify the responses of a multi-mode nonlinear system with delay, such as the horizontal VOR and its alternating slow and fast phases. This algorithm combines the procedures of Generalized Principle Component Analysis (GPCA) for classification, and Hybrid Extended Least Squares (HybELS) for identification, by minimizing a cost function in an optimization framework. It is validated here on clean and noisy VOR simulations and then applied to clinical VOR tests on controls and patients. Prediction errors were less than 1 deg for simulations and ranged from .69 deg to 2.1 deg for the clinical data. Nonlinearities, asymmetries, and dynamic parameters were detected in normal and patient data, in both fast and slow phases of the response. This objective approach to VOR analysis now allows the design of more complex protocols for the testing of oculomotor and other hybrid systems.

  7. Research of brain-computer interface automatic vehicle system based on SSVEP%基于SSVEP的脑-机接口自动车系统研究

    Institute of Scientific and Technical Information of China (English)

    赵丽; 孙永; 马彦臻; 何洋

    2011-01-01

    This paper mainly carried out proposes the research of SSVEP brain-computer interface automatic vehicle control systems,which describes the principles of the visual evoked potentials that used in brain-computer interface,and the single-chip is used to designs visual stimulation. Base on the LABVIEW platform, it also uses Hilbert Huang Transform to extract evoked potential vector continuously,which produces brain-computer interface control signals that can be applied to automatic vehicle control system to control the car around before and after exercise. According to a lot of experiments to verify,this sistem can send out the control commands that the correct rate is higher than 83% and can also send a command less than 5 seconds compared with the average time based on SSVEP,so it proves that the system is feasible and has a high application value.%阐述了视觉诱发电位用于脑-机接口的原理,系统采用单片机设计视觉刺激器,同时在LABVIEW平台上,利用希尔伯特黄变换实时提取诱发电位向量,产生脑机接口控制信号,并用于自动车控制系统,从而控制小车的前后左右运动.通过大量实验验证,设计的基于稳态视觉诱发电位的脑-机接口自动车控制系统,发送控制命令正确率高于83%,发送一个命令的平均时间低于5 s,证明该系统的方案是可行的,具有较高的应用价值.

  8. Research on Opinion Monitoring Method Based on Automatic Text Classification%基于自动分类的网络舆情监测方法研究

    Institute of Scientific and Technical Information of China (English)

    赵浚淇

    2016-01-01

    当前互联网快速发展,网络社会与现实社会逐渐同步,网上网下事件的关联性提高,网络舆情也越来越能够及时反映现实社会中发生的事情。因此,网络舆情监测不仅能够了解民意,为相关决策部门制定方案提供参考,而且能够通过大数据分析,对突发事件进行及时预警。以互联网上微博、贴吧、论坛、新闻评论等信息作为对象,以实用性为原则,研究一种基于文本自动分类的网络舆情监测方法。该方法通过网络爬虫抓取互联网上的信息,并采用基于KNN算法的文本自动分类方式完成网络舆情自动分类,最后通过实验验证了该方法的实用性。%With the rapid development of the Internet ,the cyber society and the real society are gradually synchronized , and incidents on the Internet have become more and more related to incidents offline .Public opinions online are also be‐coming more capable of reflecting social events in time .Therefore ,network public opinion monitoring not only helps to in‐form public opinions ,providing references for the decision -making departments in concern ,but also gives timely warning of unexpected events thanks to large data analysis .This study ,focusing on Internet information such as micro blog ,post bar ,forum and news comments ,explores ,on the principle of applicability ,an Internet public opinion monitoring method which is based on automatic text classification .A web crawler is applied to collect information on the Internet ,and the KNN algorithm is used to conduct the automatic classification of online public opinions .The applicability of this method is verified by experiments at the end of the current study .

  9. Modularity, adaptability and evolution in the AUTOPIA architecture for control of autonomous vehicles. Updating Mechatronics of Automatic Cars

    OpenAIRE

    Pérez Rastelli, Joshué; González, Carlos; Milanés, Vicente; Onieva, Enrique; Godoy, Jorge; Pedro, Teresa de

    2009-01-01

    International audience Computer systems to carry out control algorithms on autonomous vehicles have been developed in recent years. However, the advances in peripheral devices allow connecting the actuator controllers to the control system by means of standard communication links (USB, CAN, Ethernet ... ).The goal is to permit the use of standard computers. In this paper, we present the evolution of AUTOPIA architecture and its modularity and adaptability to move the old system based on IS...

  10. Automatic Parallelization of Classification Systems Based on Support Vector Machines: Comparison and Application to the JET Database

    International Nuclear Information System (INIS)

    Full text of publication follows: The use of learning systems for data analysis in Fusion is growing. In learning machines, the larger is the training dataset the better model can be obtained. Therefore, the training phase can demand a lot of computational time in mono-processor computers (hours or even days of Cpu time). To overcome this situation, codes should be parallelized. This article describes two general purpose parallelization techniques of a classification system based on Support Vector Machines (SVM): 1) Spread-Kernel Optimization and 2) Cascade SVM. Both of them have been applied to the recognition of the L-H confinement transition in JET. The parallel programming has been developed with MPI (Message Passing Interface) using the library LibSVM and it allows the use of different kernels. Both methods have been analyzed and validated in three parallel architectures with an arbitrary number of processors. The efficiency of parallel applications is maximized when the workload is evenly distributed among processors and the overhead introduced in the parallel processing is minimized: the cost of communication and synchronization operations must be kept as low as possible. The parallel performance based on scalability factor and processing time are shown with a benchmark dataset. Success rate for the L-H transition in JET is about 99% and the computation time has been decreased by a factor 1000. (authors)

  11. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2015-09-01

    Full Text Available Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS or terrestrial laser scanner (TLS point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

  12. Implementation of Fuzzy Logic with High Security Registration Plate (HSRP for Vehicle Classification and Checking in TollPlaza

    Directory of Open Access Journals (Sweden)

    V. Sathya

    2012-10-01

    Full Text Available In Automobile Industries, to use of High Security Registration plate (HSRP is still a challenging problem. There are more options to misuse the vehicle and exchange its engine, chassis, gear box, axle etc., In an existing system, the Regional Transport Office (RTO only determine an abstract of the vehicle and its owner. The vehicles are classified using piezo sensor and inductive loop systems. The toll-plaza is used only collected fees from the vehicles for maintain the quality roads. There are no authorized agencies allotted to identify the vehicle checking and no possibilities to control the vehicle overloading. The proposed system, toll-plaza will be act as a multi-plaza. Vehicles are classified with weight and speed. Then it is checking in toll-plaza either passed or checked. In this paper, The system uses illumination (such as Infrared and a camera to take the image of the front or rear of the vehicle, then an extracts the plate information. This data is used for enforcement and it can be used to open a gate if the vehicle is checked with RTO data in toll-plaza. In vehicle checking, we develop new rules using a fuzzy logic to improve the performance. The features of this system are implemented in the upgrading vehicles only. It is used to control the overloading to maintain road safety and to identify the theft vehicle to reduce the crime and terrorism. As Bharat Stage Emission (BSE standard vehicles are implemented in India very aggressively. The emission standard vehicles are serviced only in authorized service centre not for doing and end root machines.

  13. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    OpenAIRE

    P. Javier Herrera; Gonzalo Pajares; María Guijarro

    2009-01-01

    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the D...

  14. High-Resolution, Semi-Automatic Fault Mapping Using Umanned Aerial Vehicles and Computer Vision: Mapping from an Armchair

    Science.gov (United States)

    Micklethwaite, S.; Vasuki, Y.; Turner, D.; Kovesi, P.; Holden, E.; Lucieer, A.

    2012-12-01

    Our ability to characterise fractures depends upon the accuracy and precision of field techniques, as well as the quantity of data that can be collected. Unmanned Aerial Vehicles (UAVs; otherwise known as "drones") and photogrammetry, provide exciting new opportunities for the accurate mapping of fracture networks, over large surface areas. We use a highly stable, 8 rotor, UAV platform (Oktokopter) with a digital SLR camera and the Structure-from-Motion computer vision technique, to generate point clouds, wireframes, digital elevation models and orthorectified photo mosaics. Furthermore, new image analysis methods such as phase congruency are applied to the data to semiautomatically map fault networks. A case study is provided of intersecting fault networks and associated damage, from Piccaninny Point in Tasmania, Australia. Outcrops >1 km in length can be surveyed in a single 5-10 minute flight, with pixel resolution ~1 cm. Centimetre scale precision can be achieved when selected ground control points are measured using a total station. These techniques have the potential to provide rapid, ultra-high resolution mapping of fracture networks, from many different lithologies; enabling us to more accurately assess the "fit" of observed data relative to model predictions, over a wide range of boundary conditions.igh resolution DEM of faulted outcrop (Piccaninny Point, Tasmania) generated using the Oktokopter UAV (inset) and photogrammetric techniques.

  15. 基于视频的行人车辆检测与分类%Pedestrian-vehicle Detection and Classification Based on Video

    Institute of Scientific and Technical Information of China (English)

    杨阳; 唐慧明

    2014-01-01

    针对传统智能监控中行人车辆检测与分类算法存在目标分割不完整、分类准确率低等问题,提出一种基于视频的行人车辆检测与分类算法。利用领域信息动态调整置信区间构造混合高斯模型,采用卡尔曼滤波预测目标下一帧的位置。通过自适应EM聚类方法提取目标长宽比和面积作为特征,将目标分为行人和车辆。在模型估计过程中假设相邻帧目标做匀速直线运动,推导出目标面积变化满足线性关系,并对目标跟踪和分类进行修正,进一步提高检测准确性。实验结果表明,该算法的人车检测准确率达到90%以上,分类准确率达到80%以上。%Aiming at the problem of incomplete target segmentation and low classification accuracy of traditional pedestrian-vehicle detection and classification algorithm in intelligent monitoring,this paper presents a pedestrian-vehicle detection and classification algorithm based on video. The algorithm dynamically adjusts confidence intervals for constructing Gaussian mixture model using neighborhood information,and uses the Kalman filter to predict the position of the target in the next frame. It extracts the target aspect ratio and area through adaptive EM clustering as a feature,then divides target into pedestrians and vehicles. Assume that target makes the uniform linear motion in adjacent frame and derive the target area to meet the linear relationship change. Thus target tracking and classification can be modified to improve the detection accuracy in the end. Experimental result show that the algorithm detection rate is over 90% and classification rate is over 80% .

  16. Automatic Number Plate Recognition System

    OpenAIRE

    Rajshree Dhruw; Dharmendra Roy

    2014-01-01

    Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. The objective is to design an efficient automatic authorized vehicle identification system by using the Indian vehicle number plate. In this paper we discus different methodology for number plate localization, character segmentation & recognition of the number plate. The system is mainly applicable for non standard Indian number plates by recognizing...

  17. 空客A320系列飞机APS3200型APU自动关车故障分析%Failure Analysis of APS3200 Type APU Automatic Vehicle Shutdown of the Airbus A320 Series

    Institute of Scientific and Technical Information of China (English)

    张绪勤

    2016-01-01

    In this paper, by analyzing the troubleshooting process of APS3200 Type APU Automatic Vehicle Shutdown of the Airbus A320 Series occurring repeatedly, it proposed troubleshooting and maintenance tips to the APU shutdown automatic vehicle shutdown appearing in the operation of the aircraft power supply switching.%本文通过对空客A320系列飞机APS3200型APU反复出现自动关车故障的排故过程进行分析,对飞机运行中供电切换时出现APU自动关车现象提出排故建议和维护提示。

  18. Practical automatic Arabic license plate recognition system

    Science.gov (United States)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

    Since 1970's, the need of an automatic license plate recognition system, sometimes referred as Automatic License Plate Recognition system, has been increasing. A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognition systems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognition system is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognition system that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.

  19. 基于深度卷积神经网络的车型识别研究%Deep convolution neural networks for vehicle classification

    Institute of Scientific and Technical Information of China (English)

    邓柳; 汪子杰

    2016-01-01

    近年来,深度学习中的卷积神经网络已经广泛运用于图像识别领域,它不仅显著提升了识别准确率,同时在特征提取速度方面也优于许多传统方法。针对高速公路环境下的车型识别问题,引入卷积神经网络(CNNs)理论,设计相应特征提取算法,并结合 SVM分类器构建识别系统。通过对高速公路上主要三种车型(小车、客车、货车)的分类实验显示,该方法在识别精度及速度上均取得了较显著的提高。%In recent years,the deep convolution neural network (CNN),a state-of-the-art deep learning method,has been widely used in the field of image recognition.It can not only significantly improve the recognition accuracy,but also superior to many traditional algorithms in terms of feature extraction speed.This paper firstly introduced the CNN for the highway vehi-cle recognition.It constructed a vehicle recognition system by using a proposed deep CNN based feature extraction method and the SVMclassifier.The classification results of three major types of vehicles (cars,buses,trucks)on the highway show that significant improvements are achieved in both classification accuracy and speed.

  20. CLASSIFICATION OF URBAN FEATURE FROM UNMANNED AERIAL VEHICLE IMAGES USING GASVM INTEGRATION AND MULTI-SCALE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    M. Modiri

    2015-12-01

    Full Text Available The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

  1. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  2. 浅析全自动无人驾驶对地铁车辆基地设计的影响%Analysis of the Impact of Automatic Driverless Metro Vehicle Base Design

    Institute of Scientific and Technical Information of China (English)

    徐彪

    2015-01-01

    This paper analyzes the realization of the need for automatic driverless metro vehicle base functions to Beijing Subway Line M17 sub-canal south parking lot design, for example, described its impact on metro vehicle base design, is also recommended improve the interface between the professional coordination in the design process.%简要分析了全自动无人驾驶的实现对地铁车辆基地各项功能的需求,以北京地铁M17线次渠南停车场设计为例,阐述了其对地铁车辆基地设计的影响,同时,建议在设计过程中提高各专业间的接口协调性。

  3. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

    NARCIS (Netherlands)

    Murphy, K.; van Ginneken, B.; Schilham, A. M. R.; de Hoop, B. J.; Gietema, H. A.; Prokop, M.

    2009-01-01

    A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neig

  4. An Approach to Driverless Vehicles in Highways

    OpenAIRE

    Milanés, Vicente; Onieva, Enrique; Pérez Rastelli, Joshué; Godoy, Jorge; Villagra, Jorge

    2011-01-01

    International audience This paper presents AUTOPIA program results towards autonomous vehicles in highways. Based on our previous experience in automatic driving systems, a high-speed controller has been developed to perform vehicle's guidance autonomously. The map is generated in real time by the leading vehicle via vehicle-to-vehicle communications, permitting the vehicle equipped with the automatic system driving in any real circumstance in highways as can be lane-change maneuver. The s...

  5. Automatic Brain Lesion Detection and Classification Based on Diffusion-Weighted Imaging using Adaptive Thresholding and a Rule-Based Classifier

    Directory of Open Access Journals (Sweden)

    N.M. Saad

    2014-12-01

    Full Text Available In this paper, a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed. Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke, solid tumor, chronic stroke, and necrosis are analyzed. The analysis is divided into four stages: pre-processing, segmentation, feature extraction, and classification. In the detection and segmentation stage, the image is divided into 8x8 macro-block regions. Adaptive thresholding technique is applied to segment the lesion’s region. Statistical features are measured on the region of interest. A rulebased classifier is used to classify four types of lesions. Jaccard’s similarity index of the segmentation results for acute stroke, solid tumor, chronic stroke, and necrosis are 0.8, 0.55, 0.27, and 0.42, respectively. The classification accuracy is 93% for acute stroke, 73% for solid tumor, 84% for chronic stroke, and 60% for necrosis. Overall, adaptive thresholding provides high segmentation performance for hyper-intensity lesions. The best segmentation and classification performance is achieved for acute stroke. The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions.

  6. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

    OpenAIRE

    Borja Rodríguez-Cuenca; Silverio García-Cortés; Celestino Ordóñez; Maria C. Alonso

    2015-01-01

    Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical o...

  7. Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

    OpenAIRE

    Martinis, Sandro

    2010-01-01

    This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activi...

  8. Automatic vehicle detection using spaceborne optical remote sensing images in city area%城市街区星载光学遥感图像车辆目标自动检测方法

    Institute of Scientific and Technical Information of China (English)

    李昭慧; 张建奇

    2014-01-01

    It is difficult to detect vehicles in city area by using paceborne optical remote sensing images, because the background in city area is too complex. In this paper, an automatic vehicle detection method was proposed to address the issue by using background segmentation method. Firstly, the physical property of the vegetation was analyzed and used to suppress the vegetation background of a scene by using the multi- spectral information of the scene. Next, the reflectance characteristics of city area cover types were analyzed. Based on the reflectance characteristics of building roofs and roads, the building background in the scene was removed by employing the binary morphological method on the panchromatic band image. Finally, the famous RX algorithm was introduced to detect the vehicles on the vegetation and building background suppressed image. The proposed method is applied to the actual Quickbird image for vehicle target detection. The results show that the proposed method has strong robustness, high efficiency, and automatic characteristics, and can be used for vehicle detection in city area.%针对星载光学遥感图像城市街区复杂背景问题,提出一种车辆目标自动检测方法。首先,利用场景中植被背景的物理属性,通过多光谱波段抑制场景中的植被背景,然后,在分析城市街区地物形态反射率特性的基础上,利用全色波段并结合二值形态学方法抑制场景中的建筑物,最后,引入著名的RX算法对抑制后的图像进行车辆目标检测。将文中提出的方法应用于实际Quickbird影像的车辆目标检测,结果表明所提出的方法具有鲁棒性强,执行效率高,不需要人工辅助等方面的特点,可用于城市街区车辆目标的自动检测。

  9. Automatic acquisition and classification system for agricultural network information based on Web data%基于Web数据的农业网络信息自动采集与分类系统

    Institute of Scientific and Technical Information of China (English)

    段青玲; 魏芳芳; 张磊; 肖晓琰

    2016-01-01

    The purpose of this study is to obtain agricultural web information efficiently, and to provide users with personalized service through the integration of agricultural resources scattered in different sites and the fusion of heterogeneous environmental data. The research in this paper has improved some key information technologies, which are agricultural web data acquisition and extraction technologies, text classification based on support vector machine (SVM) and heterogeneous data collection based on the Internet of things (IOT). We first add quality target seed site into the system, and get website URL (uniform resource locator) and category information. The web crawler program can save original pages. The de-noised web page can be obtained through HTML parser and regular expressions, which create custom Node Filter objects. Therefore, the system builds a document object model (DOM) tree before digging out data area. According to filtering rules, the target data area can be identified from a plurality of data regions with repeated patterns. Next, the structured data can be extracted after property segmentation. Secondly, we construct linear SVM classification model, and realize agricultural text classification automatically. The procedures of our model include 4 steps. First of all, we use segment tool ICTCLAS to carry out the word segment and part-of-speech (POS) tagging, followed by combining agricultural key dictionary and document frequency adjustment rule to choose feature words, and building a feature vector and calculating inverse document frequency (IDF) weight value for feature words; lastly we design adaptive classifier of SVM algorithm. Finally, the perception data of different format collected by the sensor are transmitted to the designated server as the source data through the wireless sensor network. Relational database in accordance with specified acquisition frequency can be achieved through data conversion and data filtering. The key step of

  10. Automatic sequences

    CERN Document Server

    Haeseler, Friedrich

    2003-01-01

    Automatic sequences are sequences which are produced by a finite automaton. Although they are not random they may look as being random. They are complicated, in the sense of not being not ultimately periodic, they may look rather complicated, in the sense that it may not be easy to name the rule by which the sequence is generated, however there exists a rule which generates the sequence. The concept automatic sequences has special applications in algebra, number theory, finite automata and formal languages, combinatorics on words. The text deals with different aspects of automatic sequences, in particular:· a general introduction to automatic sequences· the basic (combinatorial) properties of automatic sequences· the algebraic approach to automatic sequences· geometric objects related to automatic sequences.

