WorldWideScience

Sample records for automatic vehicle classification

  1. Video Analytics Algorithm for Automatic Vehicle Classification (Intelligent Transport System)

    OpenAIRE

    ArtaIftikhar; Ali Javed

    2013-01-01

    Automated Vehicle detection and classification is an important component of intelligent transport system. Due to significant importance in various fields such as traffic accidents avoidance, toll collection, congestion avoidance, terrorist activities monitoring, security and surveillance systems, intelligent transport system has become important field of study. Various technologies have been used for detecting and classifying vehicles automatically. Automated vehicle detection is broadly divi...

  2. Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems using Neural Networks

    OpenAIRE

    Ozkurt, Celil; Camci, Fatih

    2009-01-01

    It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphesized. However, further a...

  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. Automatic Arabic Text Classification

    OpenAIRE

    Al-harbi, S; Almuhareb, A.; Al-Thubaity , A; Khorsheed, M. S.; Al-Rajeh, A.

    2008-01-01

    Automated document classification is an important text mining task especially with the rapid growth of the number of online documents present in Arabic language. Text classification aims to automatically assign the text to a predefined category based on linguistic features. Such a process has different useful applications including, but not restricted to, e-mail spam detection, web page content filtering, and automatic message routing. This paper presents the results of experiments on documen...

  5. Vision-based industrial automatic vehicle classifier

    Science.gov (United States)

    Khanipov, Timur; Koptelov, Ivan; Grigoryev, Anton; Kuznetsova, Elena; Nikolaev, Dmitry

    2015-02-01

    The paper describes the automatic motor vehicle video stream based classification system. The system determines vehicle type at payment collection plazas on toll roads. Classification is performed in accordance with a preconfigured set of rules which determine type by number of wheel axles, vehicle length, height over the first axle and full height. These characteristics are calculated using various computer vision algorithms: contour detectors, correlational analysis, fast Hough transform, Viola-Jones detectors, connected components analysis, elliptic shapes detectors and others. Input data contains video streams and induction loop signals. Output signals are vehicle enter and exit events, vehicle type, motion direction, speed and the above mentioned features.

  6. Automatic indexing, compiling and classification

    International Nuclear Information System (INIS)

    A review of the principles of automatic indexing, is followed by a comparison and summing-up of work by the authors and by a Soviet staff from the Moscou INFORM-ELECTRO Institute. The mathematical and linguistic problems of the automatic building of thesaurus and automatic classification are examined

  7. 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...

  8. Real time automatic scene classification

    OpenAIRE

    Israël, Menno; Broek, van den, Wouter; Putten, van, M.J.A.M.; Uyl, den, T.M.; 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 elements of a scene automatically with so-called ’stuff’ categories (e.g., grass, sky, sand, stone). Campbell et al. [1] use similar concepts to describe certain parts of an image, which they named...

  9. 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 ...

  10. 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.

  11. 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.

  12. 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)

  13. 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

  14. 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...

  15. 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.

  16. 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...

  17. Automatic classification of defects in weld pipe

    International Nuclear Information System (INIS)

    With the advancement of computer imaging technology, the image on hard radiographic film can be digitized and stored in a computer and the manual process of defect recognition and classification may be replace by the computer. In this paper a computerized method for automatic detection and classification of common defects in film radiography of weld pipe is described. The detection and classification processes consist of automatic selection of interest area on the image and then classify common defects using image processing and special algorithms. Analysis of the attributes of each defect such as area, size, shape and orientation are carried out by the feature analysis process. These attributes reveal the type of each defect. These methods of defect classification result in high success rate. Our experience showed that sharp film images produced better results

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. 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

  3. Automatic analysis and classification of surface electromyography.

    Science.gov (United States)

    Abou-Chadi, F E; Nashar, A; Saad, M

    2001-01-01

    In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device. PMID:11556501

  4. Classification of Dynamic Vehicle Routing Systems

    DEFF Research Database (Denmark)

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

    2007-01-01

    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...... systems based on their degree of dynamism and the system objective....

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. Automatic Genre Classification of Latin Music Using Ensemble of Classifiers

    OpenAIRE

    Silla Jr, Carlos N.; Kaestner, Celso A.A.; Koerich, Alessandro L.

    2006-01-01

    This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with the aim of improving music genre classification accuracy. Experiments carried out on a dataset conta...

  10. Automatic Classification of Seafloor Image Data by Geospatial Texture Descriptors

    OpenAIRE

    Lüdtke, Andree

    2014-01-01

    A novel approach for automatic context-sensitive classification of spatially distributed image data is introduced. The proposed method targets applications of seafloor habitat mapping but is generally not limited to this domain or use case. Spatial context information is incorporated in a two-stage classification process, where in the second step a new descriptor for patterns of feature class occurrence according to a generically defined classification scheme is applied. The method is based o...

  11. 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.

  12. 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...

  13. Automatic genre classification of English students' argumentative essays using support vector machines / by Sabrina Raaff

    OpenAIRE

    Raaff, Sabrina

    2007-01-01

    Automatic text classification refers to the classification of texts according to topic. Similar to text classification is the automatic classification of texts based on stylistic aspect of texts, such as automatic genre classification, where texts are classified according to their genre. This is the classification task that concerns this research project.* The project seeks to examine the genre of the argumentative essay, in order to develop a genre classifier, using an automatic genre cla...

  14. 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.

  15. 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

  16. Feature extraction and classification in automatic weld seam radioscopy

    International Nuclear Information System (INIS)

    The investigations conducted have shown that automatic feature extraction and classification procedures permit the identification of weld seam flaws. Within this context the favored learning fuzzy classificator represents a very good alternative to conventional classificators. The results have also made clear that improvements mainly in the field of image registration are still possible by increasing the resolution of the radioscopy system. Since, only if the flaw is segmented correctly, i.e. in its full size, and due to improved detail recognizability and sufficient contrast difference will an almost error-free classification be conceivable. (orig./MM)

  17. Automatic document classification of biological literature

    OpenAIRE

    Sternberg Paul W; Müller Hans-Michael; Chen David

    2006-01-01

    Abstract Background Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis eleg...

  18. 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.

  19. Automatic classification of time-variable X-ray sources

    CERN Document Server

    Lo, Kitty K; Murphy, Tara; Gaensler, B M

    2014-01-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the second \\textit{XMM-Newton} serendipitous source catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10-fold cross validation accuracy of the training data is ${\\sim}$97% on a seven-class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest der...

  20. Automatic document classification of biological literature

    Directory of Open Access Journals (Sweden)

    Sternberg Paul W

    2006-08-01

    Full Text Available Abstract Background Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578 compared to previously published results (F-value of 0.55 vs. 0.49, while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusion We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept.

  1. 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

  2. Towards Automatic Topical Classification of LOD Datasets.

    OpenAIRE

    Meusel, R, Spahiu, B, Bizer, C, Paulheim, H

    2015-01-01

    The datasets that are part of the Linking Open Data cloud diagram (LOD cloud) are classified into the following topical categories: media, government, publications, life sciences, geographic, social networking, user-generated content, and cross-domain. The topical categories were manually assigned to the datasets. In this paper, we investigate to which extent the topical classification of new LOD datasets can be automated using machine learning techniques and the existing annotations as super...