  11. Automatic Water Sensor Window Opening System

    KAUST Repository

    Percher, Michael

    2013-12-05

    A system can automatically open at least one window of a vehicle when the vehicle is being submerged in water. The system can include a water collector and a water sensor, and when the water sensor detects water in the water collector, at least one window of the vehicle opens.

  12. Automatic classification of air pollution measurements of the LIMBA network; Automatische Klassifizierung der Luftschadstoff-Immissionmessungen aus dem LIMBA-Messnetz

    Energy Technology Data Exchange (ETDEWEB)

    Wiegand, G.; Pfaefflin, F.

    2002-07-01

    The EU directive 96/62/EC on air quality and its daughter directives require the knowledge of statistical parameters of time series in order to asses air quality. Calculations with models are explicitly mentioned in the directive 96/62/EC. One category of such models is statistical modeling of e.g. the relationship of the annual mean value of NO{sub x} and a percentile of NO{sub 2}. In doing so, the different temporal characteristics of the time series of concentrations are utilized to determine homogeneous groups of time series of concentrations. Thus, the variance of the model results can be significantly reduced. The file system of the UBA (Federal Environmental Agency) contains time series for more than 1'000 measurement stations. The present classification scheme of the UBA provides a classification for some of the stations in one of the four classes 'city', 'country', 'traffic', and 'mountain'. In the new approach, the classification scheme of the UBA is based on a comprehensible and objective procedure. With this procedure, all available stations are classified into one of the four groups and a statistical model for all limit values of the 1{sup st} and 2{sup nd} daughter directive is derived. The 'zone' is a central geographical object for the assessment according to directive 96/62/EC. Based on municipal boundaries (Gemeindegrenzen), a tool for ArcView GIS has been developed with the following functionality: visualization and modification of polygon geo-objects, consolidation and merging of assessment values, and simple creation of thematic maps and layouts for printing. The long range influence of clusters of measurement stations is a weakness of the inverse distance weighted interpolation method (IDW). A remedy for this weakness has been programmed and provided to the UBA as a dynamic link library (DLL). (orig.)

  13. 航空影像辅助下的城区机载LiDAR汽车目标检测方法%Automatic Urban Vehicle Detection from Airborne LiDAR Data with Aerial Image

    Institute of Scientific and Technical Information of China (English)

    孙美玲; 李永树; 陈强

    2014-01-01

    The appearance of LiDAR technology provides a new method for automatic vehicle detection.In order to detect vehicle object from LiDAR data,according to the property features of different objects,a new method of automatic urban vehicle detection from airborne LiDAR data with aerial image is proposed in this paper.Firstly,it is classified ground and non-ground points using LiDAR filtering with morphological opening by reconstruction.Secondly,with the help of aerial image and its Normalized Differential Vegetation Index (NDVI) feature,it could classify LiDAR non-ground points into vegetation and non-vegetation objects.Finally,On the basis of non-vegetation objects,it could separate vehicle objects automatically from other non-vehicle objects by shape feature and height property.Three regions has been used to verify the feasibility and reliability of this method.The experiment results show that the proposed method can effectively extract vehicle objects.The mean of correctness and completeness of this method can reach 95% and 85 % respectively,which can meet the practical requirements.%机载激光雷达(LiDAR)技术的出现为地面汽车目标检测提供了新的途径.为了从机载Li-DAR点云数据中提取汽车对象,根据不同地物的属性特征,提出了一种航空影像辅助下的城区机载LiDAR汽车目标检测方法.首先利用形态学开重建滤波完成地面和地物的分类,然后在地物点的基础上结合正射影像,通过归一化植被指数(NDVI)特征完成对植被和非植被地物的初步分类,最后在非植被地物的基础上,根据地物对象的形状特征及高程信息完成汽车和建筑物及阴影植被等非汽车对象的分类,从而完成汽车目标的提取工作.3个实验区的计算结果表明:该方法能有效从LiDAR点云中提取汽车目标,正确度和完整度的均值分别为95%和85%,满足实用性要求.

  14. Vehicle detection from high-resolution aerial images based on superpixel and color name features

    Science.gov (United States)

    Chen, Ziyi; Cao, Liujuan; Yu, Zang; Chen, Yiping; Wang, Cheng; Li, Jonathan

    2016-03-01

    Automatic vehicle detection from aerial images is emerging due to the strong demand of large-area traffic monitoring. In this paper, we present a novel framework for automatic vehicle detection from the aerial images. Through superpixel segmentation, we first segment the aerial images into homogeneous patches, which consist of the basic units during the detection to improve efficiency. By introducing the sparse representation into our method, powerful classification ability is achieved after the dictionary training. To effectively describe a patch, the Histogram of Oriented Gradient (HOG) is used. We further propose to integrate color information to enrich the feature representation by using the color name feature. The final feature consists of both HOG and color name based histogram, by which we get a strong descriptor of a patch. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm for vehicle detection from aerial images.

  15. Finite Element Analysis on Automatic Transmission Box of Electric Vehicle based on Solidworks Simulation%基于Solidworks Simulation纯电动汽车变速器箱体有限元分析

    Institute of Scientific and Technical Information of China (English)

    王艳

    2012-01-01

    In early development, the transmission ratio has been designed based on the utilization rate of energy for an electric vehicle. The transmission scheme and the size of automatic transmission box have also been obtained. In this paper. Solidworks structure modeling of transmission box was constructed. Using Solidworks Simulation, the finite element analysis was gained on both the front cover and the back cover of the transmission box. The maximum stress was obtained and the location where the maximum stress took place was found. The results show that the design of the automatic transmission not only meets the optimal energy utilization rate but also meets the requirements of the static strength.%前期已经对某电动汽车进行基于能量利用率的传动比设计,设计出自动变速器传动方案及其尺寸。本文采用Solidworks软件建立箱体结构模型,并以Solidworks Simulation对箱体前盖和后盖分别进行有限元分析,得出最大应力值及出现位置。结果表明,所设计的自动变速器既满足能量利用率最优又符合静强度要求。

  16. ARCA: Traffic Classification Method Based on Automatic Reverse and Cluster Analysis%融合自动化逆向和聚类分析的协议识别方法

    Institute of Scientific and Technical Information of China (English)

    李城龙; 薛一波; 汪东升

    2012-01-01

    网络流分类与协议识别是网络管理的前提和必要条件,但是越来越多加密协议的出现,使得传统的流分类方法失效.针对加密协议的协议识别问题,提出了一种融合自动化逆向分析技术和网络消息聚类分析技术的新型分类方法(automatic reverse and message analysis,ARCA).该方法通过自动化逆向分析技术获得网络协议的结构特征;再利用网络消息聚类分析技术,获得网络协议的交互过程;最后将网络协议的结构特征与交互过程用于加密协议流量的识别和分类检测.该方法不依赖于网络包的内容检测,能够解决协议加密带来的识别问题.通过对多个加密协议(如迅雷、BT、QQ和GYalk等)真实流量的实验,其准确率和召回率分别高于96.9%和93.1%,而且只需要检测流量中0.9%的字节内容即可.因此,ARCA方法能够对各类加密协议流量进行有效和快速的识别.%Traffic classification and protocol identification are the premise and the essential condition to effective network management. However, more and more encrypted protocols make traditional traffic classification methods less effective. To address the issue, this paper proposes an automatic reverse and message analysis (ARCA) method to identify encryption protocols. Different from traditional classification approaches, the proposed method exploits the protocol structure by automatically and reversely analyzing the target protocol, obtains the protocol interactive process by clustering messages, then identifies the protocol using the protocol structure and interactive process together. This method does not need to check payload, so it can classify the encrypted protocols. The paper evaluates the efficacy and accuracy of ARCA with real world traffic, such as encryption protocols Thunder, BitTorrent, QQ and GTalk. The experimental results show that the accuracy rates and the recall rates are over 96.9% and 93.1% respectively and

  17. Automatic Tracking System of Vehicles Based on GPS and GSM%基于GPS和GSM的车辆自动跟踪系统

    Institute of Scientific and Technical Information of China (English)

    袁卫

    2011-01-01

    采用STC公司的STC12C5A60S2单片机为控制核心,在车辆被盗的情况下,利用GPS卫星定位系统确定车辆的位置,然后通过GSM网络将车辆的位置以短息的方式发送到车主指定的手机中,车主可远程控制使系统自动切断汽车内的点火电路,从而实现防盗功能。%The paper provides a system to ascertain vehicles' position by Global Position System in the case of vehicle theft, which taking STC12C5A60S2 microcomputer of STC company as control core and making use of GSM network to send position information of vehicl

  18. Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

    Science.gov (United States)

    Chen, Antong; Dogdas, Belma; Mehta, Saurin; Bagchi, Ansuman; Wise, L. David; Winkelmann, Christopher

    2014-03-01

    High-throughput micro-CT imaging has been used in our laboratory to evaluate fetal skeletal morphology in developmental toxicology studies. Currently, the volume-rendered skeletal images are visually inspected and observed abnormalities are reported for compounds in development. To improve the efficiency and reduce human error of the evaluation, we implemented a framework to automate the evaluation process. The framework starts by dividing the skull into regions of interest and then measuring various geometrical characteristics. Normal/abnormal classification on the bone segments is performed based on identifying statistical outliers. In pilot experiments using rabbit fetal skulls, the majority of the skeletal abnormalities can be detected successfully in this manner. However, there are shape-based abnormalities that are relatively subtle and thereby difficult to identify using the geometrical features. To address this problem, we introduced a model-based approach and applied this strategy on the squamosal bone. We will provide details on this active shape model (ASM) strategy for the identification of squamosal abnormalities and show that this method improved the sensitivity of detecting squamosal-related abnormalities from 0.48 to 0.92.

  19. Automatic approach to develop driving cycles for estimation of vehicle emissions%机动车尾气排放测算行驶周期的自动建立方法

    Institute of Scientific and Technical Information of China (English)

    张袺; 赵鸿铎; 赵队家

    2015-01-01

    In order to generate a driving cycle for evaluation of vehicles emissions,extensive emission data and driving activity data are collected by using a Portable Emission Measurement System (PEMS)and a GPS device.The collected data are preprocessed and the evaluation criterion for microtrips is proposed.Two heuristic algorithms, Genetic Algorithm (GA ) and Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)are given to search the best microtrips automatically.The prediction of vehicle emissions using generated driving cycle is validated.Research results show that both heuristic algorithms are able to solve the problem of automatic generation of driving cycles.The driving cycle developed by GA is more representable and it can predict the total vehicle emissions with a relatively small error.Therefore, GA is recommended for the generation of driving cycles in other cities.%为了建立用于机动车尾气排放测算的行驶周期,利用便携车载尾气采集装置(PEMS)系统和 GPS 装置进行了大量的机动车尾气排放和驾驶活动数据采集,并对数据进行了预处理,提出了短行程的评价标准,给出了最优短行程自动搜索的2种启发式算法(遗传算法(GA)和离散同步扰动随机逼近(DSPSA)),对建立的行驶周期进行了预测尾气排放总量的有效性验证。结果表明:2种启发式算法均能有效地解决行驶周期自动建立问题,GA 算法优化得到的行驶周期更有代表性,该行驶周期能以相对较小的误差预测尾气排放总量。建议将 GA 算法作为开发其他城市的行驶周期的算法工具。

  20. Research on Bench-marking Analysis Model of Competitive Intelligence Integrated Text Automatic Classification%融合文本自动分类的竞争情报定标比超分析模型研究

    Institute of Scientific and Technical Information of China (English)

    张玉峰; 黄姮

    2011-01-01

    本文分析了传统定标比超方法的思想和缺陷,提出将传统情报分析方法与智能分析技术相结合,构建了融合文本自动分类的竞争情报定标比超分析模型。本文提出构建定标比超内容层次指标体系,将其作为文本自动分类的分类体系。两种方法相辅相成、相互优化,实现竞争情报的良性循环型、科学的智能分析。进而,深入研究了该模型的功能任务和情报分析过程与算法。最后,从科学性、时效性、全面性、准确性和动态性方面对该模型进行了性能评价。%This paper analyses the theories and defects of traditional bench-marking and proposes a bench-marking analysis model of competitive intelligence integrated text automatic classification,which integrates traditional intelligence analytical method and inte

  1. 基于红外特性的自动垃圾分类综合处理技术%Comprehensive treatment automatic garbage classification technology based on infrared characteristic

    Institute of Scientific and Technical Information of China (English)

    陈璇; 方学梅; 李婷婷; 黄晓梅

    2014-01-01

    The paper is based on the automatic classification system of waste heat is the main use of the heat dissipation of different garbage distinguish main waste including food,plastic.And the use of metal detectors to detect metal,distinguish the glass using photosensitive sensor.Using the simulated robot will separate the rubbish into a designated area.%生活垃圾的分类和处理为当前社会迫切需要解决的问题,随之产生自动分类的概念。基于散热性的垃圾自动分类系统主要是利用了不同垃圾的散热性能不同区分主要垃圾包括食品类,纸类,塑料类。并利用金属探测仪来检测金属,利用光敏传感器区分玻璃。利用模拟机器手将分离的垃圾放入指定的区域。

  2. Application of adaptive and neural network computational techniques to Traffic Volume and Classification Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Fisher, H.N.; Jones, R.D.; Bisset, K.R.; Lee, L.A.

    1993-09-01

    We are developing a Traffic Volume and Classification Monitoring (TVCM) system based on adaptive and neural network computational techniques. The value of neutral networks in this application lies in their ability to learn from data and to form a mapping of arbitrary topology. The piezoelectric strip and magnetic loop sensors typically used for TVCM provide signals that are complicated and variable, and that correspond in indirect ways with the desired FWHA 13-class classification system. Further, the wide variety of vehicle configurations adds to the complexity of the classification task. Our goal is to provide a TVCM system featuring high accuracy, adaptability to wide sensor and envirorunental variations, and continuous fault detection. We have instrumented an experimental TVCM site, developed PC-based on-line data acquisition software, collected a large database of vehicles` signals together with accurate ground truth determination, and analyzed the data off-line with a neural net classification system that can distinguish between class 2 (automobiles) and class 3 (utility vehicles) with better than 90% accuracy. The neural network used, called the Connectionist Hyperprism Classification (CHC) network, features simple basis functions; rapid, linear training algorithms for basis function amplitudes and widths; and basis function elimination that enhances network speed and accuracy. Work is in progress to extend the system to other classes, to quantify the system`s adaptability, and to develop automatic fault detection techniques.

  3. Firing Dynamics Model Updating of Automatic Gun Weapon System in Certain Infantry Combat Vehicle%步兵战车自动炮武器系统发射动力学模型修正

    Institute of Scientific and Technical Information of China (English)

    张金忠; 苏忠亭; 徐达; 赵富全

    2014-01-01

    The automatic gun firing dynamics model was built up and the simulation model was updated by use of the support vector machine response surface method based on the actual firing experiment so as to analyze the influence factor of firing precision.The finite element models of the gun barrel structure and turret structure were established by means of finite element analysis method,and the rigid-flexible cou-pled firing dynamics models of the infantry combat vehicle were set up based on the restricted relationship between components and joints of the weapon system.The test system was established and the actual fir-ing experiment was carried out by use of picking up the typical structure vibration characteristics in the burst firing of automatic gun based on the same boundary conditions.Aimed at the errors between the simula-tion data and the test data,the automatic gun firing dynamics model was updated.The updating results showed that the model updating method can increase the precision of firing dynamic model and more accurately reflect the framework dynamic characteristics of infantry combat vehicle automatic gun during the course of firing.%为提高自动炮武器系统发射动力学模型精度,基于实弹射击试验建立支持向量机响应面,对仿真模型进行了修正。应用有限元分析方法建立了双炮身管结构和炮塔结构有限元模型,基于武器系统各部件间的约束关系建立了步兵战车刚柔耦合发射动力学模型;基于相同边界条件,选取自动炮连发射击中典型结构的振动特性搭建了测试系统并进行了实弹射击试验;针对仿真数据与试验数据误差,引入支持向量机响应面方法对步兵战车刚柔耦合发射动力学模型进行了修正。修正结果表明,基于支持向量机响应面的模型修正方法大幅提高了自动炮武器系统发射动力学模型的精度,更准确地反映了自动炮射击过程中的机构动态特性。

  4. 移动车载激光点云的道路标线自动识别与提取%Automatic Road Marking Detection and Extraction Based on LiDAR Point Clouds from Vehicle- Borne MMS

    Institute of Scientific and Technical Information of China (English)

    邹晓亮; 缪剑; 郭锐增; 李星全; 赵桂华

    2012-01-01

    The research focuses on LiDAR point clouds of road surface acquired from vehicle - borne mobile mapping system - Land- Mark. An automatic road marking detection and extraction method is proposed. Combining LiDAR features of retro, angle and distance with the properties of traffic marking, point clouds of road marking is extracted. The road marking is best fitted in a least squares poly- nomial fitting method and CAD map is generated for automatic detection. Based on the experimental data from Sick laser scanner mounted on LandMark system, the experimental results show the method is feasible and available.%对移动车载激光测量LandMark系统获取的路面激光点云数据进行研究,结合激光点云的回波反射率、扫描角,以及量测距离等特征信息与道路标线的属性信息,提出了一种基于车载激光点云的道路标线自动识别与提取算法。从点云中提取道路标线,采用最小二乘线性最优拟合算法对提取的标线点云进行拟合,生成道路标线的CAD轮廓线,实现道路标线的自动化识别。以移动车载LandMark系统的Sick激光扫描仪获取的路面激光点云为例进行实验,实验结果表明该方法的可行性和有效性。

  5. Improving Student Question Classification

    Science.gov (United States)

    Heiner, Cecily; Zachary, Joseph L.

    2009-01-01

    Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural…

  6. Advanced automatic target recognition for police helicopter missions

    Science.gov (United States)

    Stahl, Christoph; Schoppmann, Paul

    2000-08-01

    The results of a case study about the application of an advanced method for automatic target recognition to infrared imagery taken from police helicopter missions are presented. The method consists of the following steps: preprocessing, classification, fusion, postprocessing and tracking, and combines the three paradigms image pyramids, neural networks and bayesian nets. The technology has been developed using a variety of different scenes typical for military aircraft missions. Infrared cameras have been in use for several years at the Bavarian police helicopter forces and are highly valuable for night missions. Several object classes like 'persons' or 'vehicles' are tested and the possible discrimination between persons and animals is shown. The analysis of complex scenes with hidden objects and clutter shows the potentials and limitations of automatic target recognition for real-world tasks. Several display concepts illustrate the achievable improvement of the situation awareness. The similarities and differences between various mission types concerning object variability, time constraints, consequences of false alarms, etc. are discussed. Typical police actions like searching for missing persons or runaway criminals illustrate the advantages of automatic target recognition. The results demonstrate the possible operational benefits for the helicopter crew. Future work will include performance evaluation issues and a system integration concept for the target platform.