  3. 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.

  4. Automatic classification of protein structure by using Gauss integrals

    DEFF Research Database (Denmark)

    Røgen, Peter; Fain, B.

    2003-01-01

    dimensions, show the relative locations of the major structural classes, and "zoom into" the space of proteins to show architecture, topology, and fold clusters. The existence of a simple measure of a protein fold computed from the chain path will have a major impact on automatic fold classification....

  5. 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.

  6. 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

  7. 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.

  8. Intelligent automatic overtaking system using vision for vehicle detection

    OpenAIRE

    Milanés Montero, Vicente; Fernández Llorca, David; Villagra Serrano, Jorge; Pérez, Joshué; Fernández López, Carlos; Parra Alonso, Ignacio; González Fernández-Vallejo, Carlos; Sotelo, Miguel Ángel

    2012-01-01

    There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomo...

  9. 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.

  10. Automatic classification of time-variable X-ray sources

    International Nuclear Information System (INIS)

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

  11. 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...

  12. 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) of...... dimensions, show the relative locations of the major structural classes, and "zoom into" the space of proteins to show architecture, topology, and fold clusters. The existence of a simple measure of a protein fold computed from the chain path will have a major impact on automatic fold classification....... similarity of protein shapes. Under this metric, protein chains naturally separate into fold clusters. We use SGM to construct an automatic classification procedure for the CATH2.4 database. The method is very fast because it requires neither alignment of the chains nor any chain-chain comparison. It also...

  13. 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...

  14. Automatic classification of seismic events within a regional seismograph network

    Science.gov (United States)

    Tiira, Timo; Kortström, Jari; Uski, Marja

    2015-04-01

    A fully automatic method for seismic event classification within a sparse regional seismograph network is presented. The tool is based on a supervised pattern recognition technique, Support Vector Machine (SVM), trained here to distinguish weak local earthquakes from a bulk of human-made or spurious seismic events. The classification rules rely on differences in signal energy distribution between natural and artificial seismic sources. Seismic records are divided into four windows, P, P coda, S, and S coda. For each signal window STA is computed in 20 narrow frequency bands between 1 and 41 Hz. The 80 discrimination parameters are used as a training data for the SVM. The SVM models are calculated for 19 on-line seismic stations in Finland. The event data are compiled mainly from fully automatic event solutions that are manually classified after automatic location process. The station-specific SVM training events include 11-302 positive (earthquake) and 227-1048 negative (non-earthquake) examples. The best voting rules for combining results from different stations are determined during an independent testing period. Finally, the network processing rules are applied to an independent evaluation period comprising 4681 fully automatic event determinations, of which 98 % have been manually identified as explosions or noise and 2 % as earthquakes. The SVM method correctly identifies 94 % of the non-earthquakes and all the earthquakes. The results imply that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of confidence. The tool helps to reduce work-load in manual seismic analysis by leaving only ~5 % of the automatic event determinations, i.e. the probable earthquakes for more detailed seismological analysis. The approach presented is easy to adjust to requirements of a denser or wider high-frequency network, once enough training examples for building a station-specific data set are available.

  15. 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.

  16. 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

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

    OpenAIRE

    Yan Zhang; Wenxing Ma; Xuesong Li

    2013-01-01

    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 ...

  18. 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...

  19. 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)

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    International Nuclear Information System (INIS)

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ''classical'' automatic data classification methods fail. ((orig.))

  12. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    International Nuclear Information System (INIS)

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ''classical'' automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append

  13. 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

  14. A Hessian-based methodology for automatic surface crack detection and classification from pavement images

    Science.gov (United States)

    Ghanta, Sindhu; Shahini Shamsabadi, Salar; Dy, Jennifer; Wang, Ming; Birken, Ralf

    2015-04-01

    Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.

  15. 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

  16. 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.

  17. 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.

  18. 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 ...

  19. 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.

  20. 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.

  1. 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

  2. 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 ...

  3. Automatic classification of the 13CrMo4-5 steel worked in creep conditions

    OpenAIRE

    J. Dobrzański; M. Sroka

    2008-01-01

    Purpose: In material engineering the images obtained by various methods are the source of different information about materials. The artificial intelligence tools can be employed for automatic method for analysis of scanning electron microscope metallographic images of elements after long time operating in creep services.Design/methodology/approach: The methodology allows to work out a system of automatic classification of internal damages in 13CrMo4-5 steel working in creep conditions on the...

  4. 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.

  5. Automatic classification of gammas-gamma coincidence matrices

    International Nuclear Information System (INIS)

    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

  6. Automatic classification of gamma-gamma coincidence matrices

    International Nuclear Information System (INIS)

    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 Lecom8. 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)

  7. 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.

  8. Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors

    OpenAIRE

    K. COUSSEMENT; Van den Poel, D

    2007-01-01

    Customer complaint management is becoming a critical key success factor in today’s business environment. This study introduces a methodology to improve complaint handling strategies through an automatic email classification system that distinguishes complaints from non-complaints. As such, complaint handling becomes less time-consuming and more successful. The classification system combines traditional text information with new information about the linguistic style of an email. The empirical...

  9. Automatic workflow for the classification of local DNA conformations

    Czech Academy of Sciences Publication Activity Database

    Čech, P.; Kukal, J.; Černý, Jiří; Schneider, Bohdan; Svozil, D.

    2013-01-01

    Roč. 14, č. 205 (2013). ISSN 1471-2105 R&D Projects: GA ČR GAP305/12/1801 Institutional research plan: CEZ:AV0Z50520701 Keywords : DNA * Dinucleotide conformation * Classification * Machine learning * Neural network * k-NN * Cluster analysis Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.672, year: 2013

  10. Automatic parquet block sorting using real-time spectral classification

    Science.gov (United States)

    Astrom, Anders; Astrand, Erik; Johansson, Magnus

    1999-03-01

    This paper presents a real-time spectral classification system based on the PGP spectrograph and a smart image sensor. The PGP is a spectrograph which extracts the spectral information from a scene and projects the information on an image sensor, which is a method often referred to as Imaging Spectroscopy. The classification is based on linear models and categorizes a number of pixels along a line. Previous systems adopting this method have used standard sensors, which often resulted in poor performance. The new system, however, is based on a patented near-sensor classification method, which exploits analogue features on the smart image sensor. The method reduces the enormous amount of data to be processed at an early stage, thus making true real-time spectral classification possible. The system has been evaluated on hardwood parquet boards showing very good results. The color defects considered in the experiments were blue stain, white sapwood, yellow decay and red decay. In addition to these four defect classes, a reference class was used to indicate correct surface color. The system calculates a statistical measure for each parquet block, giving the pixel defect percentage. The patented method makes it possible to run at very high speeds with a high spectral discrimination ability. Using a powerful illuminator, the system can run with a line frequency exceeding 2000 line/s. This opens up the possibility to maintain high production speed and still measure with good resolution.

  11. 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...

  12. 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...

  13. 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.