  7. Study on Ground Automatic Identification Technology for Intelligent Vehicle Based on Vision Sensor%基于视觉传感器的自主车辆地面自动辨识技术研究

    Institute of Scientific and Technical Information of China (English)

    崔根群; 余建明; 赵娴; 赵丛琳

    2011-01-01

    The ground automatic identification technology for intelligent vehicle is iaking Leobor-Edu autonomous vehicle as a test vector and using DH-HV2003UC-T vision sensor to collect image infarmaiion of five common lane roads( cobbled road, concrete road, dirt road, grass road, tile road) , then using MATLAB image processing module to perform coding compression, recovery reconstruction, smoothing, sharpening, enhancement, feature extraction and other related processing,then using MATLAB BP neural network module to carry on pattern recognition.Through analyzing the pattern recognition result, lt shows that the objective error is 20%, the road recognition rate has reached the intended requirement in the system,and it can be universally applied in the smart vehicle or robots and other related fields.%谊自主车辆地面自动辨识技术是以Leobot-Edu自主车辆作为试验载体,并应用DH-HV2003UC-T视觉传感器对常见的5种行车路面(石子路面、水泥路面、土壤路面、草地路面、砖地路面)进行图像信息的采集,应用Matlab图像处理模块对其依次进行压缩编码、复原重建、平滑、锐化、增强、特征提取等相关处理后,再应用Matlab BP神经网络模块进行模式识别.通过对模式识别结果分析可知,网络训练目标的函数误差为20%,该系统路面识别率达到预定要求,可以在智能车辆或移动机器人等相关领域普及使用.

  8. Automatic Classification of Kepler Threshold Crossing Events

    CERN Document Server

    McCauliff, Sean; Catanzarite, Joseph; Burke, Christopher J; Coughlin, Jeffrey L; Twicken, Joseph D; Tenenbaum, Peter; Seader, Shawn; Li, Jie; Cote, Miles

    2014-01-01

    The Kepler Science Operations Center detects interesting, exoplanet transit-like signals while searching over 211,000 distinct light curves. The mission has produced four catalogs of interesting objects with planet transit-like features known as Kepler Objects of Interest (KOI). The total number of objects with transit-like features identified in the light curves has increased to as many as approximately 18,000, just examining the first three years of data. This number of significant detections has become difficult for human beings to inspect by eye in a thorough and timely fashion. In order to accelerate the process by which new planet candidates are classified and to provide an independent assessment of planet candidates, we propose a machine learning approach to establish a preliminary list of planetary candidates ranked from most credible to least credible. The classifier must distinguish between three classes of detections: non-transiting phenomena, astrophysical false positives, and planet candidates. W...

  9. Method of information extraction of marbling image characteristic and automatic classification for beef%牛肉大理石花纹图像特征信息提取及自动分级方法

    Institute of Scientific and Technical Information of China (English)

    周彤; 彭彦昆

    2013-01-01

    . Light intensity was regulated through a light controller, and the distance between the camera lens and the beef samples was adjusted though translation stages in the image acquisition device. Collected images were automatically stored in the computer for further image processing. First, some methods such as image denoising, background removal, and image enhancement were adopted to preprocess the image to obtain a region of interest (ROI). In this step, the image was cropped to separate the beef from the background. Then, an iteration method was used to segment the beef area, obtain the beef marbling area and fat area. The redundant fat area was removed to extract an effective rib-eye region. Ten characteristic parameters of beef marbling namely, the rate of marbling area in the rib-eye region, the number of large grain fat, medium grain fat, small grain fat, total grain fat, the density of large grain fat, medium grain fat, small grain fat, total grain fat and, the evenness degree of fat distribution in the rib-eye region can reflect the amount of marbling and its distribution. So they were used to establish principal component regression (PCR) model. The PCR model result yielded a correction coefficient (Rv) of 0.88 and a standard error of prediction (SEP) of 0.56. And the PCR model showed that the rate of the marbling area in the rib-eye region had the greatest effect on the grade of beef marbling. Fisher discriminant functions were constructed based on the PCR model results to classify the grade of beef marbling. Experimental results showed that the classification accuracy was 97.0%in the calibration set and 91.2%in the prediction set. On this basis, a software system was developed for the automatic grading of beef marbling. A corresponding hardware device was also developed, controlled by the software system for real time application. The speed and accuracy of the algorithm were verified with theoretical analysis and a practical test. Through tests, the average

  10. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition......, a good metric is required to measure distance or similarity between feature points so that the classification becomes feasible. Furthermore, in order to build a successful classifier, one needs to deeply understand how classifiers work. This thesis focuses on these three aspects of classification...... and explores these challenging areas. The first focus of the thesis is to properly combine different local feature experts and prior information to design an effective classifier. The preliminary classification results, provided by the experts, are fused in order to develop an automatic segmentation method...

  11. 基于多传感器的一种新型车型车种分类识别系统%A New Models and Vehicles Classification System Based on Multi-sensor

    Institute of Scientific and Technical Information of China (English)

    吕成超; 刘伟铭

    2011-01-01

    On the basis of the numbers of axes and wheels , the height of headstock, wheelbase this four common characteristics, the new models and vehicles classification system adds some parameters which is closely associated with domestic vehicle and model category, such as wheelbase, wheel-width and two-dimensional shape of headstock. It respectively uses the pressure sensitive line sensor, double-infrared column measuring light curtains to judge the axes-base and wheelbase, collect two-dimensional shape of vehicle and determine their direction of travel , then it combine with BP neural network algorithm for information extraction from multiple sensor data to judge the model car categories. The experiment proves that the system can make a good performance in classifying the model and vehicles easily, accurately and quickly.%新型车型车种分类系统在轴数、轮数、车头高度、轴距这四个常用特征的基础上,增加了轮距、轮宽和车头二维形状等与国内车辆车型类别有较强关联的参数.分别采用压敏线列传感器、双红外列测量光幕来判断轮距和轴距,采集车辆的二维形状和确定其行进方向,并结合BP神经网络算法对多个传感器数据进行信息提取,判断出车型车种类别.试验证明该系统能实现车型车种类别的简便、准确和快速判定.

  12. An Experimental Comparative Study on Three Classification Algorithms

    Institute of Scientific and Technical Information of China (English)

    蔡巍; 王永成; 李伟; 尹中航

    2003-01-01

    Classification algorithm is one of the key techniques to affect text automatic classification system's performance, play an important role in automatic classification research area. This paper comparatively analyzed k-NN. VSM and hybrid classification algorithm presented by our research group. Some 2000 pieces of Internet news provided by ChinaInfoBank are used in the experiment. The result shows that the hybrid algorithm's performance presented by the groups is superior to the other two algorithms.

  13. Automatic Reading

    Institute of Scientific and Technical Information of China (English)

    胡迪

    2007-01-01

    <正>Reading is the key to school success and,like any skill,it takes practice.A child learns to walk by practising until he no longer has to think about how to put one foot in front of the other.The great athlete practises until he can play quickly,accurately and without thinking.Ed- ucators call it automaticity.

  14. Research on algorithm of automatically recognizing andpositioning road manhole covers based on vehicle-mounted sensors%基于车载传感器的路面井盖自动定位识别算法研究

    Institute of Scientific and Technical Information of China (English)

    刘建华

    2011-01-01

    The fast recognizing and positioning of municipal manhole covers is an important problem needed to be addressed for promoting digital management of modern cities. In view of the above problem, this paper proposed an algorithm, to automatically recognize and position road manhole covers under complex background in natural scene based on vehicle-mounted sensors. Taking the elliptical geometrical characteristic of manhole cover in perspective image captured by vehicle-mounted sensors as criterion, the algorithm firstly extracted edge information by employing vector edge detection method. Secondly constructed a contour list with boundary through contour tracing, then imitated and quickly generated all the possible elliptical targets in the contour list by means of least square fitting method. Subsequently eliminated the elliptical targets without relationship corresponding to road manhole covers according to their shape features, and last formed accurate results of recognition and position. Experiment shows that generally the algorithm is able to achieve real-time manhole cover targets recognition rapidly and effectively for images conforming to quality standard of data capturing.%市政井盖快速定位与识别是提升现代城市部件空间数字化管理水平需要解决的重要问题,针对该问题提出基于车载传感器的复杂背景下路面井盖目标自动定位识别算法.该算法以车载传感器获取的透视图像中井盖所具有的椭圆形几何特征为判据,先利用矢量边缘检测方法提取边缘信息,再运用轮廓跟踪法将边缘构造成轮廓链表,然后通过最小二乘法拟合与快速生成轮廓链表中可能存在的椭圆目标,并根据井盖的形状特征排除透视图中与路面井盖无对应关系的虚假椭圆目标,最终形成高精度定位识别结果.实证研究表明,对达到数据采集质量标准的图像,在一般情况下该算法能较好地实现其中市政井盖的实时定位识别.

  15. Automatic Classification of Lip Color Based on SVM in Traditional Chinese Medicine Inspection%基于支持向量机的中医望诊唇色自动分类

    Institute of Scientific and Technical Information of China (English)

    郑莉丽; 李晓强; 李福凤; 闫西平; 王忆勤; 王真真

    2011-01-01

    The lip color of a person is closely related to his or her health in the visual diagnosis of traditional Chinese medicine (TCM). The traditional method to judge the color of lips is through observing by a TCM doctor. The diagnosis result is affected not only by the doctor's knowledge and diagnosis experience. but also by the light, temperature and other environmental impacts. For these reasons. sometimes different doctors may make different judgement for the same lips. So it is urgently needed that an objective evaluation as reference for doctors can be obtained. A method based on support vector machine (SVM) that classifies lip color by computer automatically is presented in the present paper. Firstly, nine features of lip color in Hue, Saturation and Intensity (HSI) color space were extracted.Then, according to different combinations of these features five different experiments were conducted. By comparing the results of these experiments, it was discovered that the mean value is one of the most important features for the lip color. The overall effect of classification is better when the mean value and variance of HSI were chosen than other characteristics. In addition. experiments results demonstrated that the accuracy rate of classification is not improved when more features were adopted. The objective of the present paper is to select the appropriate characteristics and to combine them effectively to classify lip colors.%在中医望诊理论中,口唇的颜色与人体的健康状况息息相关.传统的口唇颜色判断方法主要依靠医生目视观察进行判断分析,其诊断结果既受医生知识水平、诊断经验的限制,又受当时光线、温度等外部环境的影响,缺乏客观评价依据.本文提出一种基于支持向量机(SVM)的唇色自动分类方法:首先,在HSI颜色空间中提取9个唇色特征;然后,选择不同的特征组合对唇色进行分类.通过比较实验结果,发现均值特征是区别唇色的重要

  16. Single-Frame Terrain Mapping Software for Robotic Vehicles

    Science.gov (United States)

    Rankin, Arturo L.

    2011-01-01

    This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each

  17. Supervised Ensemble Classification of Kepler Variable Stars

    CERN Document Server

    Bass, Gideon

    2016-01-01

    Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. We present an expansion of existing work on the field by analyzing variable stars in the {\\em Kepler} field using an ensemble approach, combining multiple characterization and classification techniques to produce improved classification rates. Classifications for each of the roughly 150,000 stars observed by {\\em Kepler} are produced separating the stars into one of 14 variable star classes.

  18. Nominal classification

    OpenAIRE

    Senft, G.

    2007-01-01

    This handbook chapter summarizes some of the problems of nominal classification in language, presents and illustrates the various systems or techniques of nominal classification, and points out why nominal classification is one of the most interesting topics in Cognitive Linguistics.

  19. A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

    Science.gov (United States)

    Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

    2016-01-01

    In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model. PMID:26907278

  20. Automatic Morphometry of Nerve Histological Sections

    OpenAIRE

    Romero, E.; Cuisenaire, O.; Denef, J.; Delbeke, J.; Macq, B.; Veraart, C.

    2000-01-01

    A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified usi...

  1. STUDY ON SHIFT SCHEDULE OF AUTOMATIC TRANSMISSION TO IMPROVE ENGINEERING VEHICULAR EFFICIENCY

    Institute of Scientific and Technical Information of China (English)

    Gong Jie; Zhao Dingxuan; Huang Haidong; Gong Wenbin; Chen Ying

    2004-01-01

    New shift schedule for automatic transmission is proposed from the point of view of saving energy.The bench-test of automatic shift adopting this shift schedule is done on automatic transmission's test-bed.The experimental results show the shift schedule is correct.This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynamic drive system of the vehicle.

  2. Fast Probabilistic Fusion of 3d Point Clouds via Occupancy Grids for Scene Classification

    Science.gov (United States)

    Kuhn, Andreas; Huang, Hai; Drauschke, Martin; Mayer, Helmut

    2016-06-01

    High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

  3. VEHICLE IDENTIFICATION TASK SOLUTION BY WINDSCREEN MARKING WITH A BARCODE

    Directory of Open Access Journals (Sweden)

    A. Levterov

    2012-01-01

    Full Text Available The vehicle identification means are considered and the present-day traffic requirements are set. The vehicle automatic identification method concerned with barcode use is proposed and described.

  4. Experiences in automatic keywording of particle physics literature

    CERN Document Server

    Montejo Ráez, Arturo

    2001-01-01

    Attributing keywords can assist in the classification and retrieval of documents in the particle physics literature. As information services face a future with less available manpower and more and more documents being written, the possibility of keyword attribution being assisted by automatic classification software is explored. A project being carried out at CERN (the European Laboratory for Particle Physics) for the development and integration of automatic keywording is described.

  5. MOTOR VEHICLE SAFETY RESEARCH METHODOLOGY

    Directory of Open Access Journals (Sweden)

    A. Stepanov

    2015-07-01

    Full Text Available The issues of vehicle safety are considered. The methodology of approach to analyzing and solving the problem of safety management of vehicles and overall traffic is offered. The distinctive features of organization and management of vehicle safety are shown. There has been drawn a conclusion that the methodological approach to solving traffic safety problems is reduced to selection and classification of safety needs.

  6. Hierarchical word clustering - automatic thesaurus generation

    OpenAIRE

    Hodge, V.J.; Austin, J.

    2002-01-01

    In this paper, we propose a hierarchical, lexical clustering neural network algorithm that automatically generates a thesaurus (synonym abstraction) using purely stochastic information derived from unstructured text corpora and requiring no prior word classifications. The lexical hierarchy overcomes the Vocabulary Problem by accommodating paraphrasing through using synonym clusters and overcomes Information Overload by focusing search within cohesive clusters. We describe existing word catego...

  7. 1997 update for the applications guide to vehicle SNM monitors

    International Nuclear Information System (INIS)

    Ten years have elapsed since the publication of the original applications guide to vehicle special nuclear material (SNM) monitors. During that interval, use of automatic vehicle monitors has become more commonplace, and formal procedures for monitor upkeep and evaluation have become available. New concepts for vehicle monitoring are being explored, as well. This update report reviews the basics of vehicle SNM monitoring, discusses what is new in vehicle SNM monitoring, and catalogs the vehicle SNM monitors that are commercial available

  8. STUDY ON SHIFT SCHEDULE AND SIMULATION OF AUTOMATIC TRANSMISSION

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule,a simulation program using a software package of Matlab/Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.

  9. Music Genre Classification Systems - A Computational Approach

    OpenAIRE

    Ahrendt, Peter; Hansen, Lars Kai

    2006-01-01

    Automatic music genre classification is the classification of a piece of music into its corresponding genre (such as jazz or rock) by a computer. It is considered to be a cornerstone of the research area Music Information Retrieval (MIR) and closely linked to the other areas in MIR. It is thought that MIR will be a key element in the processing, searching and retrieval of digital music in the near future. This dissertation is concerned with music genre classification systems and in particular...

  10. Automatic-Control Challenges in Future Urban Vehicles: A Blend of Chassis, Energy and Networking Management Les défis de la commande automatique dans les futurs véhicules urbains : un mélange de gestion de châssis, d’énergie et du réseau

    Directory of Open Access Journals (Sweden)

    Savaresi S.M.

    2012-10-01

    Full Text Available The topic of this paper is the discussion of new challenges that the scientific field of automatic-control will face in the next decades, in the area of vehicles control. The focus is on urban vehicles for personal mobility, since this type of vehicles will be subject to the biggest changes in the next decades. The paper is articulated in three sections – in a top-down framework – briefly addressing and discussing the following items: the main drivers, which will force a change in urban personal mobility; the main types of vehicles, which are expected to address at best such drivers; the main automatic-control challenges on such type of vehicles. The scope of this paper is purposely non-technical. Its aim is mainly to discuss the emerging new challenges from the perspective of the automatic-control scientists and practitioners. The goal of the paper is to establish a discussion framework on the problems and opportunities, which will arise in this field, in the near future. Le sujet du présent article est une discussion sur les nouveaux défis auxquels le domaine scientifique de la commande automatique des véhicules va faire face dans les prochaines décennies. L’accent est mis sur les véhicules urbains destinés à une mobilité individuelle, puisque c’est ce type de véhicules qui va faire l’objet des plus grands changements dans les prochaines décennies. Le présent article s’articule, selon une démarche descendante, en trois sections abordant et discutant brièvement les éléments suivants : les principaux moteurs qui vont imposer un changement en matière de mobilité individuelle; les principaux types de véhicules qui sont attendus pour répondre au mieux à de tels moteurs et les principaux défis de la commande automatique sur un tel type de véhicules. À dessein, la portée du présent article est non technique. Son but est principalement de discuter les nouveaux défis émergeants, à partir de perspectives des

  11. [Classification of viruses by computer].

    Science.gov (United States)

    Ageeva, O N; Andzhaparidze, O G; Kibardin, V M; Nazarova, G M; Pleteneva, E A

    1982-01-01

    The study used the information mass containing information on 83 viruses characterized by 41 markers. The suitability of one of the variants of cluster analysis for virus classification was demonstrated. It was established that certain stages of automatic allotment of viruses into groups by the degree of similarity of their properties end the formation of groups which consist of viruses sufficiently close to each other by their properties and are sufficiently isolated. Comparison of these groups with the classification proposed by the ICVT established their correspondence to individual families. Analysis of the obtained classification system permits sufficiently grounded conclusions to be drawn with regard to the classification position of certain viruses, the classification of which has not yet been completed by the ICVT.

  12. Meta Classification for Variable Stars

    CERN Document Server

    Pichara, Karim; León, Daniel

    2016-01-01

    The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New science problems emerge and it is critical to be able to re-use the models learned before, without rebuilding everything from the beginning when the science problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. Conventional mixture of experts algorithms in machine learning literature can not be used since each expert (model) uses different inputs. We also consider computational complexity of the model by ...

  13. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    Science.gov (United States)

    Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

    2009-01-01

    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

  14. Physics of Automatic Target Recognition

    CERN Document Server

    Sadjadi, Firooz

    2007-01-01

    Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, sensor and processing adaptation, transformation of electromagnetic and acoustic waves in their interactions with targets, background clutter, transmission media, and sensing elements. The general inverse scattering, and advanced signal processing techniques and scientific evaluation methodologies being used in this multi disciplinary field will be part of this exposition. The issues of modeling of target signatures in various spectral modalities, LADAR, IR, SAR, high resolution radar, acoustic, seismic, visible, hyperspectral, in diverse geometric aspects will be addressed. The methods for signal processing and classification will cover concepts such as sensor adaptive and artificial neural networks, time reversal filt...

  15. Learning slip behavior using automatic mechanical supervision

    OpenAIRE

    Angelova, Anelia; Matthies, Larry; Helmick, Daniel; Perona, Pietro

    2007-01-01

    We address the problem of learning terrain traversability properties from visual input, using automatic mechanical supervision collected from sensors onboard an autonomous vehicle. We present a novel probabilistic framework in which the visual information and the mechanical supervision interact to learn particular terrain types and their properties. The proposed method is applied to learning of rover slippage from visual information in a completely auto...

  16. Adaboost Technique for Vehicle Detection in Aerial Surveillance

    Directory of Open Access Journals (Sweden)

    R.Sindoori

    2013-04-01

    Full Text Available An approach for vehicle detection system from satellite images, which are used in many applications. Vehicle detection is done by pixelwise classification method instead sliding window and region based methods, which are used in existing system. The vital part of the paper is feature extraction and vehicle colour classification. Feature extraction includes edge and corner detection. For edgedetection, the Canny edge detector technique is applied. For, corner detection, the Harris corner detector process is applied. Adaboost is employed for vehicle colour extraction to separate vehicle and non-vehicle colours. Utterly, morphological operations are applied to enhance the vehicle detection.

  17. 汽车自动变速器故障诊断专家系统的研究%Research on the Fault Diagnoses Expert System of Vehicle Automatic Transmission

    Institute of Scientific and Technical Information of China (English)

    蒋鸣雷

    2013-01-01

    Automatic transmission is a system integrating the machine, electricity and liquid. The fault diagnosis of automatic transmission is very dif icult. At present, the fault diagnosis expert system is the development direction of automatic transmission diagnostic technology. By using the production rule and the frame representation, knowledge base and the fault tree of automatic transmission is are constructed, and the expert system is designed by applying forward reasoning mechanism. The fault diagnosis expert system of automatic transmission is developed by using Visual Basic 6 and Microsoft Access 2003 as the programming language.%  汽车自动变速器是集机、电、液于一体的系统,其故障诊断难度大,故障诊断专家系统是目前汽车自动变速器故障诊断技术的发展方向。利用产生式规则和框架表示法构建知识库,建立自动变速器工作异常故障树并应用正向推理机制对专家系统进行了设计,以Visual Basic 6.0、Microsoft Access 2003为编程语言开发了自动变速器故障诊断专家系统。

  18. Intelligent behaviors through vehicle-to-vehicle and vehicle-to-infrastructure communication

    Science.gov (United States)

    Garcia, Richard D.; Sturgeon, Purser; Brown, Mike

    2012-06-01

    The last decade has seen a significant increase in intelligent safety devices on private automobiles. These devices have both increased and augmented the situational awareness of the driver and in some cases provided automated vehicle responses. To date almost all intelligent safety devices have relied on data directly perceived by the vehicle. This constraint has a direct impact on the types of solutions available to the vehicle. In an effort to improve the safety options available to a vehicle, numerous research laboratories and government agencies are investing time and resources into connecting vehicles to each other and to infrastructure-based devices. This work details several efforts in both the commercial vehicle and the private auto industries to increase vehicle safety and driver situational awareness through vehicle-to-vehicle and vehicle-to-infrastructure communication. It will specifically discuss intelligent behaviors being designed to automatically disable non-compliant vehicles, warn tractor trailer vehicles of unsafe lane maneuvers such as lane changes, passing, and merging, and alert drivers to non-line-of-sight emergencies.

  19. 基于Google Earth的ETM+遥感图像自动分类方法%Automatic Classification Method of ETM + Remote Sensing Images Based on Google Earth

    Institute of Scientific and Technical Information of China (English)

    李文庆; 姜琦刚; 邢宇; 吴淞; 印影; 刘舒; 崔璨

    2012-01-01

    为了快速准确识别地物、设计野外路线并减少踏勘后对前期解译工作的修改,本文参考Google Earth软件提供的高分辨率遥感图像,利用ETM+解译生成训练样本,然后采用最大似然监督分类算法进行ETM+图像分类.结果表明:与非监督分类和非监督-监督混合分类方法相比,基于Google Earth高分辨率遥感图像的ETM最大似然监督分类方法效果好、精度高,是一种经济、高效的技术手段,可用于初步识别地物分布情况、设计野外路线和勘查点等工作,对野外工作具有一定的指导意义;不同融合方式、不同波段组合的图像分类结果明显不同,该区域ETM+图像R(5)G(4)B(3)波段组合、PCA融合图像的分类总精度最好.%Through referring the high - resolution remote sensing images provided by Google Earth, the training samples were generated by the manual interpretation of the Landsat ETM+ images. The samples were used to conduct ETM+ image classification by using the maximum likelihood supervised classification algorithm. The results showed that; in comparison with the methods of non -supervised classification and unsupervised - supervision mixed classification, the ETM maximum likelihood supervised classification method based on Google Earth high - resolution remote sensing images worked well with high precision, which was an economical and efficient technical means. It could be used to roughly identify the distribution of surface feature, and to design field routes and exploration points, which had a certain guiding significance on field work. The classification results of different fusion methods and different band combinations of images were significantly different. The overall classification accuracy of ETM+ images with R(5)G(4)B(3) band combination and PCA fused image in this region was the best.

  20. Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving

    Science.gov (United States)

    Kerner, Boris S.

    2016-05-01

    In a mini-review Kerner (2013) it has been shown that classical traffic flow theories and models failed to explain empirical traffic breakdown - a phase transition from metastable free flow to synchronized flow at highway bottlenecks. The main objective of this mini-review is to study the consequence of this failure of classical traffic-flow theories for an analysis of empirical stochastic highway capacity as well as for the effect of automatic driving vehicles and cooperative driving on traffic flow. To reach this goal, we show a deep connection between the understanding of empirical stochastic highway capacity and a reliable analysis of automatic driving vehicles in traffic flow. With the use of simulations in the framework of three-phase traffic theory, a probabilistic analysis of the effect of automatic driving vehicles on a mixture traffic flow consisting of a random distribution of automatic driving and manual driving vehicles has been made. We have found that the parameters of automatic driving vehicles can either decrease or increase the probability of the breakdown. The increase in the probability of traffic breakdown, i.e., the deterioration of the performance of the traffic system can occur already at a small percentage (about 5%) of automatic driving vehicles. The increase in the probability of traffic breakdown through automatic driving vehicles can be realized, even if any platoon of automatic driving vehicles satisfies condition for string stability.

  1. Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

    Directory of Open Access Journals (Sweden)

    Xiangwei Xing

    2014-01-01

    Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

  2. Automatic Kurdish Dialects Identification

    Directory of Open Access Journals (Sweden)

    Hossein Hassani

    2016-02-01

    Full Text Available Automatic dialect identification is a necessary Lan guage Technology for processing multi- dialect languages in which the dialects are linguis tically far from each other. Particularly, this becomes crucial where the dialects are mutually uni ntelligible. Therefore, to perform computational activities on these languages, the sy stem needs to identify the dialect that is the subject of the process. Kurdish language encompasse s various dialects. It is written using several different scripts. The language lacks of a standard orthography. This situation makes the Kurdish dialectal identification more interesti ng and required, both form the research and from the application perspectives. In this research , we have applied a classification method, based on supervised machine learning, to identify t he dialects of the Kurdish texts. The research has focused on two widely spoken and most dominant Kurdish dialects, namely, Kurmanji and Sorani. The approach could be applied to the other Kurdish dialects as well. The method is also applicable to the languages which are similar to Ku rdish in their dialectal diversity and differences.

  3. Vehicle to Vehicle Services

    DEFF Research Database (Denmark)

    Brønsted, Jeppe Rørbæk

    2008-01-01

    As computing devices, sensors, and actuators pervade our surroundings, new applications emerge with accompanying research challenges. In the transportation domain vehicles are being linked by wireless communication and equipped with an array of sensors and actuators that make is possible to provide...... location aware infotainment, increase safety, and lessen environmental strain. This dissertation is about service oriented architecture for pervasive computing with an emphasis on vehicle to vehicle applications. If devices are exposed as services, applications can be created by composing a set of services...... and governing the flow of data among them. In pervasive computing, composing services is, however, not the whole story. To fully realize their potential, applications must also deal with challenges such as device heterogeneity, context awareness, openendedness, and resilience to dynamism in network connectivity...

  4. MULTI-LABEL CLASSIFICATION OF PRODUCT REVIEWS USING STRUCTURED SVM

    Directory of Open Access Journals (Sweden)

    Jincy B. Chrystal

    2015-05-01

    Full Text Available Most of the text classification problems are associated with multiple class labels and hence automatic text classification is one of the most challenging and prominent research area. Text classification is the problem of categorizing text documents into different classes. In the multi-label classification scenario, each document is associated may have more than one label. The real challenge in the multi-label classification is the labelling of large number of text documents with a subset of class categories. The feature extraction and classification of such text documents require an efficient machine learning algorithm which performs automatic text classification. This paper describes the multi-label classification of product review documents using Structured Support Vector Machine.

  5. Text Classification using Data Mining

    CERN Document Server

    Kamruzzaman, S M; Hasan, Ahmed Ryadh

    2010-01-01

    Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the...

  6. Automatic emotional expression analysis from eye area

    Science.gov (United States)

    Akkoç, Betül; Arslan, Ahmet

    2015-02-01

    Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

  7. Vehicle License Plate Recognition Syst

    Directory of Open Access Journals (Sweden)

    Meenakshi,R. B. Dubey

    2012-12-01

    Full Text Available The vehicle license plate recognition system has greater efficiency for vehicle monitoring in automatic zone access control. This Plate recognition system will avoid special tags, since all vehicles possess a unique registration number plate. A number of techniques have been used for car plate characters recognition. This system uses neural network character recognition and pattern matching of characters as two character recognition techniques. In this approach multilayer feed-forward back-propagation algorithm is used. The performance of the proposed algorithm has been tested on several car plates and provides very satisfactory results.

  8. Sparse representation for vehicle recognition

    Science.gov (United States)

    Monnig, Nathan D.; Sakla, Wesam

    2014-06-01

    The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.

  9. Feasible Path Planning for Autonomous Vehicles

    Directory of Open Access Journals (Sweden)

    Vu Trieu Minh

    2014-01-01

    Full Text Available The objective of this paper is to find feasible path planning algorithms for nonholonomic vehicles including flatness, polynomial, and symmetric polynomial trajectories subject to the real vehicle dynamical constraints. Performances of these path planning methods are simulated and compared to evaluate the more realistic and smoother generated trajectories. Results show that the symmetric polynomial algorithm provides the smoothest trajectory. Therefore, this algorithm is recommended for the development of an automatic control for autonomous vehicles.

  10. Hubble Classification

    Science.gov (United States)

    Murdin, P.

    2000-11-01

    A classification scheme for galaxies, devised in its original form in 1925 by Edwin P Hubble (1889-1953), and still widely used today. The Hubble classification recognizes four principal types of galaxy—elliptical, spiral, barred spiral and irregular—and arranges these in a sequence that is called the tuning-fork diagram....

  11. Vision systems for manned and robotic ground vehicles

    Science.gov (United States)

    Sanders-Reed, John N.; Koon, Phillip L.

    2010-04-01

    A Distributed Aperture Vision System for ground vehicles is described. An overview of the hardware including sensor pod, processor, video compression, and displays is provided. This includes a discussion of the choice between an integrated sensor pod and individually mounted sensors, open architecture design, and latency issues as well as flat panel versus head mounted displays. This technology is applied to various ground vehicle scenarios, including closed-hatch operations (operator in the vehicle), remote operator tele-operation, and supervised autonomy for multi-vehicle unmanned convoys. In addition, remote vision for automatic perimeter surveillance using autonomous vehicles and automatic detection algorithms is demonstrated.

  12. A Continuously Updated, Global Land Classification Map Project

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to demonstrate a fully automatic capability for generating a global, high resolution (30 m) land classification map, with continuous updates from...

  13. Automated Periodontal Diseases Classification System

    OpenAIRE

    Aliaa A. A. Youssif; Abeer Saad Gawish,; Mohammed Elsaid Moussa

    2012-01-01

    This paper presents an efficient and innovative system for automated classification of periodontal diseases, The strength of our technique lies in the fact that it incorporates knowledge from the patients' clinical data, along with the features automatically extracted from the Haematoxylin and Eosin (H&E) stained microscopic images. Our system uses image processing techniques based on color deconvolution, morphological operations, and watershed transforms for epithelium & connective tissue se...

  14. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  15. On the relevance of spectral features for instrument classification

    OpenAIRE

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai; Arenas-García, Jerónimo

    2007-01-01

    Automatic knowledge extraction from music signals is a key component for most music organization and music information retrieval systems. In this paper, we consider the problem of instrument modelling and instrument classification from the rough audio data. Existing systems for automatic instrument classification operate normally on a relatively large number of features, from which those related to the spectrum of the audio signal are particularly relevant. In this paper, we confront two diff...

  16. A complete discrimination system for polynomials with complex coefficients and its automatic generation

    Institute of Scientific and Technical Information of China (English)

    梁松新; 张景中

    1999-01-01

    By establishing a complete discrimination system for polynomials, the problem of complete root classification for polynomials with complex coefficients is utterly solved, furthermore, the algorithm obtained is made into a general program in Maple, which enables the complete discrimination system and complete root classification of a polynomial to be automatically generated by computer, without any human intervention. Besides, by using the automatic generation of root classification, a method to determine the positive definiteness of a polynomial in one or two indeterminates is automatically presented.

  17. Semi-automatic knee cartilage segmentation

    Science.gov (United States)

    Dam, Erik B.; Folkesson, Jenny; Pettersen, Paola C.; Christiansen, Claus

    2006-03-01

    Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail. We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.

  18. Comparative Analysis of Vehicle Make and Model Recognition Techniques

    Directory of Open Access Journals (Sweden)

    Faiza Ayub Syed

    2014-03-01

    Full Text Available Vehicle Make and Model Recognition (VMMR has emerged as a significant element of vision based systems because of its application in access control systems, traffic control and monitoring systems, security systems and surveillance systems, etc. So far a number of techniques have been developed for vehicle recognition. Each technique follows different methodology and classification approaches. The evaluation results highlight the recognition technique with highest accuracy level. In this paper we have pointed out the working of various vehicle make and model recognition techniques and compare these techniques on the basis of methodology, principles, classification approach, classifier and level of recognition After comparing these factors we concluded that Locally Normalized Harris Corner Strengths (LHNS performs best as compared to other techniques. LHNS uses Bayes and K-NN classification approaches for vehicle classification. It extracts information from frontal view of vehicles for vehicle make and model recognition.

  19. Unification of automatic target tracking and automatic target recognition

    Science.gov (United States)

    Schachter, Bruce J.

    2014-06-01

    The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

  20. Automated Periodontal Diseases Classification System

    Directory of Open Access Journals (Sweden)

    Aliaa A. A. Youssif

    2012-01-01

    Full Text Available This paper presents an efficient and innovative system for automated classification of periodontal diseases, The strength of our technique lies in the fact that it incorporates knowledge from the patients' clinical data, along with the features automatically extracted from the Haematoxylin and Eosin (H&E stained microscopic images. Our system uses image processing techniques based on color deconvolution, morphological operations, and watershed transforms for epithelium & connective tissue segmentation, nuclear segmentation, and extraction of the microscopic immunohistochemical features for the nuclei, dilated blood vessels & collagen fibers. Also, Feedforward Backpropagation Artificial Neural Networks are used for the classification process. We report 100% classification accuracy in correctly identifying the different periodontal diseases observed in our 30 samples dataset.

  1. Adaboost Technique for Vehicle Detection in Aerial Surveillance

    OpenAIRE

    R. Sindoori; Ravichandran, K.S.; B. Santhi

    2013-01-01

    An approach for vehicle detection system from satellite images, which are used in many applications. Vehicle detection is done by pixelwise classification method instead sliding window and region based methods, which are used in existing system. The vital part of the paper is feature extraction and vehicle colour classification. Feature extraction includes edge and corner detection. For edgedetection, the Canny edge detector technique is applied. For, corner detection, the Harris corner detec...

  2. Automatic Fiscal Stabilizers

    Directory of Open Access Journals (Sweden)

    Narcis Eduard Mitu

    2013-11-01

    Full Text Available Policies or institutions (built into an economic system that automatically tend to dampen economic cycle fluctuations in income, employment, etc., without direct government intervention. For example, in boom times, progressive income tax automatically reduces money supply as incomes and spendings rise. Similarly, in recessionary times, payment of unemployment benefits injects more money in the system and stimulates demand. Also called automatic stabilizers or built-in stabilizers.

  3. Automatic input rectification

    OpenAIRE

    Long, Fan; Ganesh, Vijay; Carbin, Michael James; Sidiroglou, Stelios; Rinard, Martin

    2012-01-01

    We present a novel technique, automatic input rectification, and a prototype implementation, SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. When given an atypical input that does not satisfy these constraints, SOAP automatically rectifies the input (i.e., changes the input so that it satisfies the learned constraints). The goal is to automatically convert potentially dangerous inputs into typical inputs that the ...

  4. Study on Automatic Determination of Foot- arch Classification Parameters, Their Reliabilities and Classifying Consistency%人体足弓类型评价参数自动提取、可靠性分析及分类结果一致性研究

    Institute of Scientific and Technical Information of China (English)

    邱海; 熊树平; 孙娜; 屠晏欣

    2012-01-01

    为了实现对人体足弓类型进行科学评价和分类,本文研究了如何从二维脚印图中快速地自动提取系列足弓类型评价参数,利用实验设计的方法对系列评价参数进行了可靠性、相关性和分类结果一致性的分析.研究表明足弓系数和脚印系数比比值系数有更好可靠性,三种参数存在很好的线性相关性,通过它们进行脚型分类的结果有较好的一致性.研究结果对鞋类产品设计、运动员与士兵的选拨、足部保健等有重要的实际意义.%In order to evaluate and classify foot - arch type scientifically, this paper proposed a method to automatically determine foot - arch classification parameters from two - dimensional footprints. An experimental study was conducted to evaluate the reliabilities and correlation of each parameter and the classifying consistency. The results showed that the reliabilities of arch index and footprint index are higher than those of ratio index; each parameter does correlate with each other well and the foot - arch type classification results from three investigated parameters have good consistency. The research output could be important in several aspects such as the design of comfortable footwear products, the screening process of athletes and soldiers, and the health care of foot.

  5. Automatic classification of Deep Web sources based on KNN algorithm%基于K-近邻算法的Deep Web数据源的自动分类

    Institute of Scientific and Technical Information of China (English)

    张智; 顾韵华

    2011-01-01

    To meet the need of Deep Web query, an algorithm for classification of Deep Web sources based on KNN is put forward. The algorithm extracts the form features from Web pages, and makes the form features vector normal. Then the algorithm classifies Deep Web pages by computing distance. The experimental results show that the algorithm has improved in precision and recall.%针对Deep Web的查询需求,提出了一种基于K-近邻算法的Deep Web数据源的自动分类方法.该算法在对Deep Web网页进行表单特征提取及规范化的基础上,基于距离对Deep Web网页所属的目标主题进行判定.实验结果表明:基于K-近邻分类算法可以较有效地进行DeepWeb数据源的自动分类,并得到较高的查全率和查准率.