  14. 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

  15. The systems of automatic weight control of vehicles in the road and rail transport in Poland

    Directory of Open Access Journals (Sweden)

    2011-09-01

    Full Text Available . Condition of roads in Poland, despite the on-going modernisation works is still unsatisfactory. One reason is the excessive wear caused by overloaded vehicles. This problem also applies to rail transport, although to a much lesser extent. One solution may be the system of automatic weight control of road and rail vehicles. The article describes the legal and organizational conditions of oversize vehicles inspection in Poland. Characterized current practices weighing road vehicles, based on measurements of static technology. The article includes the description of the existing applications of the automatic dynamic weighing technology, known as systems WIM (Weigh in Motion. Additionally, the weighing technology and construction of weighing stands in road and rail are characterized. The article ends with authors' conclusions indicating the direction and ways of improving the weighing control systems for vehicles.

  16. 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...

  17. Automatic lane keeping of a vehicle based on perception net

    Science.gov (United States)

    Boo, Kwangsuck; Jung, Moonyoung

    2000-10-01

    The objective of this research is to monitor and control the vehicle motion in order to remove out the existing safety risk based upon the human-machine cooperative vehicle control. A predictive control method is proposed to control the steering wheel of the vehicle to keep the lane. Desired angle of the steering wheel to control the vehicle motion could be calculated based upon vehicle dynamics, current and estimated pose of the vehicle every sample steps. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder through the Perception Net, where not only the state variables, but also the corresponding uncertainties were propagated in forward and backward direction in such a way to satisfy the given constraint condition, maintain consistency, reduce the uncertainties, and guarantee robustness. A series of experiments was conducted to evaluate the control performance, in which a car like robot was utilized to quit unwanted safety problem. As the results, the robot was keeping very well a given lane with arbitrary shape at moderate speed.

  18. 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.

  19. 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

  20. 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.

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Automatic classification of DMSA scans using an artificial neural network

    International Nuclear Information System (INIS)

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice. (paper)

  7. 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.

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

    DEFF Research Database (Denmark)

    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...... and amplitude) using reflectivity observations from a single weather radar system. A categorical sequence of most likely wind regimes is estimated from a wind speed time series by combining a Markov-Switching model and a global decoding technique, the Viterbi algorithm. In parallel, attributes of precipitation...... 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...

  9. 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 ...

  10. 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.

  11. 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.

  12. Automatic classification of the 13CrMo4-5 steel worked in creep conditions

    Directory of Open Access Journals (Sweden)

    J. Dobrzański

    2008-08-01

    Full Text Available Purpose: In material engineering the images obtained by various methods are the source of different information about materials. The artificial intelligence tools can be employed for automatic method for analysis of scanning electron microscope metallographic images of elements after long time operating in creep services.Design/methodology/approach: The methodology allows to work out a system of automatic classification of internal damages in 13CrMo4-5 steel working in creep conditions on the base of computational images analysis by the use of artificial neural networks. Input vectors of artificial neural networks were optimized by the use of genetic algorithms.Findings: The methodology of digital image analysis allowing identification of geometrical coefficients characterizing damages in the materials after long-time operating in creep conditions and methodology of classification of these damages by the use of artificial neural network were evaluated.Practical implications: The presented method can be use as a practical application for classification of creep-damages of elements power industry installations components operating in creep conditions.Originality/value: Applying of images analysis and neural networks to identification and classification of internal damages of 13CrMo4-5 steel working in creep conditions could shorten the time of classification and eliminate of many subjective errors made by humans.

  13. Automatic classification and accurate size measurement of blank mask defects

    Science.gov (United States)

    Bhamidipati, Samir; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2015-07-01

    complexity of defects encountered. The variety arises due to factors such as defect nature, size, shape and composition; and the optical phenomena occurring around the defect. This paper focuses on preliminary characterization results, in terms of classification and size estimation, obtained by Calibre MDPAutoClassify tool on a variety of mask blank defects. It primarily highlights the challenges faced in achieving the results with reference to the variety of defects observed on blank mask substrates and the underlying complexities which make accurate defect size measurement an important and challenging task.

  14. 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.

  15. 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.

  16. 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 ...

  17. Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

    OpenAIRE

    Chaudhry, Asmatullah; Hassan, Mehdi; Khan, Asifullah; Kim, Jin Young

    2013-01-01

    In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view th...

  18. Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

    OpenAIRE

    Wiliem, Arnold; Sanderson, Conrad; Wong, Yongkang; Hobson, Peter; Minchin, Rodney F.; Lovell, Brian C.

    2014-01-01

    This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and...

  19. 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...

  20. 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.

  1. 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.

  2. Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

    Directory of Open Access Journals (Sweden)

    Jose Luis Rodríguez-Sotelo

    2014-12-01

    Full Text Available Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

  3. Automatic Ferrite Content Measurement based on Image Analysis and Pattern Classification

    Directory of Open Access Journals (Sweden)

    Hafiz Muhammad Tanveer

    2015-05-01

    Full Text Available The existing manual point counting technique for ferrite content measurement is a difficult time consuming method which has limited accuracy due to limited human perception and error induced by points on boundaries of grid spacing. In this paper, we present a novel algorithm, based on image analysis and pattern classification, to evaluate the volume fraction of ferrite in microstructure containing ferrite and austenite. The prime focus of the proposed algorithm is to solve the problem of ferrite content measurement using automatic binary classification approach. Classification of image data into two distinct classes, using optimum threshold finding method, is the key idea behind the new algorithm. Automation of the process to measure the ferrite content and to speed up specimen’s testing procedure is the main feature of the newly developed algorithm. Improved performance index by reducing error sources is reflected from obtained results and validated through the comparison with a well-known method of Ohtsu.

  4. 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

  5. An embedded omnidirectional vision navigator for automatic guided vehicles

    Science.gov (United States)

    Feng, Weijia; Zhang, Baofeng; Röning, Juha; Cao, Zuoliang; Zong, Xiaoning

    2011-01-01

    Omnidirectional vision appears the definite significance since its advantage of acquiring full 360° horizontal field of vision information simultaneously. In this paper, an embedded original omnidirectional vision navigator (EOVN) based on fish-eye lens and embedded technology has been researched. Fish-eye lens is one of the special ways to establish omnidirectional vision. However, it appears with an unavoidable inherent and enormous distortion. A unique integrated navigation method which is conducted on the basis of targets tracking has been proposed. It is composed of multi-target recognition and tracking, distortion rectification, spatial location and navigation control. It is called RTRLN. In order to adapt to the different indoor and outdoor navigation environments, we implant mean-shift and dynamic threshold adjustment into the Particle Filter algorithm to improve the efficiency and robustness of tracking capability. RTRLN has been implanted in an independent development embedded platform. EOVN likes a smart crammer based on COMS+FPGA+DSP. It can guide various vehicles in outdoor environments by tracking the diverse marks hanging in the air. The experiments prove that the EOVN is particularly suitable for the guidance applications which need high requirements on precision and repeatability. The research achievements have a good actual applied inspection.

  6. Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

    Science.gov (United States)

    Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

    2015-08-01

    Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, Ada

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. Adaptive Gearshift Strategy Based on Generalized Load Recognition for Automatic Transmission Vehicles

    OpenAIRE

    Yulong Lei; Ke Liu; Yuanxia Zhang; Yao Fu; Hongbo Liu; Ge Lin; Hui Tang

    2015-01-01

    Recognizing various driving conditions in real time and adjusting control strategy accordingly in automatic transmission vehicles are important to improve their adaptability to the external environment. This study defines a generalized load concept which can comprehensively reflect driving condition information. The principle of a gearshift strategy based on generalized load is deduced theoretically, adopting linear interpolation between the shift lines on flat and on the largest gradient roa...

  12. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    OpenAIRE

    Ming-Shyan Wang; Seng-Chi Chen; Po-Hsiang Chuang; Shih-Yu Wu; Fu-Shung Hsu

    2015-01-01

    An automatic guided vehicle (AGV) is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID) control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN) control is consider...

  13. Automatic Bayesian classification of healthy controls, bipolar disorder and schizophrenia using intrinsic connectivity maps from fMRI data

    OpenAIRE

    Arribas, Juan I.; Calhoun, Vince D.; Adalı, Tülay

    2010-01-01

    We present a method for supervised, automatic and reliable classification of healthy controls, patients with bipolar disorder and patients with schizophrenia using brain imaging data. The method uses four supervised classification learning machines trained with a stochastic gradient learning rule based on the minimization of Kullback-Leibler divergence and an optimal model complexity search through posterior probability estimation. Prior to classification, given the high dimensionality of fun...

  14. Automatic quality classification of entire electrocardiographic recordings obtained with a novel patch type recorder

    DEFF Research Database (Denmark)

    Saadi, Dorthe Bodholt; Hoppe, Karsten; Egstrup, Kenneth;

    2014-01-01

    algorithm for automatic classification of entire ECG recordings into the groups “noisy” and “clean” recordings. This novel algorithm is based on three features and a simple Bayes classifier. The algorithm was tested on 40 ECG recordings in a five-fold cross validation scheme and it obtained an average......Recently, new patch type electrocardiogram (ECG) recorders have reached the market. These new devices possess a number of advantages compared to the traditional Holter recorders. This forms the basis of questions related to benefits and drawbacks of different ambulatory ECG recording techniques....... One of the important questions is the ability to obtain high clinical quality of the recordings during the entire monitoring period. It is thus desirable to be able to obtain an automatic estimate of the global quality of entire ECG recordings. The purpose of this pilot study is therefore to design an...

  15. 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

  16. 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. .

  17. Automatic disruption classification based on manifold learning for real-time applications on JET

    International Nuclear Information System (INIS)

    Disruptions remain the biggest threat to the safe operation of tokamaks. To efficiently mitigate the negative effects, it is now considered important not only to predict their occurrence but also to be able to determine, with high probability, the type of disruption about to occur. This paper reports the results obtained using the nonlinear generative topographic map manifold learning technique for the automatic classification of disruption types. It has been tested using an extensive database of JET discharges selected from JET campaigns from C15 (year 2005) up to C27 (year 2009). The success rate of the classification is extremely high, sometimes reaching 100%, and therefore the prospects for the deployment of this tool in real time are very promising. (paper)

  18. A Novel Approach for Automatic Web Page Classification using Feature Intervals

    Directory of Open Access Journals (Sweden)

    V. Santhosh Kumar

    2012-09-01

    Full Text Available A new web page classification algorithm using weighted voting of feature intervals known as WVFI is proposed in this paper. This classifier first discretizes the web page features using a supervised disctretization algorithm which identifies the number of intervals each feature has to be discretized automatically. Each feature is then made to predict the class of the corresponding feature in the test web page using the class distribution of its intervals. The final class of the test web page is predicted by aggregating the weighted vote of each feature. Experiments done on a benchmarking data set called WebKB has shown good classification accuracy when compared with many of the existing classifiers.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. Automatic active contour-based segmentation and classification of carotid artery ultrasound images.

    Science.gov (United States)

    Chaudhry, Asmatullah; Hassan, Mehdi; Khan, Asifullah; Kim, Jin Young

    2013-12-01

    In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view these facts, image alignment is used as a preprocessing step to align the carotid artery ultrasound images. In our experimental study, we exploit intima-media thickness (IMT) measurement to detect the presence of plaque in the artery. Support vector machine (SVM) classification is employed using these segmented images to distinguish the normal and diseased artery images. IMT measurement is used to form the feature vector. Our proposed approach segments the carotid artery images in an automatic way and further classifies them using SVM. Experimental results show the learning capability of SVM classifier and validate the usefulness of our proposed approach. Further, the proposed approach needs minimum interaction from a user for an early detection of plaque in carotid artery. Regarding the usefulness of the proposed approach in healthcare, it can be effectively used in remote areas as a preliminary clinical step even in the absence of highly skilled radiologists. PMID:23417308

  4. 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.

  5. 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

  6. AUTOMATIC UNSUPERVISED CLASSIFICATION OF ALL SLOAN DIGITAL SKY SURVEY DATA RELEASE 7 GALAXY SPECTRA

    International Nuclear Information System (INIS)

    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 rest-frame 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 Spectroscopic K-means-based (ASK) classification separates galaxies in colors, with classes characteristic of the red sequence, the blue cloud, as well as the green valley. When red sequence galaxies and green valley galaxies present emission lines, they are characteristic of active galactic nucleus activity. Blue galaxy classes have emission lines corresponding to star formation regions. We find the expected correlation between spectroscopic class and Hubble type, but this relationship exhibits a high intrinsic scatter. Several potential uses of the ASK classification are identified and sketched, including fast determination of physical properties by interpolation, classes as templates in redshift determinations, and target selection in follow-up works (we find classes of Seyfert galaxies, green valley galaxies, as well as a significant number of outliers). The ASK classification is publicly accessible through various Web sites.

  7. 40 CFR 86.085-20 - Incomplete vehicles, classification.

    Science.gov (United States)

    2010-07-01

    ... and Heavy-Duty Engines, and for 1985 and Later Model Year New Gasoline Fueled, Natural Gas-Fueled, Liquefied Petroleum Gas-Fueled and Methanol-Fueled Heavy-Duty Vehicles § 86.085-20 Incomplete...

  8. 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...

  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. 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%.

  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. PMID:26847825

  12. Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

    Directory of Open Access Journals (Sweden)

    Rumen Andonov

    2015-10-01

    Full Text Available In this work, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN scheme classifies up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.

  13. 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.

  14. 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

  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. Automatic earthquake detection and classification with continuous hidden Markov models: a possible tool for monitoring Las Canadas caldera in Tenerife

    International Nuclear Information System (INIS)

    A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.

  17. 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.

  18. 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

  19. 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%.

  20. 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.

  1. 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...

  2. 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.

  3. 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.

  4. Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

    Science.gov (United States)

    Su, Jie; Xu, Xuan; He, Yongjun; Song, Jinming

    2016-01-01

    We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression) classifier was used individually; while the recognition rate was significantly higher (95.642%) when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44%) of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review. PMID:27298758

  5. 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

  6. 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.

  7. 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...

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Automatic Road Lighting System (ARLS) Model Based on Image Processing of Captured Video of Vehicle Toy Motion

    CERN Document Server

    Suprijadi,; Viridi, Sparisoma

    2011-01-01

    Using a vehicle toy as a moving object an automatic road lighting system (ARLS) model is constructed. A video camera with 25 fps is used to capture the vehicle toy motion as it moves in the test segment of the road. Captured images are then processed to calculate vehicle toy speed. This information of the speed together with position of vehicle toy is then used to switch on and off the lighting system along the path that passes by the vehicle toy. Length of the road test segment is 1 m, the video camera is positioned about 1.075 m above the test segment, and the vehicle toy dimension is 13 cm x 9.3 cm. Maximum speed that ARLS can handle is about 1.32 m/s with error less than 23.48 %. The highest performance is obtained about 91 % at speed 0.93 m/s.