  6. Automatic differentiation bibliography

    Energy Technology Data Exchange (ETDEWEB)

    Corliss, G.F. (comp.)

    1992-07-01

    This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.

  7. Research on the automatic detection and intelligent classification technique of external thread surface quality%外螺纹表面质量自动检测及智能分类技术研究

    Institute of Scientific and Technical Information of China (English)

    郭联金; 朱日龙; 杨国卿; 罗炳军

    2015-01-01

    针对某一企业现有的外螺纹表面质量检测装置效率低、功能不完善、难以实现在线检测的问题,提出采用机器视觉及基于神经网络的智能模式识别技术设计一套自动检测不同类型螺纹零件的几何参数,并对其表面缺陷进行智能化识别、分类的系统。介绍了系统架构及部件选型,基于VS2008、OpenCV等软件开发平台,设计了外螺纹尺寸检测、头部槽型识别及表面缺陷识别的实现方法。实验表明,系统具有精度高、速度快、运行稳定等优点。%For the low efficiency and the not perfect function of the existing external thread surface quality detection device in a enterprise, which is difficult to achieve real-time online detection, machine vision and the intelligent pattern recognition based on neural network technology were proposed to solve the problem. The goal was to design a set of geometry parameter automatic detection device for the different types of external thread, and its surface defect also be intelligently recognized and classified. The system architecture and component selection were introduced. Based on VS2008, OpenCV software development platform, the implementation method of size detection, head shape recognition and surface defect identification were designed. Experiments showed that the system run with high precision, low error, speed, and fast speed.

  8. Use of an automatic procedure for determination of classes of land use in the Teste Araras area of the peripheral Paulist depression

    Science.gov (United States)

    Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Valeriano, D. D.

    1981-01-01

    An evaluation of the multispectral image analyzer (system Image 1-100), using automatic classification, is presented. The region studied is situated. The automatic was carried out using the maximum likelihood (MAXVER) classification system. The following classes were established: urban area, bare soil, sugar cane, citrus culture (oranges), pastures, and reforestation. The classification matrix of the test sites indicate that the percentage of correct classification varied between 63% and 100%.

  9. Clever Toolbox - the Art of Automated Genre Classification

    DEFF Research Database (Denmark)

    2005-01-01

    Automatic musical genre classification can be defined as the science of finding computer algorithms that a digitized sound clip as input and yield a musical genre as output. The goal of automated genre classification is, of course, that the musical genre should agree with the human classificasion...

  10. Prototype Selection for Classification in Standard and Generalized Dissimilarity Spaces

    NARCIS (Netherlands)

    Plasencia Calaña, Y.

    2015-01-01

    Automatic pattern classification for a given problem domain aims at assigning a class or category membership to a new unseen object from the same domain. This is performed in three main stages: data preprocessing, representation and classification. The data preprocessing highly depends on the data t

  11. Research Dynamics of the Classification Methods of Remote Sensing Images

    Institute of Scientific and Technical Information of China (English)

    Yan; ZHANG; Baoguo; WU; Dong; WANG

    2013-01-01

    As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification process and system of remote sensing images. According to the recent research status of domestic and international remote sensing classification methods,the new study dynamics of remote sensing classification,such as artificial neural networks,support vector machine,active learning and ensemble multi-classifiers,were introduced,providing references for the automatic and intelligent development of remote sensing images classification.

  12. Feature Selection Approach in Animal Classification

    OpenAIRE

    Y H Sharath Kumar; C D Divya

    2014-01-01

    In this paper, we propose a model for automatic classification of Animals using different classifiers Nearest Neighbour, Probabilistic Neural Network and Symbolic. Animal images are segmented using maximal region merging segmentation. The Gabor features are extracted from segmented animal images. Discriminative texture features are then selected using the different feature selection algorithm like Sequential Forward Selection, Sequential Floating Forward Selection, Sequential B...

  13. Visual Alphabets: Video Classification by End Users

    NARCIS (Netherlands)

    Israël, Menno; Broek, van den Egon L.; Putten, van der Peter; Uyl, den Marten J.; Petrushin, Valery A.; Khan, Latifur

    2006-01-01

    The work presented here introduces a real-time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification c

  14. Advances in Detection and Classification of Underwater Targets using Synthetic Aperture Sonar Imagery

    OpenAIRE

    Fei, Tai

    2015-01-01

    In this PhD thesis, the problem of underwater mine detection and classification using synthetic aperture sonar (SAS) imagery is considered. The automatic detection and automatic classification (ADAC) system is applied to images obtained by SAS systems. The ADAC system contains four steps, namely mine-like object (MLO) detection, image segmentation, feature extraction, and mine type classification. This thesis focuses on the last three steps. In the mine-like object detectio...

  15. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  16. Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields

    NARCIS (Netherlands)

    Rudovic, Ognjen; Pavlovic, Vladimir; Pantic, Maja

    2013-01-01

    Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification

  17. Automatic recognition of lactating sow behaviors through depth image processing

    Science.gov (United States)

    Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shiftin...

  18. PANACEA English automatically acquired lexicon for LAB domain: Lexical Semantic classes for nouns

    OpenAIRE

    Universitat Pompeu Fabra. Institut Universitari de Ling????stica Aplicada (IULA)

    2012-01-01

    TThis is a domain-specific lexicon of for English for labour (LAB) domain. This lexicon contains a set of nouns classified into seven different semantic classes. It has been automatically created using the PANACEA web services for noun classification and the crawled data for this domain and language, previously annotated with FreeLing tagger. The crawled data was obtained crawling web pages that were automatically detected to be in the English language and were automatically classified as rel...

  19. PANACEA English automatically acquired lexicon for ENV domain: Lexical Semantic classes for nouns

    OpenAIRE

    Universitat Pompeu Fabra. Institut Universitari de Ling????stica Aplicada (IULA)

    2012-01-01

    This is a domain-specific lexicon of for English for environtment (ENV) domain. This lexicon contains a set of nouns classified into seven different semantic classes. It has been automatically created using the PANACEA web services for noun classification and the crawled data for this domain and language, previously annotated with FreeLing tagger. The crawled data was obtained crawling web pages that were automatically detected to be in the English language and were automatically classified a...

  20. Automatic Licenses Plate Recognition

    OpenAIRE

    Ronak P Patel; Narendra M Patel; Keyur Brahmbhatt

    2013-01-01

    This paper describes the Smart Vehicle Screening System, which can be installed into a tollboothfor automated recognition of vehicle license plate information using a photograph of a vehicle. An automatedsystem could then be implemented to control the payment of fees, parking areas, highways, bridges ortunnels, etc. This paper contains new algorithm for recognition number plate using Morphological operation,Thresholding operation, Edge detection, Bounding box analysis for number plate extract...

  1. Automatic Payroll Deposit System.

    Science.gov (United States)

    Davidson, D. B.

    1979-01-01

    The Automatic Payroll Deposit System in Yakima, Washington's Public School District No. 7, directly transmits each employee's salary amount for each pay period to a bank or other financial institution. (Author/MLF)

  2. 四轴车辆全轮转向之自动模式研究%A Research on the Automatic Mode of All-wheel Steering for Four-axle Vehicle

    Institute of Scientific and Technical Information of China (English)

    陈思忠; 郑凯锋

    2015-01-01

    在第2轴转角比例于第1轴转角,第3轴转角比例于第4轴转角的条件下,从理论上分析了全轮转向与双前桥转向之间的差异。接着以质心侧偏角为零,设计了控制器一和控制器二两种控制器,并再次分析了双前桥转向、带控制器一的全轮转向和带控制器二的全轮转向三者之间的区别与联系,为四轴车辆的全轮转向技术的研究提供了理论参考。%Under the condition of that the wheel turning angles in second and third axles are respectively proportional to those of first and fourth axles, the differences between all wheel steering and double-front-axle steer-ing are analyzed theoretically. Then with the sideslip angle of mass center set to zero, controller 1 and controller 2 are designed, and the differences and connections between double-front-axle steering, all wheel steering with con-troller 1 and all wheel steering with controller 2 are analyzed again, providing theoretical references for the research on the all wheel steering technique for four-axle vehicle.

  3. An open-set detection evaluation methodology for automatic emotion recognition in speech

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2007-01-01

    In this paper, we present a detection approach and an ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech. The traditional classification approach does not seem to be suitable and flexible enough for typical emotion recognition tasks. For example, classification d

  4. Depth Level Control System using Peripheral Interface Controller for Underwater Vehicle

    Directory of Open Access Journals (Sweden)

    Muhamad Fadli Ghani

    2013-01-01

    Full Text Available This research explained on a design and development of an Automatic Depth Control System for underwater vehicle. Definition of underwater vehicle is a robotic sub-sea that is a part of the emerging field of autonomous and unmanned vehicles. This project shows the implementation’s development of an Automatic Depth Control System on a test prototyping vehicle especially involved small-scale and low cost sub-sea robots. The Automatic Depth Control System assembled with mechanical system and module of electronic system for development of a controller.

  5. Two Systems for Automatic Music Genre Recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2012-01-01

    We re-implement and test two state-of-the-art systems for automatic music genre classification; but unlike past works in this area, we look closer than ever before at their behavior. First, we look at specific instances where each system consistently applies the same wrong label across multiple...... trials of cross-validation. Second, we test the robustness of each system to spectral equalization. Finally, we test how well human subjects recognize the genres of music excerpts composed by each system to be highly genre representative. Our results suggest that neither high-performing system has...... a capacity to recognize music genre....

  6. Odor Classification using Agent Technology

    Directory of Open Access Journals (Sweden)

    Sigeru OMATU

    2014-03-01

    Full Text Available In order to measure and classify odors, Quartz Crystal Microbalance (QCM can be used. In the present study, seven QCM sensors and three different odors are used. The system has been developed as a virtual organization of agents using an agent platform called PANGEA (Platform for Automatic coNstruction of orGanizations of intElligent Agents. This is a platform for developing open multi-agent systems, specifically those including organizational aspects. The main reason for the use of agents is the scalability of the platform, i.e. the way in which it models the services. The system models functionalities as services inside the agents, or as Service Oriented Approach (SOA architecture compliant services using Web Services. This way the adaptation of the odor classification systems with new algorithms, tools and classification techniques is allowed.

  7. Control of Multiple Robotic Sentry Vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Feddema, J.; Klarer, P.; Lewis, C.

    1999-04-01

    As part of a project for the Defense Advanced Research Projects Agency, Sandia National Laboratories is developing and testing the feasibility of using of a cooperative team of robotic sentry vehicles to guard a perimeter and to perform surround and diversion tasks. This paper describes on-going activities in the development of these robotic sentry vehicles. To date, we have developed a robotic perimeter detection system which consists of eight ''Roving All Terrain Lunar Explorer Rover'' (RATLER{trademark}) vehicles, a laptop-based base-station, and several Miniature Intrusion Detection Sensors (MIDS). A radio frequency receiver on each of the RATLER vehicles alerts the sentry vehicles of alarms from the hidden MIDS. When an alarm is received, each vehicle decides whether it should investigate the alarm based on the proximity of itself and the other vehicles to the alarm. As one vehicle attends an alarm, the other vehicles adjust their position around the perimeter to better prepare for another alarm. We have also demonstrated the ability to drive multiple vehicles in formation via tele-operation or by waypoint GPS navigation. This is currently being extended to include mission planning capabilities. At the base-station, the operator can draw on an aerial map the goal regions to be surrounded and the repulsive regions to be avoided. A potential field path planner automatically generates a path from the vehicles' current position to the goal regions while avoiding the repulsive regions and the other vehicles. This path is previewed to the operator before the regions are downloaded to the vehicles. The same potential field path planner resides on the vehicle, except additional repulsive forces from on-board proximity sensors guide the vehicle away from unplanned obstacles.

  8. Control of multiple robotic sentry vehicles

    Science.gov (United States)

    Feddema, John T.; Lewis, Christopher L.; Klarer, Paul

    1999-07-01

    As part of a project for the Defense Advanced Research Projects Agency, Sandia National Laboratories is developing and testing the feasibility of using of a cooperative team of robotic sentry vehicles to guard a perimeter and to perform surround and diversion tasks. This paper describes on-going activities in the development of these robotic sentry vehicles. To date, we have developed a robotic perimeter detection system which consists of eight 'Roving All Terrain Lunar Explorer Rover' (RATLERTM) vehicles, a laptop-based base-station, and several Miniature Intrusion Detection Sensors (MIDS). A radio frequency receiver on each of the RATLER vehicles alerts the sentry vehicles of alarms from the hidden MIDS. When an alarm is received, each vehicle decides whether it should investigate the alarm based on the proximity of itself and the other vehicles to the alarm. As one vehicle attends an alarm, the other vehicles adjust their position around the perimeter to better prepare for another alarm. We have also demonstrated the ability to drive multiple vehicles in formation via tele-operation or by waypoint GPS navigation. This is currently being extended to include mission planning capabilities. At the base-station, the operator can draw on an aerial map the goal regions to be surrounded and the repulsive regions to be avoided. A potential field path planner automatically generates a path from the vehicles' current position to the goal regions while avoiding the repulsive regions and the other vehicles. This path is previewed to the operator before the regions are downloaded to the vehicles. The same potential field path planner resides on the vehicle, expect additional repulsive forces from on-board proximity sensors guide the vehicle away from unplanned obstacles.

  9. 无人机多光谱影像辐射一致性自动校正%Automatic radiation uniformity correction of multispectral imagery acquired with unmanned aerial vehicle

    Institute of Scientific and Technical Information of China (English)

    杨贵军; 万鹏; 于海洋; 徐波; 冯海宽

    2015-01-01

    针对一个架次内无人机影像由于光照度变化、拍摄角度等原因引起的相同地物点在不同影像上的辐射信息不一致的问题,验证了利用SIFT(scale invariant feature transform)算法匹配同名点,然后利用同名点灰度值的相关关系建立校正模型,再用该校正模型校正整幅影像的辐射一致性校正方法。对比评价了基于直方图匹配的色彩一致性校正方法、原始色彩空间辐射一致性校正、针对三波段影像的 HSV(hue,saturation,value)色彩空间亮度一致性校正以及双边滤波去噪的效果。试验结果表明:基于直方图匹配的色彩一致性校正能在视觉上达到很好的效果,但是会造成校正后影像的灰度级严重缺失;基于同名点灰度值相关关系的校正模型能够很好地恢复待校正影像与基准影像的辐射一致性;HSV色彩空间亮度一致性校正能够在色彩上和辐射信息上与基准影像均达到很好的一致性,但只适用于三波段影像;双边滤波在去除噪声的同时,能够保持甚至提高校正后影像与基准影像的辐射一致性。%Unmanned aerial vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. In a data acquisition task, hundreds of images are acquired in a sortie due to the very small footprint of each image. The gray-scale value of homonymous points in adjacent-image pairs which is gathered by unmanned aerial vehicle in a sortie should be consistent on an -idealized condition. Actually, the gray-scale value of homonymous points always yields different results due to changes in illuminance, different shooting angle etc. In this study, we described a methodology for radiation uniformity correction among different images. The first step of the methodology was matching the images by scale invariant feature transform (SIFT) feature detector to extract homonymous points from which its gray

  10. Feature Extraction in Radar Target Classification

    Directory of Open Access Journals (Sweden)

    Z. Kus

    1999-09-01

    Full Text Available This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT and Multiple Signal Characterisation (MUSIC, based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.

  11. On the Design of a Robotic System Composed of an Unmanned Surface Vehicle and a Piggybacked VTOL

    OpenAIRE

    Pinto, Eduardo; Santana, Pedro; Marques, Francisco; Mendonça, Ricardo; Lourenço, André; Barata, José

    2014-01-01

    Part 8: Robotics and Mechatronics International audience This paper presents the core ideas of the RIVERWATCH experiment and describes its hardware architecture. The RIVERWATCH experiment considers the use of autonomous surface vehicles piggybacking multi-rotor unmanned aerial vehicles for the automatic monitoring of riverine environments. While the surface vehicle benefits from the aerial vehicle to extend its field of view, the aerial vehicle benefits from the surface vehicle to ensur...

  12. Line matching for automatic change detection algorithm

    Science.gov (United States)

    Dhollande, Jérôme; Monnin, David; Gond, Laetitia; Cudel, Christophe; Kohler, Sophie; Dieterlen, Alain

    2012-06-01

    During foreign operations, Improvised Explosive Devices (IEDs) are one of major threats that soldiers may unfortunately encounter along itineraries. Based on a vehicle-mounted camera, we propose an original approach by image comparison to detect signicant changes on these roads. The classic 2D-image registration techniques do not take into account parallax phenomena. The consequence is that the misregistration errors could be detected as changes. According to stereovision principles, our automatic method compares intensity proles along corresponding epipolar lines by extrema matching. An adaptive space warping compensates scale dierence in 3D-scene. When the signals are matched, the signal dierence highlights changes which are marked in current video.

  13. Abandoned vehicles

    CERN Multimedia

    Relations with the Host States Service

    2004-01-01

    The services in charge of managing the CERN site have recently noted an increase in the number of abandoned vehicles. This poses a risk from the point of view of safety and security and, on the eve of several important events in honour of CERN's fiftieth anniversary, is detrimental to the Organization's image. Owners of vehicles that have been left immobile for some time on the CERN site, including on the external car park by the flags, are therefore invited to contact the Reception and Access Control Service (service-parking-longterm@cern.ch) before 1st October 2004 and, where appropriate, move their vehicle to a designated long-term parking area. After this date, any vehicle whose owner has failed to respond to this request and which is without a number plate, has been stationary for several weeks or is out of service, may be impounded at the owner's risk and expense. Relations with the Host States Service Tel. 72848

  14. Evaluation for Uncertain Image Classification and Segmentation

    CERN Document Server

    Martin, Arnaud; Arnold-Bos, Andreas

    2008-01-01

    Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

  15. ANPS - AUTOMATIC NETWORK PROGRAMMING SYSTEM

    Science.gov (United States)

    Schroer, B. J.

    1994-01-01

    Development of some of the space program's large simulation projects -- like the project which involves simulating the countdown sequence prior to spacecraft liftoff -- requires the support of automated tools and techniques. The number of preconditions which must be met for a successful spacecraft launch and the complexity of their interrelationship account for the difficulty of creating an accurate model of the countdown sequence. Researchers developed ANPS for the Nasa Marshall Space Flight Center to assist programmers attempting to model the pre-launch countdown sequence. Incorporating the elements of automatic programming as its foundation, ANPS aids the user in defining the problem and then automatically writes the appropriate simulation program in GPSS/PC code. The program's interactive user dialogue interface creates an internal problem specification file from user responses which includes the time line for the countdown sequence, the attributes for the individual activities which are part of a launch, and the dependent relationships between the activities. The program's automatic simulation code generator receives the file as input and selects appropriate macros from the library of software modules to generate the simulation code in the target language GPSS/PC. The user can recall the problem specification file for modification to effect any desired changes in the source code. ANPS is designed to write simulations for problems concerning the pre-launch activities of space vehicles and the operation of ground support equipment and has potential for use in developing network reliability models for hardware systems and subsystems. ANPS was developed in 1988 for use on IBM PC or compatible machines. The program requires at least 640 KB memory and one 360 KB disk drive, PC DOS Version 2.0 or above, and GPSS/PC System Version 2.0 from Minuteman Software. The program is written in Turbo Prolog Version 2.0. GPSS/PC is a trademark of Minuteman Software. Turbo Prolog

  16. Vehicle Travel Information System (VTRIS) - Data Download Tool

    Data.gov (United States)

    Department of Transportation — The VTRIS W-Tables are designed to provide a standard format for presenting the outcome of the Vehicle Weighing and Classification efforts at truck weigh sites. The...