  14. 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.

  15. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2015-01-01

    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  16. 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

  17. 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).

  18. 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.

  19. Development of a rapid method for the automatic classification of biological agents' fluorescence spectral signatures

    Science.gov (United States)

    Carestia, Mariachiara; Pizzoferrato, Roberto; Gelfusa, Michela; Cenciarelli, Orlando; Ludovici, Gian Marco; Gabriele, Jessica; Malizia, Andrea; Murari, Andrea; Vega, Jesus; Gaudio, Pasquale

    2015-11-01

    Biosecurity and biosafety are key concerns of modern society. Although nanomaterials are improving the capacities of point detectors, standoff detection still appears to be an open issue. Laser-induced fluorescence of biological agents (BAs) has proved to be one of the most promising optical techniques to achieve early standoff detection, but its strengths and weaknesses are still to be fully investigated. In particular, different BAs tend to have similar fluorescence spectra due to the ubiquity of biological endogenous fluorophores producing a signal in the UV range, making data analysis extremely challenging. The Universal Multi Event Locator (UMEL), a general method based on support vector regression, is commonly used to identify characteristic structures in arrays of data. In the first part of this work, we investigate fluorescence emission spectra of different simulants of BAs and apply UMEL for their automatic classification. In the second part of this work, we elaborate a strategy for the application of UMEL to the discrimination of different BAs' simulants spectra. Through this strategy, it has been possible to discriminate between these BAs' simulants despite the high similarity of their fluorescence spectra. These preliminary results support the use of SVR methods to classify BAs' spectral signatures.

  20. 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.

  1. 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

  2. A Grid Service for Automatic Land Cover Classification Using Hyperspectral Images

    Science.gov (United States)

    Jasso, H.; Shin, P.; Fountain, T.; Pennington, D.; Ding, L.; Cotofana, N.

    2004-12-01

    Hyperspectral images are collected using Airborne Visible/Infrared Imaging Spectrometer (Aviris) optical sensors [1]. 224 contiguous channels are measured across the spectral range, from 400 to 2500 nanometers. We present a system for the automatic classification of land cover using hyperspectral images, and propose an architecture for deploying the system in a grid environment that harnesses distributed file storage and CPU resources for the task. Originally, we ran the following data mining algorithms on a 300x300 image of a section of the Sevilleta National Wildlife Refuge in New Mexico [2]: Maximum Likelihood, Naive Bayes Classifier, Minimum Distance, and Support Vector Machine (SVM). For this, ground truth for 673 pixels was manually collected according to eight possible land covers: river, riparian, agriculture, arid upland, semi-arid upland, barren, pavement, or clouds. The classification accuracies for these algorithms were of 96.4%, 90.9%, 88.4%, and 77.6%, respectively [3]. In this study, we noticed that the slope between adjacent frequencies produces specific patterns across the whole spectrum, giving a good indication of the pixel's land cover type. Wavelet analysis makes these global patterns explicit, by breaking down the signal into variable-sized windows, where long time windows capture low-frequency information and short time windows capture high-frequency information. High frequency information translates to information among close neighbors while low frequency information displays the overall trend of the features. We pre-processed the data using different families of wavelets, resulting in an increase in the performance of the Naive Bayesian Classifier and SVM to 94.2% and 90.1%, respectively. Classification accuracy with SVM was further increased to 97.1 % by modifying the mechanism by which multi-class is achieved using basic two-class SVMs. The original winner-take-all SVM scheme was replaced with a one-against-one scheme, in which k(k-1

  3. 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

  4. 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

  5. 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.

    2016-04-01

    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.

  6. 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.

  7. 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

  8. Photoelectric scanning-based method for positioning omnidirectional automatic guided vehicle

    Science.gov (United States)

    Huang, Zhe; Yang, Linghui; Zhang, Yunzhi; Guo, Yin; Ren, Yongjie; Lin, Jiarui; Zhu, Jigui

    2016-03-01

    Automatic guided vehicle (AGV) as a kind of mobile robot has been widely used in many applications. For better adapting to the complex working environment, more and more AGVs are designed to be omnidirectional by being equipped with Mecanum wheels for increasing their flexibility and maneuverability. However, as the AGV with this kind of wheels suffers from the position errors mainly because of the frequent slipping property, how to measure its position accurately in real time is an extremely important issue. Among the ways of achieving it, the photoelectric scanning methodology based on angle measurement is efficient. Hence, we propose a feasible method to ameliorate the positioning process, which mainly integrates four photoelectric receivers and one laser transmitter. To verify the practicality and accuracy, actual experiments and computer simulations have been conducted. In the simulation, the theoretical positioning error is less than 0.28 mm in a 10 m×10 m space. In the actual experiment, the performances about the stability, accuracy, and dynamic capability of this method were inspected. It demonstrates that the system works well and the performance of the position measurement is high enough to fulfill the mainstream tasks.

  9. 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.

  10. Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification

    Directory of Open Access Journals (Sweden)

    Luliang Tang

    2015-11-01

    Full Text Available In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT, which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane.

  11. INVESTMENT OF THE DEVELOPMENT OF ROAD-BUILD MEANS, AUTOMATIC AND INFORMATIONAL SYSTEMS TO INCREASE TRAFFIC SAFETY IN VEHICLE SYSTEMS

    Directory of Open Access Journals (Sweden)

    Shirokov Lev Alekseevich

    2015-09-01

    Full Text Available The modern transport system is a complex integrated object, which includes various road pavements, different technical means to provide vehicles motion, organizational systems of traffic management. In the contemporary conditions of construction industry functioning the task to create vehicle systems is of a great economic importance. Great labour and material resources are used for production of transport means for providing construction works and operation of these means. The authors consider the questions of theoretical and informational foundation development for the formation of the criteria basis of investment optimization task during construction of automatical and informational systems for increase of traffic safety in transport systems, providing zero accident rate.

  12. 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

  13. 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%

  14. An automatic indexing and neural network approach to concept retrieval and classification of multilingual (Chinese-English) documents.

    Science.gov (United States)

    Lin, C H; Chen, H

    1996-01-01

    An automatic indexing and concept classification approach to a multilingual (Chinese and English) bibliographic database is presented. We introduced a multi-linear term-phrasing technique to extract concept descriptors (terms or keywords) from a Chinese-English bibliographic database. A concept space of related descriptors was then generated using a co-occurrence analysis technique. Like a man-made thesaurus, the system-generated concept space can be used to generate additional semantically-relevant terms for search. For concept classification and clustering, a variant of a Hopfield neural network was developed to cluster similar concept descriptors and to generate a small number of concept groups to represent (summarize) the subject matter of the database. The concept space approach to information classification and retrieval has been adopted by the authors in other scientific databases and business applications, but multilingual information retrieval presents a unique challenge. This research reports our experiment on multilingual databases. Our system was initially developed in the MS-DOS environment, running ETEN Chinese operating system. For performance reasons, it was then tested on a UNIX-based system. Due to the unique ideographic nature of the Chinese language, a Chinese term-phrase indexing paradigm considering the ideographic characteristics of Chinese was developed as a multilingual information classification model. By applying the neural network based concept classification technique, the model presents a novel way of organizing unstructured multilingual information. PMID:18263007

  15. 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.

  16. 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.

  17. Evaluation of Methods for Robust, Automatic Detection of Net Tear with Remotely Operated Vehicle and Remote Sensing

    OpenAIRE

    Haugene, Tormod

    2014-01-01

    Accompanying the continuous growth of the aquaculture fish farming industry in the recent years, the usage of Remotely Operated Vehicles (ROV) for regular inspections of net integrity has become increasingly common. For a human ROV operator, routine inspections can be repetitious and time consuming, and improving the regularity and efficiency of these operations are of interest. The aim of this study was therefore be to develop a robust technique for automatic detection of net damage with an ...

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. Towards Automatic Trunk Classification on Young Conifers

    DEFF Research Database (Denmark)

    Petri, Stig; Immerkær, John

    2009-01-01

    trunk. The classification performance of the method was investigated using a SVM and 10-fold stratified cross validation, resulting in a correct classification rate of 90.2% (101/112) when discriminating between trees having one top shoot and trees having multiple top shoots. Future work aims to improve...

  4. 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...

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

    DEFF Research Database (Denmark)

    Petri, Stig

    throughout development to keep this bias to a minimum. The specific goals with regard to classification performance was determined in cooperation with Peter Schjøtt of the Danish Garden Nursery Owner Association, and set to an average error rate of less than 2% for all categories of defects, and a goal of a...... classification models, and evaluating 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...

  6. 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.

  7. MaNIAC-UAV - a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles

    Science.gov (United States)

    Henrique Castelo Branco, Luiz; César Lima Segantine, Paulo

    2015-09-01

    Intelligent Transportation Systems - ITS is a set of integrated technologies (Remote Sensing, Image Processing, Communications Systems and others) that aim to offer services and advanced traffic management for the several transportation modes (road, air and rail). Collect data on the characteristics and conditions of the road surface and keep them update is an important and difficult task that needs to be currently managed in order to reduce accidents and vehicle maintenance costs. Nowadays several roads and highways are paved, but usually there is insufficient updated data about current condition and status. There are different types of pavement defects on the roads and to keep them in good condition they should be constantly monitored and maintained according to pavement management strategy. This paper presents a methodology to obtain, automatically, information about the conditions of the highway asphalt pavement. Data collection was done through remote sensing using an UAV (Unmanned Aerial Vehicle) and the image processing and pattern recognition techniques through Geographic Information System.

  8. 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.

  9. Automatic Cataract Classification based on Ultrasound Technique Using Machine Learning: A comparative Study

    Science.gov (United States)

    Caxinha, Miguel; Velte, Elena; Santos, Mário; Perdigão, Fernando; Amaro, João; Gomes, Marco; Santos, Jaime

    This paper addresses the use of computer-aided diagnosis (CAD) system for the cataract classification based on ultrasound technique. Ultrasound A-scan signals were acquired in 220 porcine lenses. B-mode and Nakagami images were constructed. Ninety-seven parameters were extracted from acoustical, spectral and image textural analyses and were subjected to feature selection by Principal Component Analysis (PCA). Bayes, K Nearest-Neighbors (KNN), Fisher Linear Discriminant (FLD) and Support Vector Machine (SVM) classifiers were tested. The classification of healthy and cataractous lenses shows a good performance for the four classifiers (F-measure ≥92.68%) with SVM showing the highest performance (90.62%) for initial versus severe cataract classification.

  10. 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.

  11. Semi-automatic supervised classification of minerals from x-ray mapping images

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Flesche, Harald; Larsen, Rasmus;

    1998-01-01

    spectroscopy (EDS) in a scanning electron microscope (SEM). Extensions to traditional multivariate statistical methods are applied to perform the classification. Training sets are grown from one or a few seed points by a method that ensures spatial and spectral closeness of observations. Spectral closeness is...... training, a standard quadratic classifier is applied. The performance for each parameter setting is measured by the overall misclassification rate on an independently generated validation set. The classification method is presently used as a routine petrographical analysis method at Norsk Hydro Research...

  12. 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.

  13. Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine.

    Science.gov (United States)

    Cao, Cheng; Tutwiler, Richard Laurence; Slobounov, Semyon

    2008-08-01

    There is a growing body of knowledge indicating long-lasting residual electroencephalography (EEG) abnormalities in concussed athletes that may persist up to 10-year postinjury. Most often, these abnormalities are initially overlooked using traditional concussion assessment tools. Accordingly, premature return to sport participation may lead to recurrent episodes of concussion, increasing the risk of recurrent concussions with more severe consequences. Sixty-one athletes at high risk for concussion (i.e., collegiate rugby and football players) were recruited and underwent EEG baseline assessment. Thirty of these athletes suffered from concussion and were retested at day 30 postinjury. A number of task-related EEG recordings were conducted. A novel classification algorithm, the support vector machine (SVM), was applied as a classifier to identify residual functional abnormalities in athletes suffering from concussion using a multichannel EEG data set. The total accuracy of the classifier using the 10 features was 77.1%. The classifier has a high sensitivity of 96.7% (linear SVM), 80.0% (nonlinear SVM), and a relatively lower but acceptable selectivity of 69.1% (linear SVM) and 75.0% (nonlinear SVM). The major findings of this report are as follows: 1) discriminative features were observed at theta, alpha, and beta frequency bands, 2) the minimal redundancy relevance method was identified as being superior to the univariate t -test method in selecting features for the model calculation, 3) the EEG features selected for the classification model are linked to temporal and occipital areas, and 4) postural parameters influence EEG data set and can be used as discriminative features for the classification model. Overall, this report provides sufficient evidence that 10 EEG features selected for final analysis and SVM may be potentially used in clinical practice for automatic classification of athletes with residual brain functional abnormalities following a concussion

  14. 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.

  15. On Automatic Voice Casting for Expressive Speech: Speaker Recognition vs. Speech Classification

    OpenAIRE

    Obin, Nicolas; Roebel, Axel; Bachman, Grégoire

    2014-01-01

    This paper presents the first large-scale automatic voice casting system, and explores the adaptation of speaker recognition techniques to measure voice similarities. The proposed system is based on the representation of a voice by classes (e.g., age/gender, voice quality, emotion). First, a multi-label system is used to classify speech into classes. Then, the output probabilities for each class are concatenated to form a vector that represents the vocal signature of a speech recording. Final...

  16. 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...

  17. Automatic Incident Classification for Big Traffic Data by Adaptive Boosting SVM

    OpenAIRE

    Wang, Li-li; Ngan, Henry Y. T.; Yung, Nelson H. C.

    2015-01-01

    Modern cities experience heavy traffic flows and congestions regularly across space and time. Monitoring traffic situations becomes an important challenge for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it is helpful to automatically detect and classify different traffic incidents such as severity of congestion, abnormal driving pattern, abrupt or illegal stop on road, etc. Although most TCSS are equipped with basic incident detection algorithms, they are however cr...