  17. Vehicle speed control transition module and method

    Energy Technology Data Exchange (ETDEWEB)

    Mangan, E.L.; Conklin, B.

    1986-12-16

    An apparatus is described for automatically controlling the speed of a driverless vehicle comprising a stationary frame supporting first, second and third aligned drive tubes between tracks on the frame and which are adapted to support a driverless vehicle. A means is included for independently driving the first, second and third drive tubes such that each tube, when driven, rotates about its longitudinal axis, sensor means disposed along the frame for actuation by a driverless vehicle. The means for independently driving the drive tubes includes variable speed drive means responsive to the sensor means fro driving the second tube between first and second speeds. A method is described of automatically controlling the speed of a driverless vehicle which travels along first, second and third aligned drive tubes by frictional contact therewith. It maintains the first, second, and third drive tubes out of mechanical engagement with each other at all times, sensing the location of the driverless vehicle, driving the first drive tube at a first speed, driving the third drive tube at a second at a second speed. It also varies the speed of the second drive tube from the first speed to the second speed based on the sensed location of the driverless vehicle while the vehicle is driven by contact with the second drive tube.

  18. Automotive Control Systems: For Engine, Driveline, and Vehicle

    Science.gov (United States)

    Kiencke, Uwe; Nielsen, Lars

    Advances in automotive control systems continue to enhance safety and comfort and to reduce fuel consumption and emissions. Reflecting the trend to optimization through integrative approaches for engine, driveline, and vehicle control, this valuable book enables control engineers to understand engine and vehicle models necessary for controller design, and also introduces mechanical engineers to vehicle-specific signal processing and automatic control. The emphasis on measurement, comparisons between performance and modeling, and realistic examples derive from the authors' unique industrial experience

  19. Automatic text summarization

    CERN Document Server

    Torres Moreno, Juan Manuel

    2014-01-01

    This new textbook examines the motivations and the different algorithms for automatic document summarization (ADS). We performed a recent state of the art. The book shows the main problems of ADS, difficulties and the solutions provided by the community. It presents recent advances in ADS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several exemples are included in order to clarify the theoretical concepts.  The books currently available in the area of Automatic Document Summarization are not recent. Powerful algorithms have been develop

  20. Automatic utilities auditing

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Colin Boughton [Energy Metering Technology (United Kingdom)

    2000-08-01

    At present, energy audits represent only snapshot situations of the flow of energy. The normal pattern of energy audits as seen through the eyes of an experienced energy auditor is described. A brief history of energy auditing is given. It is claimed that the future of energy auditing lies in automatic meter reading with expert data analysis providing continuous automatic auditing thereby reducing the skill element. Ultimately, it will be feasible to carry out auditing at intervals of say 30 minutes rather than five years.

  1. Automatic Camera Control

    DEFF Research Database (Denmark)

    Burelli, Paolo; Preuss, Mike

    2014-01-01

    Automatically generating computer animations is a challenging and complex problem with applications in games and film production. In this paper, we investigate howto translate a shot list for a virtual scene into a series of virtual camera configurations — i.e automatically controlling the virtual...... camera. We approach this problem by modelling it as a dynamic multi-objective optimisation problem and show how this metaphor allows a much richer expressiveness than a classical single objective approach. Finally, we showcase the application of a multi-objective evolutionary algorithm to generate a shot...

  2. Automatic classification of protein structure by using Gauss integrals

    DEFF Research Database (Denmark)

    Røgen, Peter; Fain, B.

    2003-01-01

    We introduce a method of looking at, analyzing, and comparing protein structures. The topology of a protein is captured by 30 numbers inspired by Vassiliev knot invariants. To illustrate the simplicity and power of this topological approach, we construct a measure (scaled Gauss metric, SGM...

  3. Automatic Classification of Serrated Patterns in Direct Immunofluorescence Images

    NARCIS (Netherlands)

    Shi, Chenyu; Meijer, Joost; Guo, Jiapan; Azzopardi, George; Jonkman, Marcel F.; Petkov, Nicolai

    2015-01-01

    Direct immunofluorescence (DIF) images are used by clinical experts for the diagnosis of autoimmune blistering diseases. The analysis of serration patterns in DIF images concerns two types of patterns, namely n- and u-serrated. Manual analysis is time-consuming and challenging due to noise. We propo

  4. Optical coherence tomography: automatic retina classification through support vector machines

    OpenAIRE

    Bernardes, Rui; Serranho, Pedro; Santos, Torcato; Gonçalves, Valter; Cunha-Vaz, José

    2012-01-01

    Optical coherence tomography (OCT) is becoming one of the most important imaging modalities in ophthalmology due to its non-invasiveness and by allowing the visualisation the human retina structure in detail. It was recently proposed that OCT data embeds functional information from the human retina. Specifically, it was proposed that blood–retinal barrier status information is present within OCT data from the human retina. Besides this ability, the authors present data supporting the idea ...

  5. Automatic Classification of Offshore Wind Regimes With Weather Radar Observations

    OpenAIRE

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2014-01-01

    Weather radar observations are called to play an important role in offshore wind energy. In particular, they can enable the monitoring of weather conditions in the vicinity of large-scale offshore wind farms and thereby notify the arrival of precipitation systems associated with severe wind fluctuations. The information they provide could then be integrated into an advanced prediction system for improving offshore wind power predictability and controllability. In this paper, we address the au...

  6. Classification in Australia.

    Science.gov (United States)

    McKinlay, John

    Despite some inroads by the Library of Congress Classification and short-lived experimentation with Universal Decimal Classification and Bliss Classification, Dewey Decimal Classification, with its ability in recent editions to be hospitable to local needs, remains the most widely used classification system in Australia. Although supplemented at…

  7. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  8. Multi-borders classification

    OpenAIRE

    Mills, Peter

    2014-01-01

    The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification problems into binary classification problems is specified using a recursive control language.

  9. Sports Type Classification using Signature Heatmaps

    DEFF Research Database (Denmark)

    Gade, Rikke; Moeslund, Thomas B.

    2013-01-01

    Automatic classification of activities in a sports arena is important in order to analyse and optimise the use of the arenas. In this work we classify five sports types based only on occupancy heatmaps produced from position data. Due to privacy issues we use thermal imaging for detecting people...... and then calculate their positions on the court us- ing homography. Heatmaps are produced by summarising Gaussian distributions respresenting people over 10-minute periods. Before classification the heatmaps are projected to a low-dimensional discriminative space using the principle of Fisherfaces. Our result using...

  10. Automated urban features classification and recognition from combined RGB/lidar data

    Science.gov (United States)

    Elhifnawy Eid, Hassan Elsaid

    2011-12-01

    Although a Red, Green and Blue (RGB) image provides rich semantic information for different features, it is difficult to extract and separate features which share similar texture properties. The data provided by a LIght Detection And Ranging (LIDAR) system contain dense spatial information for terrain and non-terrain objects, but feature extraction poses difficulties in separating different features sharing the same height information. The thesis objective is to introduce an automated urban classification technique using combined semantic and spatial information leading to the ability to extract different features efficiently. RGB color channels are used to produce two color invariant images for vegetation and shadowy areas identification. Otsu segmentation is applied on these color invariant images to identify shadows and vegetation candidates from each other. An RGB image is transformed into two other color spaces, YCbCr and HSV. Luminance color channel is extracted from YCbCr color space, while hue and saturation color channels are extracted from HSV color space. Global thresholding is applied on these color channels individually and collectively for detecting sandy areas. Wavelet transform is used for detecting building boundaries from LIDAR height data. Final building candidates are identified after removing vegetation areas from the resulting image of extracted buildings from LIDAR data. After successful building extraction using wavelets and vegetation, sandy and shadowy areas from an RGB, remaining features will be the roads. This new filter combination introduces a highly efficient automatic urban classification approach from combined LIDAR/RGB data. The proposed urban classification algorithm will introduce classified libraries for several features and in order to use this output efficiently an independent search algorithm is required. An efficient texture and boundary search algorithm is introduced for automatic object recognition of buildings using both

  11. Classification of Small UAVs and Birds by Micro-Doppler Signatures

    NARCIS (Netherlands)

    Molchanov, P.; Egiazarian, K.; Astola, J.; Harmanny, R.I.A.; Wit, J.J.M. de

    2013-01-01

    The problem of unmanned aerial vehicles classification using continuous wave radar is considered in this paper. Classification features are extracted from micro-Doppler signature. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by

  12. Automatic Complexity Analysis

    DEFF Research Database (Denmark)

    Rosendahl, Mads

    1989-01-01

    One way to analyse programs is to to derive expressions for their computational behaviour. A time bound function (or worst-case complexity) gives an upper bound for the computation time as a function of the size of input. We describe a system to derive such time bounds automatically using abstract...

  13. Classification of sports types from tracklets

    DEFF Research Database (Denmark)

    Gade, Rikke; Moeslund, Thomas B.

    that sports types can be classified from features extracted from short trajectories of the players. From tracklets created by a Kalman filter tracker we extract four robust features; Total distance, lifespan, distance span and mean speed. For clas- sification we use a quadratic discriminant analysis. In our...... experiments we use 30 2-minutes thermal video sequences from each of five different sports types. By applying a 10- fold cross validation we obtain a correct classification rate of 94.5 %.......Automatic analysis of video is important in order to process and exploit large amounts of data, e.g. for sports analysis. Classification of sports types is one of the first steps to- wards a fully automatic analysis of the activities performed at sports arenas. In this work we test the idea...

  14. Remote Sensing Information Classification

    Science.gov (United States)

    Rickman, Douglas L.

    2008-01-01

    This viewgraph presentation reviews the classification of Remote Sensing data in relation to epidemiology. Classification is a way to reduce the dimensionality and precision to something a human can understand. Classification changes SCALAR data into NOMINAL data.

  15. SINGULAR POINT DETECTION FOR EFFICIENT FINGERPRINT CLASSIFICATION

    OpenAIRE

    Ali Ismail Awad; Kensuke Baba

    2012-01-01

    A singular point or singularity on fingerprint is considered as a fingerprint landmark due its scale, shift, and rotation immutability. It is used for both fingerprint classification and alignment in automatic fingerprint identification systems. This paper presents a comparative study between two singular point detection methods available in the literature. The comparative study has been conducted on the Poincaré index and the complex filter methods, and it aims to catch the optimum singular...

  16. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  17. Automatically operated maglev public transport line in Nagoya

    Energy Technology Data Exchange (ETDEWEB)

    Hibi, Osamu [Aichi Rapid Transit Co., Aichi Prefecture (Japan). Engineering Dept.

    2006-07-01

    Tobu-Kyuryo-Line in Nagoya (Japan) is a mid-size automatically operated passenger line which adopts the HSST (high-speed surface transport) system. The vehicles are levitated with normal conducting electromagnets and propelled by a linear induction motor. Low noise by levitation and stable acceleration by the linear induction motor improve the riding comfort and ensure a faster transportation. Automatic operation is indispensable for stable operation and reduction of running costs. In the opening year, the Tobu-Kyuryo-Line played an important role as an access to EXPO2005 by carrying 20 million passengers. So far it had no serious trouble and has been operated safely. (orig.)

  18. Meta-classification for Variable Stars

    Science.gov (United States)

    Pichara, Karim; Protopapas, Pavlos; León, Daniel

    2016-03-01

    The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New scientific problems emerge, and it is critical to be able to reuse the models learned before, without rebuilding everything from the beginning when the sciencientific problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. A conventional mixture of expert algorithms in machine learning literature cannot be used since each expert (model) uses different inputs. We also consider the computational complexity of the model by using the most expensive models only when it is necessary. We test our model with EROS-2 and MACHO data sets, and we show that we solve most of the classification challenges only by training a meta-model to learn how to integrate the previous experts.

  19. BROAD PHONEME CLASSIFICATION USING SIGNAL BASED FEATURES

    Directory of Open Access Journals (Sweden)

    Deekshitha G

    2014-12-01

    Full Text Available Speech is the most efficient and popular means of human communication Speech is produced as a sequence of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC features derived through short time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme classification is achieved using features derived directly from the speech at the signal level itself. Broad phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR, short time energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first three formants. Features derived from short time frames of training speech are used to train a multilayer feedforward neural network based classifier with manually marked class label as output and classification accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction which is useful for applications such as automatic speech recognition and automatic language identification.

  20. Cross-ontological analytics for alignment of different classification schemes

    Science.gov (United States)

    Posse, Christian; Sanfilippo, Antonio P; Gopalan, Banu; Riensche, Roderick M; Baddeley, Robert L

    2010-09-28

    Quantification of the similarity between nodes in multiple electronic classification schemes is provided by automatically identifying relationships and similarities between nodes within and across the electronic classification schemes. Quantifying the similarity between a first node in a first electronic classification scheme and a second node in a second electronic classification scheme involves finding a third node in the first electronic classification scheme, wherein a first product value of an inter-scheme similarity value between the second and third nodes and an intra-scheme similarity value between the first and third nodes is a maximum. A fourth node in the second electronic classification scheme can be found, wherein a second product value of an inter-scheme similarity value between the first and fourth nodes and an intra-scheme similarity value between the second and fourth nodes is a maximum. The maximum between the first and second product values represents a measure of similarity between the first and second nodes.

  1. Shadow detection and removal in RGB VHR images for land use unsupervised classification

    Science.gov (United States)

    Movia, A.; Beinat, A.; Crosilla, F.

    2016-09-01

    Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

  2. Discriminative Chemical Patterns: Automatic and Interactive Design.

    Science.gov (United States)

    Bietz, Stefan; Schomburg, Karen T; Hilbig, Matthias; Rarey, Matthias

    2015-08-24

    The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios.

  3. Product Classification in E-Commerce using Distributional Semantics

    OpenAIRE

    Gupta, Vivek; Karnick, Harish; Bansal, Ashendra; Jhala, Pradhuman

    2016-01-01

    Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also d...

  4. Automatic trend estimation

    CERN Document Server

    Vamos¸, C˘alin

    2013-01-01

    Our book introduces a method to evaluate the accuracy of trend estimation algorithms under conditions similar to those encountered in real time series processing. This method is based on Monte Carlo experiments with artificial time series numerically generated by an original algorithm. The second part of the book contains several automatic algorithms for trend estimation and time series partitioning. The source codes of the computer programs implementing these original automatic algorithms are given in the appendix and will be freely available on the web. The book contains clear statement of the conditions and the approximations under which the algorithms work, as well as the proper interpretation of their results. We illustrate the functioning of the analyzed algorithms by processing time series from astrophysics, finance, biophysics, and paleoclimatology. The numerical experiment method extensively used in our book is already in common use in computational and statistical physics.

  5. Stability Control of Vehicle Emergency Braking with Tire Blowout

    Directory of Open Access Journals (Sweden)

    Qingzhang Chen

    2014-01-01

    Full Text Available For the stability control and slowing down the vehicle to a safe speed after tire failure, an emergency automatic braking system with independent intellectual property is developed. After the system has received a signal of tire blowout, the automatic braking mode of the vehicle is determined according to the position of the failure tire and the motion state of vehicle, and a control strategy for resisting tire blowout additional yaw torque and deceleration is designed to slow down vehicle to a safe speed in an expected trajectory. The simulating test system is also designed, and the testing results show that the vehicle can be quickly stabilized and kept in the original track after tire blowout with the emergency braking system described in the paper.

  6. Automatic Program Reports

    OpenAIRE

    Lígia Maria da Silva Ribeiro; Gabriel de Sousa Torcato David

    2007-01-01

    To profit from the data collected by the SIGARRA academic IS, a systematic setof graphs and statistics has been added to it and are available on-line. Thisanalytic information can be automatically included in a flexible yearly report foreach program as well as in a synthesis report for the whole school. Somedifficulties in the interpretation of some graphs led to the definition of new keyindicators and the development of a data warehouse across the university whereeffective data consolidation...

  7. Automatic food decisions

    DEFF Research Database (Denmark)

    Mueller Loose, Simone

    Consumers' food decisions are to a large extent shaped by automatic processes, which are either internally directed through learned habits and routines or externally influenced by context factors and visual information triggers. Innovative research methods such as eye tracking, choice experiments...... and food diaries allow us to better understand the impact of unconscious processes on consumers' food choices. Simone Mueller Loose will provide an overview of recent research insights into the effects of habit and context on consumers' food choices....

  8. Automatic Differentiation Variational Inference

    OpenAIRE

    Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.

    2016-01-01

    Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, which makes it difficult to efficiently cycle through the steps. To this end, we develop automatic differentiation variational inference (ADVI). Using our method, the scientist on...

  9. AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2016-06-01

    Full Text Available In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

  10. Automatic Extraction of Building Outline from High Resolution Aerial Imagery

    Science.gov (United States)

    Wang, Yandong

    2016-06-01

    In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

  11. Feasibility study of an automatic vehicle for planetary exploration

    Science.gov (United States)

    Gerli, C.; Murolo, A.; Mugnuolo, R.; Gallo, E.; Cantatore, F.; Giardino, L.

    1993-01-01

    A study with the following objectives is reported: definition of the scientific objectives of a planetary exploration using a rover; definition of the planetary rover requirements; identification and characterization of the main subsystems of the rover; definition and critical areas and technological risks; and verification of the possibility on international cooperation on a planetary mission. The use of such a rover to investigate the Moon and Mars is focused upon.

  12. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...

  13. Beoordeling van het Automatic Rear Fog Flashlight ARFF-systeem (knipperende mistachterlampen) : een notitie ten behoeve van het Verbond van Verzekeraars.

    NARCIS (Netherlands)

    Schoon, C.C.

    1996-01-01

    The Automatic Rear Fog Flashlight (ARFF) system is an electronic circuit for activating the rear fog flashlights when braking a vehicle with the rear foglights switched on. The system aims to warn drivers of following vehicles for braking vehicles that are in front of them, in circumstances of bad v

  14. Classification rules for Indian Rice diseases

    Directory of Open Access Journals (Sweden)

    A. Nithya

    2011-01-01

    Full Text Available Many techniques have been developed for learning rules and relationships automatically from diverse data sets, to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. Decision tree is one of learning algorithm which posses certain advantages that make it suitable for discovering the classification rule for data mining applications. Normally Decision trees widely used learning method and do not require any prior knowledge of data distribution, works well on noisy data .It has been applied to classify Rice disease based on the symptoms. This paper intended to discover classification rules for the Indian rice diseases using the c4.5 decision trees algorithm. Expert systems have been used in agriculture since the early 1980s. Several systems have been developed in different countries including the USA, Europe, and Egypt for plant-disorder diagnosis, management and other production aspects. This paper explores what Classification rule can do in the agricultural domain.

  15. Automatic design of decision-tree algorithms with evolutionary algorithms.

    Science.gov (United States)

    Barros, Rodrigo C; Basgalupp, Márcio P; de Carvalho, André C P L F; Freitas, Alex A

    2013-01-01

    This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

  16. Vehicle Real-time Location Based on Visual Perception Model

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.

  17. HUMAN-SIMULATING VEHICLE STEERING CONTROL ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    XU Youchun; LI Keqiang; CHANG Ming; CHEN Jun

    2006-01-01

    A new vehicle steering control algorithm is presented. Unlike the traditional methods do,the algorithm uses a sigmoid function to describe the principle of the human driver's steering strategy.Based on this function, a human simulating vehicle steering model, human-simulating steering control(HS) algorithm is designed. In order to improve the adaptability to different environments, a parameter adaptive adjustment algorithm is presented. This algorithm can online modify the value of the key parameters of the HS real time. HS controller is used on a vehicle equipped with computer vision system and computer controlled steering actuator system, the result from the automatic vehicle steering experiment shows that the HS algorithm gives good performance at different speed, even at the maximum speed of 172 km/h.

  18. MALLS - Mobile Automatic Launch and Landing Station for VTOL UAVs

    OpenAIRE

    Gising, Andreas

    2008-01-01

    The market for vertical takeoff and landing unmanned aerial vehicles, VTOL UAVs, is growing rapidly. To reciprocate the demand of VTOL UAVs in offshore applications, CybAero has developed a novel concept for landing on moving objects called MALLS, Mobile Automatic Launch and Landing Station. MALLS can tilt its helipad and is supposed to align to either the horizontal plane with an operator adjusted offset or to the helicopter skids. Doing so, eliminates the gyroscopic forces otherwise induced...

  19. Differential Risk of Injury in Child Occupants by Passenger Car Classification

    OpenAIRE

    Kallan, Michael J.; Durbin, Dennis R.; Elliott, Michael R.; Menon, Rajiv A.; Winston, Flaura K.

    2003-01-01

    In the United States, passenger cars are the most common passenger vehicle, yet they vary widely in size and crashworthiness. Using data collected from a population-based sample of crashes in State Farm-insured vehicles, we quantified the risk of injury to child occupants by passenger car size and classification. Injury risk is predicted by vehicle weight; however, there is an increased risk in both Large vs. Luxury and Sports vs. Small cars, despite similar average vehicle weights in both co...

  20. Ontology Building Using Classification Rules and Discovered Concepts

    Directory of Open Access Journals (Sweden)

    Gorskis Henrihs

    2015-12-01

    Full Text Available Building an ontology is a difficult and time-consuming task. In order to make this task easier and faster, some automatic methods can be employed. This paper examines the feasibility of using rules and concepts discovered during the classification tree building process in the C4.5 algorithm, in a completely automated way, for the purposes of building an ontology from data. By building the ontology directly from continuous data, concepts and relations can be discovered without specific knowledge about the domain. This paper also examines how this method reproduces the classification capabilities of the classification three within an ontology using concepts and class expression axioms.

  1. Automatic Configuration in NTP

    Institute of Scientific and Technical Information of China (English)

    Jiang Zongli(蒋宗礼); Xu Binbin

    2003-01-01

    NTP is nowadays the most widely used distributed network time protocol, which aims at synchronizing the clocks of computers in a network and keeping the accuracy and validation of the time information which is transmitted in the network. Without automatic configuration mechanism, the stability and flexibility of the synchronization network built upon NTP protocol are not satisfying. P2P's resource discovery mechanism is used to look for time sources in a synchronization network, and according to the network environment and node's quality, the synchronization network is constructed dynamically.

  2. Current challenges in autonomous vehicle development

    Science.gov (United States)

    Connelly, J.; Hong, W. S.; Mahoney, R. B., Jr.; Sparrow, D. A.

    2006-05-01

    The field of autonomous vehicles is a rapidly growing one, with significant interest from both government and industry sectors. Autonomous vehicles represent the intersection of artificial intelligence (AI) and robotics, combining decision-making with real-time control. Autonomous vehicles are desired for use in search and rescue, urban reconnaissance, mine detonation, supply convoys, and more. The general adage is to use robots for anything dull, dirty, dangerous or dumb. While a great deal of research has been done on autonomous systems, there are only a handful of fielded examples incorporating machine autonomy beyond the level of teleoperation, especially in outdoor/complex environments. In an attempt to assess and understand the current state of the art in autonomous vehicle development, a few areas where unsolved problems remain became clear. This paper outlines those areas and provides suggestions for the focus of science and technology research. The first step in evaluating the current state of autonomous vehicle development was to develop a definition of autonomy. A number of autonomy level classification systems were reviewed. The resulting working definitions and classification schemes used by the authors are summarized in the opening sections of the paper. The remainder of the report discusses current approaches and challenges in decision-making and real-time control for autonomous vehicles. Suggested research focus areas for near-, mid-, and long-term development are also presented.

  3. Purging Musical Instrument Sample Databases Using Automatic Musical Instrument Recognition Methods

    OpenAIRE

    Livshin, Arie; Rodet, Xavier

    2009-01-01

    cote interne IRCAM: Livshin09a None / None National audience Compilation of musical instrument sample databases requires careful elimination of badly recorded samples and validation of sample classification into correct categories. This paper introduces algorithms for automatic removal of bad instrument samples using Automatic Musical Instrument Recognition and Outlier Detection techniques. Best evaluation results on a methodically contaminated sound database are achieved using the i...

  4. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles.

    Science.gov (United States)

    Zhang, Linhuan; Ahamed, Tofael; Zhang, Yan; Gao, Pengbo; Takigawa, Tomohiro

    2016-04-22

    The aim of this study was to design a navigation system composed of a human-controlled leader vehicle and a follower vehicle. The follower vehicle automatically tracks the leader vehicle. With such a system, a human driver can control two vehicles efficiently in agricultural operations. The tracking system was developed for the leader and the follower vehicle, and control of the follower was performed using a camera vision system. A stable and accurate monocular vision-based sensing system was designed, consisting of a camera and rectangular markers. Noise in the data acquisition was reduced by using the least-squares method. A feedback control algorithm was used to allow the follower vehicle to track the trajectory of the leader vehicle. A proportional-integral-derivative (PID) controller was introduced to maintain the required distance between the leader and the follower vehicle. Field experiments were conducted to evaluate the sensing and tracking performances of the leader-follower system while the leader vehicle was driven at an average speed of 0.3 m/s. In the case of linear trajectory tracking, the RMS errors were 6.5 cm, 8.9 cm and 16.4 cm for straight, turning and zigzag paths, respectively. Again, for parallel trajectory tracking, the root mean square (RMS) errors were found to be 7.1 cm, 14.6 cm and 14.0 cm for straight, turning and zigzag paths, respectively. The navigation performances indicated that the autonomous follower vehicle was able to follow the leader vehicle, and the tracking accuracy was found to be satisfactory. Therefore, the developed leader-follower system can be implemented for the harvesting of grains, using a combine as the leader and an unloader as the autonomous follower vehicle.

  5. Automatic contrast phase estimation in CT volumes.

    Science.gov (United States)

    Sofka, Michal; Wu, Dijia; Sühling, Michael; Liu, David; Tietjen, Christian; Soza, Grzegorz; Zhou, S Kevin

    2011-01-01

    We propose an automatic algorithm for phase labeling that relies on the intensity changes in anatomical regions due to the contrast agent propagation. The regions (specified by aorta, vena cava, liver, and kidneys) are first detected by a robust learning-based discriminative algorithm. The intensities inside each region are then used in multi-class LogitBoost classifiers to independently estimate the contrast phase. Each classifier forms a node in a decision tree which is used to obtain the final phase label. Combining independent classification from multiple regions in a tree has the advantage when one of the region detectors fail or when the phase training example database is imbalanced. We show on a dataset of 1016 volumes that the system correctly classifies native phase in 96.2% of the cases, hepatic dominant phase (92.2%), hepatic venous phase (96.7%), and equilibrium phase (86.4%) in 7 seconds on average. PMID:22003696

  6. Automatic personnel contamination monitor

    International Nuclear Information System (INIS)

    United Nuclear Industries, Inc. (UNI) has developed an automatic personnel contamination monitor (APCM), which uniquely combines the design features of both portal and hand and shoe monitors. In addition, this prototype system also has a number of new features, including: micro computer control and readout, nineteen large area gas flow detectors, real-time background compensation, self-checking for system failures, and card reader identification and control. UNI's experience in operating the Hanford N Reactor, located in Richland, Washington, has shown the necessity of automatically monitoring plant personnel for contamination after they have passed through the procedurally controlled radiation zones. This final check ensures that each radiation zone worker has been properly checked before leaving company controlled boundaries. Investigation of the commercially available portal and hand and shoe monitors indicated that they did not have the sensitivity or sophistication required for UNI's application, therefore, a development program was initiated, resulting in the subject monitor. Field testing shows good sensitivity to personnel contamination with the majority of alarms showing contaminants on clothing, face and head areas. In general, the APCM has sensitivity comparable to portal survey instrumentation. The inherit stand-in, walk-on feature of the APCM not only makes it easy to use, but makes it difficult to bypass. (author)

  7. Automatic detection of sleep macrostructure based on a sensorized T-shirt.

    Science.gov (United States)

    Bianchi, Anna M; Mendez, Martin O

    2010-01-01

    In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained classification was compared to the standard clinical practice based on polysomnography (PSG). In the present work we combined T-shirt recordings and automatic classification and could obtain reliable sleep profiles, i.e. the sleep classification in WAKE, REM (rapid eye movement) and NREM stages, based on heart rate variability (HRV), respiration and movement signals. PMID:21096842

  8. Retinal vasculature classification using novel multifractal features

    International Nuclear Information System (INIS)

    Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms. (paper)

  9. Enhancing Accuracy of Plant Leaf Classification Techniques

    Directory of Open Access Journals (Sweden)

    C. S. Sumathi

    2014-03-01

    Full Text Available Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. Living beings also depend on plants for their food, hence it is of great importance to know about the plants growing around us and to preserve them. Automatic plant leaf classification is widely researched. This paper investigates the efficiency of learning algorithms of MLP for plant leaf classification. Incremental back propagation, Levenberg–Marquardt and batch propagation learning algorithms are investigated. Plant leaf images are examined using three different Multi-Layer Perceptron (MLP modelling techniques. Back propagation done in batch manner increases the accuracy of plant leaf classification. Results reveal that batch training is faster and more accurate than MLP with incremental training and Levenberg– Marquardt based learning for plant leaf classification. Various levels of semi-batch training used on 9 species of 15 sample each, a total of 135 instances show a roughly linear increase in classification accuracy.

  10. Transportation Modes Classification Using Sensors on Smartphones

    Directory of Open Access Journals (Sweden)

    Shih-Hau Fang

    2016-08-01

    Full Text Available This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

  11. Transportation Modes Classification Using Sensors on Smartphones.

    Science.gov (United States)

    Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

    2016-08-19

    This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

  12. Transportation Modes Classification Using Sensors on Smartphones.

    Science.gov (United States)

    Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

    2016-01-01

    This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes. PMID:27548182

  13. Accelerometry-Based Classification of Human Activities Using Markov Modeling

    Directory of Open Access Journals (Sweden)

    Andrea Mannini

    2011-01-01

    Full Text Available Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM classifiers are studied by contrasting them with Gaussian Mixture Model (GMM classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.

  14. Object classification using local subspace projection

    Science.gov (United States)

    Nealy, Jennifer; Muise, Robert

    2011-06-01

    We consider the problem of object classification from image data. Significant challenges are presented when objects can be imaged from different view angles and have different distortions. For example, a vehicle will appear completely different depending on the viewing angle of the sensor but must still be classified as the same vehicle. In regards to face recognition, a person may have a variety of facial expressions and a pattern recognition algorithm would need to account for these distortions. Traditional algorithms such as PCA filters are linear in nature and cannot account for the underlying non-linear structure which characterizes an object. We examine nonlinear manifold techniques applied to the pattern recognition problem. One mathematical construct receiving significant research attention is diffusion maps, whereby the underlying training data are remapped so that Euclidean distance in the mapped data is equivalent to the manifold distance of the original dataset. This technique has been used successfully for applications such as data organization, noise filtering, and anomaly detection with only limited experiments with object classification. For very large datasets (size N), pattern classification with diffusion maps becomes rather onerous as there is a requirement for the eigenvectors of an NxN matrix. We characterize the performance of a 40 person facial recognition problem with standard K-NN classifier, a diffusion distance classifier, and standard PCA. We then develop a local subspace projection algorithm which approximates the diffusion distance without the prohibitive computations and shows comparable classification performance.

  15. Automatic and efficient driving strategies while approaching a traffic light

    CERN Document Server

    Treiber, Martin

    2014-01-01

    Vehicle-infrastructure communication opens up new ways to improve traffic flow efficiency at signalized intersections. In this study, we assume that equipped vehicles can obtain information about switching times of relevant traffic lights in advance. This information is used to improve traffic flow by the strategies 'early braking', 'anticipative start', and 'flying start'. The strategies can be implemented in driver-information mode, or in automatic mode by an Adaptive Cruise Controller (ACC). Quality criteria include cycle-averaged capacity, driving comfort, fuel consumption, travel time, and the number of stops. By means of simulation, we investigate the isolated strategies and the complex interactions between the strategies and between equipped and non-equipped vehicles. As universal approach to assess equipment level effects we propose relative performance indexes and found, at a maximum speed of 50 km/h, improvements of about 15% for the number of stops and about 4% for the other criteria. All figures d...

  16. Automatic target tracking on multi-resolution terrain

    Institute of Scientific and Technical Information of China (English)

    WAN Ming; ZHANG Wei; MURRAY Marie O.; KAUFMAN Arie

    2006-01-01

    We propose a high-performance path planning algorithm for automatic target tracking in the applications of real-time simulation and visualization of large-scale terrain datasets, with a large number of moving objects (such as vehicles) tracking multiple moving targets. By using a modified Dijkstra's algorithm, an optimal path between each vehicle-target pair over a weighted grid-presented terrain is computed and updated to eliminate the problem of local minima and losing of tracking. Then, a dynamic path re-planning strategy using multi-resolution representation of a dynamic updating region is proposed to achieve high-performance by trading-off precision for efficiency, while guaranteeing accuracy. Primary experimental results showed that our algorithm successfully achieved 10 to 96 frames per second interactive path-replanning rates during a terrain simulation scenario with 10 to 100 vehicles and multiple moving targets.

  17. Hand eczema classification

    DEFF Research Database (Denmark)

    Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M;

    2008-01-01

    of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

  18. Classification of articulators.

    Science.gov (United States)

    Rihani, A

    1980-03-01

    A simple classification in familiar terms with definite, clear characteristics can be adopted. This classification system is based on the number of records used and the adjustments necessary for the articulator to accept these records. The classification divides the articulators into nonadjustable, semiadjustable, and fully adjustable articulators (Table I). PMID:6928204

  19. Forestry Vehicle

    Science.gov (United States)

    1982-01-01

    Power Pack II provides an economical means of moving a power source into remote roadless forest areas. It was developed by Prof. Miles and his associates, working in cooperation with the University of California's Department of Forestry. The team combined its own design of an all-terrain vehicle with a suspension system based on the NASA load equalization technology. Result is an intermediate-sized unit which carries a power source and the powered tools to perform a variety of forest management tasks which cannot be done economically with current equipment. Power Pack II can traverse very rough terrain and climb a 60 degree slope; any one of the wheels can move easily over an obstacle larger than itself. Work is being done on a more advanced Power Pack III.

  20. Vehicle Development Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — FUNCTION: Supports the development of prototype deployment platform vehicles for offboard countermeasure systems. DESCRIPTION: The Vehicle Development Laboratory is...

  1. Volume component analysis for classification of LiDAR data

    Science.gov (United States)

    Varney, Nina M.; Asari, Vijayan K.

    2015-03-01

    One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach.1 This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block. This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%

  2. A Disaster Document Classification Technique Using Domain Specific Ontologies

    Directory of Open Access Journals (Sweden)

    Qazi Mudassar Ilyas

    2015-12-01

    Full Text Available Manual data collection and entry is one of the bottlenecks in conventional disaster management information systems. Time is a critical factor in emergency situations and timely data collection and processing may help in saving several lives. An effective disaster management system needs to collect data from World Wide Web automatically. A prerequisite for data collection process is document classification mechanism to classify a particular document into different categories. Ontologies are formal bodies of knowledge used to capture machine understandable semantics of a domain of interest and have been used successfully to support document classification in various domains. This paper presents an ontology-based document classification technique for automatic data collection in a disaster management system. A general ontology of disasters is used that contains the description of several natural and man-made disasters. The proposed technique augments the conventional classification measures with the ontological knowledge to improve the precision of classification. A preliminary implementation of the proposed technique shows promising results with up to 10% overall improvement in precision when compared with conventional classification methods.

  3. Classification, disease, and diagnosis.

    Science.gov (United States)

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  4. Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung

    NARCIS (Netherlands)

    Sluimer, Ingrid C.; Prokop, Mathias; Hartmann, Ieneke; van Ginneken, Bram

    2006-01-01

    An automatic method for textural analysis of complete HRCT lung slices is presented. The system performs classification of regions of interest (ROIs) into one of six classes: normal, hyperlucency, fibrosis, ground glass, solid, and focal. We propose a novel method of automatically generating ROIs th

  5. Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms.

    Science.gov (United States)

    Lindner, Claudia; Wang, Ching-Wei; Huang, Cheng-Ta; Li, Chung-Hsing; Chang, Sheng-Wei; Cootes, Tim F

    2016-01-01

    Cephalometric tracing is a standard analysis tool for orthodontic diagnosis and treatment planning. The aim of this study was to develop and validate a fully automatic landmark annotation (FALA) system for finding cephalometric landmarks in lateral cephalograms and its application to the classification of skeletal malformations. Digital cephalograms of 400 subjects (age range: 7-76 years) were available. All cephalograms had been manually traced by two experienced orthodontists with 19 cephalometric landmarks, and eight clinical parameters had been calculated for each subject. A FALA system to locate the 19 landmarks in lateral cephalograms was developed. The system was evaluated via comparison to the manual tracings, and the automatically located landmarks were used for classification of the clinical parameters. The system achieved an average point-to-point error of 1.2 mm, and 84.7% of landmarks were located within the clinically accepted precision range of 2.0 mm. The automatic landmark localisation performance was within the inter-observer variability between two clinical experts. The automatic classification achieved an average classification accuracy of 83.4% which was comparable to an experienced orthodontist. The FALA system rapidly and accurately locates and analyses cephalometric landmarks in lateral cephalograms, and has the potential to significantly improve the clinical work flow in orthodontic treatment. PMID:27645567

  6. Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets

    Science.gov (United States)

    Molina, M. M.; Luna, J. M.; Romero, C.; Ventura, S.

    2012-01-01

    This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

  7. An Investigation of Automatic Change Detection for Topographic Map Updating

    Science.gov (United States)

    Duncan, P.; Smit, J.