  18. 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

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

    Science.gov (United States)

    Lemanzyk, Thomas; Anding, Katharina; Linss, Gerhard; Rodriguez Hernández, Jorge; Theska, René

    2015-02-01

    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.

  20. A software tool for automatic classification and segmentation of 2D/3D medical images

    International Nuclear Information System (INIS)

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided

  1. 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.

  2. A Survey on Automatic Vehicle Parking and Retrieval Using Android Smartphone

    Directory of Open Access Journals (Sweden)

    Arockia Muthu.A*

    2014-11-01

    Full Text Available This paper focuses the construction of the system which assists in parking and retrieving of the car automatically. Unlike previous generation this system drives automatically while parking which is controlled with the help of smart phone. Remote door control is another feature that helps the user to open the car door with aid of smart phone. The system is equipped with an alarm system in case of disturbance situation. The proposed system uses a 3-axis accelerometer which replaces the motion sensor in the existing system and provides a secure means of parking system. This system is going to be implemented using ARM cortex-M3 microcontroller.

  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

    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, excluding the complexities of real-life systems and situations. The paper reviews and discusses issues......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...

  4. 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

  5. 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.

  6. 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...

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. Parallelization of automatic classification systems based on support vector machines: Comparison and application to JET database

    International Nuclear Information System (INIS)

    In learning machines, the larger the training dataset the better model can be obtained. Therefore, the training phase can be very demanding in terms of computational time in mono-processor computers. To overcome this difficulty, codes should be parallelized. This article describes two general purpose parallelization techniques of a classification system based on support vector machines (SVM). Both of them have been applied to the recognition of the L-H confinement regime in JET. This has allowed reducing the training computation time from 70 h to 3 min.

  12. Automatic classification of harmonic data using $k$-means and least square support vector machine

    OpenAIRE

    ERİŞTİ, HÜSEYİN; TÜMEN, VEDAT; YILDIRIM, ÖZAL; ERİŞTİ, BELKIS; DEMİR, Yakup

    2015-01-01

    In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using $k$-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a $k$-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained throug...

  13. 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

    difficult to define, and seemingly based on factors more broad than acoustics, this remarkable result motivates investigation into, among other things, why it works and what it means for how humans organize music. In this paper, we review the application of SRC and ATM to recognizing genre, and attempt to......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...

  14. Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

    International Nuclear Information System (INIS)

    In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis

  15. Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

    Science.gov (United States)

    Kiyuna, Tomoharu; Saito, Akira; Marugame, Atsushi; Yamashita, Yoshiko; Ogura, Maki; Cosatto, Eric; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2013-03-01

    Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

  16. 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.

  17. 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.

  18. Automatic segmentation and classification of human brain image based on a fuzzy brain atlas

    Science.gov (United States)

    Tan, Ou; Jia, Chunguang; Duan, Huilong; Lu, Weixue

    1998-09-01

    It is difficult to automatically segment and classify tomograph images of actual patient's brain. Therefore, many interactive operations are performed. It is very time consuming and its precision is much depended on the user. In this paper, we combine a brain atlas and 3D fuzzy image segmentation into the image matching. It can not only find out the precise boundary of anatomic structure but also save time of the interactive operation. At first, the anatomic information of atlas is mapped into tomograph images of actual brain with a two step image matching method. Then, based on the mapping result, a 3D fuzzy structure mask is calculated. With the fuzzy information of anatomic structure, a new method of fuzzy clustering based on genetic algorithm is used to segment and classify the real brain image. There is only a minimum requirement of interaction in the whole process, including removing the skull and selecting some intrinsic point pairs.

  19. Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

    Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

  20. 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.

  1. 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.

  2. Development, Implementation and Evaluation of Segmentation Algorithms for the Automatic Classification of Cervical Cells

    Science.gov (United States)

    Macaulay, Calum Eric

    Cancer of the uterine cervix is one of the most common cancers in women. An effective screening program for pre-cancerous and cancerous lesions can dramatically reduce the mortality rate for this disease. In British Columbia where such a screening program has been in place for some time, 2500 to 3000 slides of cervical smears need to be examined daily. More than 35 years ago, it was recognized that an automated pre-screening system could greatly assist people in this task. Such a system would need to find and recognize stained cells, segment the images of these cells into nucleus and cytoplasm, numerically describe the characteristics of the cells, and use these features to discriminate between normal and abnormal cells. The thrust of this work was (1) to research and develop new segmentation methods and compare their performance to those in the literature, (2) to determine dependence of the numerical cell descriptors on the segmentation method used, (3) to determine the dependence of cell classification accuracy on the segmentation used, and (4) to test the hypothesis that using numerical cell descriptors one can correctly classify the cells. The segmentation accuracies of 32 different segmentation procedures were examined. It was found that the best nuclear segmentation procedure was able to correctly segment 98% of the nuclei of a 1000 and a 3680 image database. Similarly the best cytoplasmic segmentation procedure was found to correctly segment 98.5% of the cytoplasm of the same 1000 image database. Sixty-seven different numerical cell descriptors (features) were calculated for every segmented cell. On a database of 800 classified cervical cells these features when used in a linear discriminant function analysis could correctly classify 98.7% of the normal cells and 97.0% of the abnormal cells. While some features were found to vary a great deal between segmentation procedures, the classification accuracy of groups of features was found to be independent of the

  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 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. The...... that sleep contains six diverse latent sleep states and that state transitions are continuous processes. Conclusions: The model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders. (C) 2014 Elsevier B.V. All rights reserved.......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...

  5. 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.

  6. 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.

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

    Science.gov (United States)

    Hervas, Jaime Rubio; Reyhanoglu, Mahmut; Tang, Hui

    2014-12-01

    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. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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...

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

    OpenAIRE

    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 performance of the restraint system. In this study it is investigated whether braking settings as well as driver distraction influence the kinematic response of an occupant during braking events, ...

  15. Wavelet-SVM classification and automatic recognition of unstained viable cells in phase-contrast microscopy

    International Nuclear Information System (INIS)

    Irradiation of individual cultured mammalian cells with a pre-selected number of ions down to one ion per single cell is a useful experimental approach to investigating the low-dose ionising radiation exposure effects and thus contributing to a more realistic human cancer risk assessment. One of the crucial tasks of all the microbeam apparatuses is the visualisation, recognition and positioning of every individual cell of the cell culture to be irradiated. Before irradiations, mammalian cells (specifically, Chinese hamster V79 cells) are seeded and grown as a monolayer on a mylar surface used as the bottom of a specially designed holder. Manual recognition of unstained cells in a bright-field microscope is a time-consuming procedure; therefore, a parallel algorithm has been conceived and developed in order to speed up this irradiation protocol step. Many technical problems have been faced to overcome the complexity of the images to be analysed: cell discrimination in an inhomogeneous background, among many disturbing bodies mainly due to the mylar surface roughness and culture medium bodies; cell shapes, depending on how they attach to the surface, which phase of the cell cycle they are in and on cell density. Preliminary results of the recognition and classification based on a method of wavelet kernels for the support vector machine classifier will be presented. (authors)

  16. [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

  17. 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

  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. 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. PMID:26776211

  20. 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.