    2012-08-01

    Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

  8. Transmissions in vehicles 2010

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2010-07-01

    Within the international VDI congress 'Gears in vehicles 2010' of the VDI Wissensforum GmbH (Duesseldorf, Federal Republic of Germany) between 22nd and 23rd June, 2010, in Friedrichshafen (Federal Republic of Germany), the following lectures were held: (1) 8HP70H - The moldhybrid transmission from ZF - Cjallenges and achievements (P. Gutmann); (2) GETRAG boosted range extender - A highly flexible electric powertrain for maximum CO{sub 2} reduction (S. Huepkes); (3) E-Transmission between full-hybrid and E-drive (P. Tenberge); (4) Reducing NO{sub x} and particulate emissions in electrified drivelines (R. Kuberczyk); (5) Simulation aided HEV and EV development: from the component to the whole powertrain (A. Gacometti); (6) Investigations on operating behaviour of the optimized CVT hybrid driveline (B.-R. Hoehn); (7) Customer-oriented dimensioning of electrified drivetrains (M. Eghtessad); (8) Decentralized optimal control strategy for parallel hybrid electric vehicles (A. Frenkel); (9) The new generation 6-speed automatic transmission AF40 (G. Bednarek); (10) Customized mechatronic solutions for integrated transmission control units (M. Wieczorek); (11) The optimal automatic transmission for front-transverse applications - Planetary transmissions or dual clutch transmissions? (G. Gumpoltsberger); (12) The new shift-by-wire gearshift lever for the Audi A8 - Requirements and concept (T. Guttenbergere); (13) The new shift-by-wire gearshift lever for the Audi A8 - Realization (A. Giefer); (14) Fuel-efficient transmissions of the future: Calculation of the efficiency factor for vehicle transmissions (B. Volpert); (15) HT-ACM: A new polymer generation for static and dynamic gearbox sealing solutions (E. Osen); (16) 'Energy efficiency equipped solutions by SKF' for power train applications - A contribution to CO{sub 2} - emission reduction and sustainability (T. Bobke); (17) 6-Ratio planetary shift transmission controlled by 4 external brakes, and design

  9. Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

    Directory of Open Access Journals (Sweden)

    Stefan Dech

    2012-09-01

    Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

  10. Electronic amplifiers for automatic compensators

    CERN Document Server

    Polonnikov, D Ye

    1965-01-01

    Electronic Amplifiers for Automatic Compensators presents the design and operation of electronic amplifiers for use in automatic control and measuring systems. This book is composed of eight chapters that consider the problems of constructing input and output circuits of amplifiers, suppression of interference and ensuring high sensitivity.This work begins with a survey of the operating principles of electronic amplifiers in automatic compensator systems. The succeeding chapters deal with circuit selection and the calculation and determination of the principal characteristics of amplifiers, as

  11. The Automatic Telescope Network (ATN)

    CERN Document Server

    Mattox, J R

    1999-01-01

    Because of the scheduled GLAST mission by NASA, there is strong scientific justification for preparation for very extensive blazar monitoring in the optical bands to exploit the opportunity to learn about blazars through the correlation of variability of the gamma-ray flux with flux at lower frequencies. Current optical facilities do not provide the required capability.Developments in technology have enabled astronomers to readily deploy automatic telescopes. The effort to create an Automatic Telescope Network (ATN) for blazar monitoring in the GLAST era is described. Other scientific applications of the networks of automatic telescopes are discussed. The potential of the ATN for science education is also discussed.

  12. GSM-GPS Based Intelligent Security and Control System for Vehicle

    Directory of Open Access Journals (Sweden)

    Mr. Kiran Gaikwad

    2013-05-01

    Full Text Available The revolution of Mobile and Technology has made ‘GSM based vehicle security system’. The vehicle security system is prominent worldwide. But it is not so much secure system. Every vehicle owner wants maximum protection of his vehicle; otherwise thief can easily trap the vehicle. So, by combing the idea of mobile and vehicle security system GSM based vehicle security system can be designed. So this GSM-GPS based vehicle security system works when someone tries to steal your vehicle. This paper deals with the design {&} development of an embedded system, which is being used to prevent/control the theft of a vehicle. The instrument is an embedded system based on GSM and GPS technology. The instrument is installed in the engine of the vehicle. The main objective of this instrument is to protect the vehicle from any unauthorized access, through entering a protected password and intimate the status and location of the same vehicle to the authorize person (owner using Global System for Mobile Communication (GSM and Global Positioning System (GPS technology. Here owner of vehicle can control system through Cell phone or a personal computer (PC. In this system new concept is inclusion of RTC (Real Time Clock by which vehicle can be permanently off depending upon date and time set. This system is intelligent because it performs many tasks automatically and also control vehicle on/off from a distance

  13. Generating Best Features for Web Page Classification

    Directory of Open Access Journals (Sweden)

    K. Selvakuberan

    2008-03-01

    Full Text Available As the Internet provides millions of web pages for each and every search term, getting interesting and required results quickly from the Web becomes very difficult. Automatic classification of web pages into relevant categories is the current research topic which helps the search engine to get relevant results. As the web pages contain many irrelevant, infrequent and stop words that reduce the performance of the classifier, extracting or selecting representative features from the web page is an essential pre-processing step. The goal of this paper is to find minimum number of highly qualitative features by integrating feature selection techniques. We conducted experiments with various numbers of features selected by different feature selection algorithms on a well defined initial set of features and show that cfssubset evaluator combined with term frequency method gives minimal qualitative features enough to attain considerable classification accuracy.

  14. A Classification-based Review Recommender

    Science.gov (United States)

    O'Mahony, Michael P.; Smyth, Barry

    Many online stores encourage their users to submit product/service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews. We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the mosthelpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.

  15. Recursive heuristic classification

    Science.gov (United States)

    Wilkins, David C.

    1994-01-01

    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  16. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  17. A simplified immunohistochemical classification of skeletal muscle fibres in mouse

    Directory of Open Access Journals (Sweden)

    M. Kammoun

    2014-06-01

    Full Text Available The classification of muscle fibres is of particular interest for the study of the skeletal muscle properties in a wide range of scientific fields, especially animal phenotyping. It is therefore important to define a reliable method for classifying fibre types. The aim of this study was to establish a simplified method for the immunohistochemical classification of fibres in mouse. To carry it out, we first tested a combination of several anti myosin heavy chain (MyHC antibodies in order to choose a minimum number of antibodies to implement a semi-automatic classification. Then, we compared the classification of fibres to the MyHC electrophoretic pattern on the same samples. Only two anti MyHC antibodies on serial sections with the fluorescent labeling of the Laminin were necessary to classify properly fibre types in Tibialis Anterior and Soleus mouse muscles in normal physiological conditions. This classification was virtually identical to the classification realized by the electrophoretic separation of MyHC. This immunohistochemical classification can be applied to the total area of Tibialis Anterior and Soleus mouse muscles. Thus, we provide here a useful, simple and time-efficient method for immunohistochemical classification of fibres, applicable for research in mouse

  18. A Survey on Data Classification using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Dr. Chandra.E

    2011-10-01

    Full Text Available Data classification is an essential step in trying to safeguard one of the chief properties of an organization -its information. Data classification engages categorizing information to predefined levels. For every level, organizations must allocate different levels of security controls and restrict the number of employees or persons who can access the information. Data classification would be difficult in large organizations with significant content to estimate and classify. With the assistance of a data classification approach, organizations can recognize and implement proper setting for private access control and also can obtain decision based on the obtained result from data classification. Machine learning techniques are one of the best classification approaches in the recent development. Many techniques are available under the machine learning approaches and the most important objective of machine learning research is to automatically learn to identify complex patterns and make intelligent decisions based on data. In this paper, data classification usingmany Machine Learning Techniques has been discussed. This paper focuses on better Machine Learning Techniques which supports data classification are available in the literature.

  19. Classification of airborne laser scanning data using JointBoost

    Science.gov (United States)

    Guo, Bo; Huang, Xianfeng; Zhang, Fan; Sohn, Gunho

    2015-02-01

    The demands for automatic point cloud classification have dramatically increased with the wide-spread use of airborne LiDAR. Existing research has mainly concentrated on a few dominant objects such as terrain, buildings and vegetation. In addition to those key objects, this paper proposes a supervised classification method to identify other types of objects including power-lines and pylons from point clouds using a JointBoost classifier. The parameters for the learning model are estimated with various features computed based on the geometry and echo information of a LiDAR point cloud. In order to overcome the shortcomings stemming from the inclusion of bare ground data before classification, the proposed classifier directly distinguishes terrain using a feature step-off count. Feature selection is conducted using JointBoost to evaluate feature correlations thus improving both classification accuracy and operational efficiency. In this paper, the contextual constraints for objects extracted by graph-cut segmentation are used to optimize the initial classification results obtained by the JointBoost classifier. Our experimental results show that the step-off count significantly contributes to classification. Seventeen effective features are selected for the initial classification results using the JointBoost classifier. Our experiments indicate that the proposed features and method are effective for classification of airborne LiDAR data from complex scenarios.

  20. VEHICLE SIMULATION MODEL FOR DEVELOPING AN INTELLIGENT SLOPE SHIFT STRATEGY

    Institute of Scientific and Technical Information of China (English)

    Jin Hui; Ge Anlin

    2004-01-01

    With the rapid development of electronics and the growing demand for higher vehicle performance,intelligent shift technology is becoming increasingly important,and it promises to be a developing trend in vehicle automatic transmissions.A new simulation model is presented,which includes engine,powertrain,tire and vehicle dynamics models.Based on the model,simulation experiments are conducted to investigate the slope shift strategy.The data and conclusions obtained from the simulations are valuable contributions to the development of an intelligent slope shift strategy.

  1. Alternating-Current Motor Drive for Electric Vehicles

    Science.gov (United States)

    Krauthamer, S.; Rippel, W. E.

    1982-01-01

    New electric drive controls speed of a polyphase as motor by varying frequency of inverter output. Closed-loop current-sensing circuit automatically adjusts frequency of voltage-controlled oscillator that controls inverter frequency, to limit starting and accelerating surges. Efficient inverter and ac motor would give electric vehicles extra miles per battery charge.

  2. Structuring automated vehicle guidance knowledge in The Netherlands

    NARCIS (Netherlands)

    Hoedemaeker, M.; Bastiaensen, E.G.H.J.; Zwaneveld, P.J.

    2000-01-01

    This paper describes the set-up of a AVG knowledge database from the AVV Transport Research Centre from the Dutch Ministry of Transport, Public Works and Water Management. The database provides a categorised overview of all research performed in The Netherlands in the area of Automatic Vehicle Guida

  3. Automatic programming of simulation models

    Science.gov (United States)

    Schroer, Bernard J.; Tseng, Fan T.; Zhang, Shou X.; Dwan, Wen S.

    1988-01-01

    The objective of automatic programming is to improve the overall environment for describing the program. This improved environment is realized by a reduction in the amount of detail that the programmer needs to know and is exposed to. Furthermore, this improved environment is achieved by a specification language that is more natural to the user's problem domain and to the user's way of thinking and looking at the problem. The goal of this research is to apply the concepts of automatic programming (AP) to modeling discrete event simulation system. Specific emphasis is on the design and development of simulation tools to assist the modeler define or construct a model of the system and to then automatically write the corresponding simulation code in the target simulation language, GPSS/PC. A related goal is to evaluate the feasibility of various languages for constructing automatic programming simulation tools.

  4. Clothes Dryer Automatic Termination Evaluation

    Energy Technology Data Exchange (ETDEWEB)

    TeGrotenhuis, Ward E.

    2014-10-01

    Volume 2: Improved Sensor and Control Designs Many residential clothes dryers on the market today provide automatic cycles that are intended to stop when the clothes are dry, as determined by the final remaining moisture content (RMC). However, testing of automatic termination cycles has shown that many dryers are susceptible to over-drying of loads, leading to excess energy consumption. In particular, tests performed using the DOE Test Procedure in Appendix D2 of 10 CFR 430 subpart B have shown that as much as 62% of the energy used in a cycle may be from over-drying. Volume 1 of this report shows an average of 20% excess energy from over-drying when running automatic cycles with various load compositions and dryer settings. Consequently, improving automatic termination sensors and algorithms has the potential for substantial energy savings in the U.S.

  5. Classification of Sporting Activities Using Smartphone Accelerometers

    Directory of Open Access Journals (Sweden)

    Noel E. O'Connor

    2013-04-01

    Full Text Available In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT. Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach.

  6. Deep Reconstruction Models for Image Set Classification.

    Science.gov (United States)

    Hayat, Munawar; Bennamoun, Mohammed; An, Senjian

    2015-04-01

    Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods. PMID:26353289

  7. Automated classification of parenchymal patterns in mammograms

    Energy Technology Data Exchange (ETDEWEB)

    Karssemeijer, N. [University Hospital Nijmegen, Department of Radiology, PO Box 9101, 6500 HB Nijmegen (Netherlands)

    1998-02-01

    A method for automated determination of parenchymal patterns in mammograms has been developed that is insensitive to changes in the mammographic imaging technique. The method was designed to study the relation between breast cancer risk and changes of mammographic density. It includes a new method for automatic segmentation of the pectoral muscle in oblique mammograms, based on application of the Hough transform. The technique developed for classification of parenchymal patterns is based on a distance transform that subdivides the breast tissue area into regions in which distance to the skin line is approximately equal. Features are calculated from grey level histograms computed in these regions. In this way, dependency on varying tissue thickness in the peripheral zone of the breast is minimized. Additional features represent differences between tissue projected in pectoral and breast area. Robustness and classification performance were studied on a test set of 615 digitized mammograms, applying a kNN classifier and leave-one-out for training. Using four density categories in 67% of the cases an exact agreement was obtained with a subjective classification made by a radiologist. The number of cases for which classifications of the radiologist and the program differed by more that one category was only 2%. For more recent mammograms, recorded after 1991, an exact agreement of 80% was obtained. (author)

  8. 49 CFR 567.7 - Requirements for persons who alter certified vehicles.

    Science.gov (United States)

    2010-10-01

    ... Federal motor vehicle safety, Bumper, and Theft Prevention standards, and (2) Assumes legal responsibility... classification from that shown on the original certification label, the type as modified shall be provided....

  9. Differential risk of injury in child occupants by passenger car classification.

    Science.gov (United States)

    Kallan, Michael J; Durbin, Dennis R; Elliott, Michael R; Menon, Rajiv A; Winston, Flaura K

    2003-01-01

    In the United States, passenger cars are the most common passenger vehicle, yet they vary widely in size and crashworthiness. Using data collected from a population-based sample of crashes in State Farm-insured vehicles, we quantified the risk of injury to child occupants by passenger car size and classification. Injury risk is predicted by vehicle weight; however, there is an increased risk in both Large vs. Luxury and Sports vs. Small cars, despite similar average vehicle weights in both comparisons. Parents who are purchasing passenger cars should strongly consider the size of the vehicle and its crashworthiness. PMID:12941234

  10. k-Nearest Neighbors for automated classification of celestial objects

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The nearest neighbors (NNs) classifiers, especially the k-Nearest Neighbors (kNNs) algorithm, are among the simplest and yet most efficient classification rules and widely used in practice. It is a nonparametric method of pattern recognition. In this paper, k-Nearest Neighbors, one of the most commonly used machine learning methods, work in automatic classification of multi-wavelength astronomical objects. Through the experiment, we conclude that the running speed of the kNN classier is rather fast and the classification accuracy is up to 97.73%. As a result, it is efficient and applicable to discriminate active objects from stars and normal galaxies with this method. The classifiers trained by the kNN method can be used to solve the automated classification problem faced by astronomy and the virtual observatory (VO).

  11. Land Cover Classification Using ALOS Imagery For Penang, Malaysia

    International Nuclear Information System (INIS)

    This paper presents the potential of integrating optical and radar remote sensing data to improve automatic land cover mapping. The analysis involved standard image processing, and consists of spectral signature extraction and application of a statistical decision rule to identify land cover categories. A maximum likelihood classifier is utilized to determine different land cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The land cover information was extracted from the digital data using PCI Geomatica 10.3.2 software package. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. The classification accuracies from the optical and radar feature combinations are studied. Our research finds that fusion of radar and optical significantly improved classification accuracies. This study indicates that the land cover/use can be mapped accurately by using this approach

  12. Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs.

    Science.gov (United States)

    Chen, Haijian; Han, Dongmei; Dai, Yonghui; Zhao, Lina

    2015-01-01

    In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of "C programming language" are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate. PMID:26448738

  13. Automatic sleep staging using state machine-controlled decision trees.

    Science.gov (United States)

    Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

    2015-01-01

    Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.

  14. Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs

    Directory of Open Access Journals (Sweden)

    Haijian Chen

    2015-01-01

    Full Text Available In recent years, Massive Open Online Courses (MOOCs are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.

  15. Image Processing Method for Automatic Discrimination of Hoverfly Species

    Directory of Open Access Journals (Sweden)

    Vladimir Crnojević

    2014-01-01

    Full Text Available An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients and the robust version of recently proposed CLBP (complete local binary pattern. These features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract statistics characterizing the constellations of these points. Such simple features can be used to automatically discriminate four selected hoverfly species with polynomial kernel SVM and achieve high classification accuracy.

  16. Automatic local Gabor Features extraction for face recognition

    CERN Document Server

    Jemaa, Yousra Ben

    2009-01-01

    We present in this paper a biometric system of face detection and recognition in color images. The face detection technique is based on skin color information and fuzzy classification. A new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their correspondent geometrical points. These fiducial points are described by sets of wavelet components which are used for recognition. To achieve the face recognition, we use neural networks and we study its performances for different inputs. We compare the two types of features used for recognition: geometric distances and Gabor coefficients which can be used either independently or jointly. This comparison shows that Gabor coefficients are more powerful than geometric distances. We show with experimental results how the importance recognition ratio makes our system an effective tool for automatic face detection and recognition.

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

    Directory of Open Access Journals (Sweden)

    Hongying Meng

    2014-11-01

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

  18. Automatic target recognition in synthetic aperture sonar images for autonomous mine hunting

    NARCIS (Netherlands)

    Quesson, B.A.J.; Sabel, J.C.; Bouma, H.; Dekker, R.J.; Lengrand-Lambert, J.

    2010-01-01

    The future of Mine Countermeasures (MCM) operations lies with unmanned platforms where Automatic Target Recognition (ATR) is an essential step in making the mine hunting process autonomous. At TNO, a new ATR method is currently being developed for use on an Autonomous Underwater Vehicle (AUV), using

  19. Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving

    CERN Document Server

    Kerner, Boris S

    2016-01-01

    In a mini-review [Physica A {\\bf 392} (2013) 5261--5282] it has been shown that classical traffic flow theories and models failed to explain empirical traffic breakdown -- a phase transition from metastable free flow to synchronized flow at highway bottlenecks. The main objective of this mini-review is to study the consequence of this failure of classical traffic-flow theories for an analysis of empirical stochastic highway capacity as well as for the effect of automatic driving vehicles and cooperative driving on traffic flow. To reach this goal, we show a deep connection between the understanding of empirical stochastic highway capacity and a reliable analysis of automatic driving vehicles in traffic flow. With the use of simulations in the framework of three-phase traffic theory, a probabilistic analysis of the effect of automatic driving vehicles on a mixture traffic flow consisting of a random distribution of automatic driving and manual driving vehicles has been made. We have found that the parameters o...

  20. Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud

    OpenAIRE

    Liang Cheng; Yang Wu; Lihua Tong; Yanming Chen; Manchun Li

    2015-01-01

    A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Th...