  1. 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…

  2. Pedestrian and car detection and classification for unmanned ground vehicle using 3D lidar and monocular camera

    Science.gov (United States)

    Cho, Kuk; Baeg, Seung-Ho; Lee, Kimin; Lee, Hae Seok; Park, SangDeok

    2011-05-01

    This paper describes an object detection and classification method for an Unmanned Ground Vehicle (UGV) using a range sensor and an image sensor. The range sensor and the image sensor are a 3D Light Detection And Ranging (LIDAR) sensor and a monocular camera, respectively. For safe driving of the UGV, pedestrians and cars should be detected on their moving routes of the vehicle. An object detection and classification techniques based on only a camera has an inherent problem. On the view point of detection with a camera, a certain algorithm should extract features and compare them with full input image data. The input image has a lot of information as object and environment. It is hard to make a decision of the classification. The image should have only one reliable object information to solve the problem. In this paper, we introduce a developed 3D LIDAR sensor and apply a fusion method both 3D LIDAR data and camera data. We describe a 3D LIDAR sensor which is developed by LG Innotek Consortium in Korea, named KIDAR-B25. The 3D LIDAR sensor detects objects, determines the object's Region of Interest (ROI) based on 3D information and sends it into a camera region for classification. In the 3D LIDAR domain, we recognize breakpoints using Kalman filter and then make a cluster using a line segment method to determine an object's ROI. In the image domain, we extract the object's feature data from the ROI region using a Haar-like feature method. Finally it is classified as a pedestrian or car using a trained database with an Adaboost algorithm. To verify our system, we make an experiment on the performance of our system which is mounted on a ground vehicle, through field tests in an urban area.

  3. 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...

  4. 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, ...

  5. 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.

  6. 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.

  7. Performance portability study of an automatic target detection and classification algorithm for hyperspectral image analysis using OpenCL

    Science.gov (United States)

    Bernabe, Sergio; Igual, Francisco D.; Botella, Guillermo; Garcia, Carlos; Prieto-Matias, Manuel; Plaza, Antonio

    2015-10-01

    Recent advances in heterogeneous high performance computing (HPC) have opened new avenues for demanding remote sensing applications. Perhaps one of the most popular algorithm in target detection and identification is the automatic target detection and classification algorithm (ATDCA) widely used in the hyperspectral image analysis community. Previous research has already investigated the mapping of ATDCA on graphics processing units (GPUs) and field programmable gate arrays (FPGAs), showing impressive speedup factors that allow its exploitation in time-critical scenarios. Based on these studies, our work explores the performance portability of a tuned OpenCL implementation across a range of processing devices including multicore processors, GPUs and other accelerators. This approach differs from previous papers, which focused on achieving the optimal performance on each platform. Here, we are more interested in the following issues: (1) evaluating if a single code written in OpenCL allows us to achieve acceptable performance across all of them, and (2) assessing the gap between our portable OpenCL code and those hand-tuned versions previously investigated. Our study includes the analysis of different tuning techniques that expose data parallelism as well as enable an efficient exploitation of the complex memory hierarchies found in these new heterogeneous devices. Experiments have been conducted using hyperspectral data sets collected by NASA's Airborne Visible Infra- red Imaging Spectrometer (AVIRIS) and the Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensors. To the best of our knowledge, this kind of analysis has not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

  8. 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.

  9. 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

  10. 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...

  11. 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.

  12. 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".

  13. 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...

  14. 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...

  15. A system to automatically classify and name any individual genome-sequenced organism independently of current biological classification and nomenclature.

    OpenAIRE

    Marakeby, Haitham; Badr, Eman; Torkey, Hanaa; Song, Yuhyun; Leman, Scotland; Monteil, Caroline; Lenwood S. Heath

    2014-01-01

    A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity withi...

  16. 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.

  17. Classification and Localization of Vehicle Occupants Using 3D Range Images

    OpenAIRE

    Devarakota, Pandu Ranga Rao

    2008-01-01

    This thesis deals with the problem of classifying automotive vehicle occupants and estimating their position. This information is critical in designing future smart airbag systems providing maximum protection for passengers. According to the American National Highway Traffic Safety Administration (NHTSA), since 1990, in the USA, 227 deaths have been attributed to airbags deployed in low-speed crashes which included 119 children, and 22 infants. In these cases, intelligent deployment of the ai...

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

    Science.gov (United States)

    Faganeli, J; Jager, F

    2010-03-01

    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%. PMID:20130344

  19. 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%

  20. 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.

  1. 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.

  2. 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.

  3. An image analysis and classification system for automatic weed species identification in different crops for precision weed management

    OpenAIRE

    Weis, Martin

    2010-01-01

    A system for the automatic weed detection in arable fields was developed in this thesis. With the resulting maps, weeds in fields can be controlled on a sub-field level, according to their abundance. The system contributes to the emerging field of Precision Farming technologies. Precision Farming technologies have been developed during the last two decades to refine the agricultural management practise. The goal of Precision Farming is to vary treatments within fields, according to the local ...

  4. 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.

  5. 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...

  6. 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.

  7. Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

    Science.gov (United States)

    Ressel, R.; Frost, A.; Lehner, S.

    2015-04-01

    Most icebergs present in northern latitudes originate from western Greenland glaciers, from where they drift into Baffin Bay, circulating north along Greenland coast and south along Canadian coast. Some of them drift more southwards up to Newfoundland, where they frequently cross shipping routes. Furthermore, the Arctic summer sea ice coverage significantly decreased over the last three decades. This has attracted numerous attentions from maritime end-users. To keep Arctic shipping routes safe, the monitoring of sea ice and icebergs is crucial. For this purpose, satellite-based Synthetic Aperture Radar (SAR) is well suited. Equipped with an active radar antenna, SAR satellites provide image data of the ocean and frozen waters independent of weather conditions, cloud cover or absence of daylight. In this paper, we present a processor for sea ice classification and (subsequent) iceberg detection based on TerraSAR-X imagery. In the classification step, texture features are extracted from the images and fed into a neural network, indicating areas of low sea ice concentration. Then, an adapted Constant False Alarm Rate (CFAR) detector is executed in order to detect icebergs. In the end, sea ice boundary and iceberg positions are output. Our experiments deal with HH polarized TerraSAR-X images taken in spring season in the Baffin Bay off the western Greenland coast, where both, sea ice and icebergs are present. Our results exemplify how a comprehensive ice processor with complementary information can be set up for near real time (NRT) service in ice infested waters.

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

    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

  9. Minimum Hamiltonian Ascent Trajectory Evaluation (MASTRE) program (update to automatic flight trajectory design, performance prediction, and vehicle sizing for support of Shuttle and Shuttle derived vehicles) engineering manual

    Science.gov (United States)

    Lyons, J. T.

    1993-01-01

    The Minimum Hamiltonian Ascent Trajectory Evaluation (MASTRE) program and its predecessors, the ROBOT and the RAGMOP programs, have had a long history of supporting MSFC in the simulation of space boosters for the purpose of performance evaluation. The ROBOT program was used in the simulation of the Saturn 1B and Saturn 5 vehicles in the 1960's and provided the first utilization of the minimum Hamiltonian (or min-H) methodology and the steepest ascent technique to solve the optimum trajectory problem. The advent of the Space Shuttle in the 1970's and its complex airplane design required a redesign of the trajectory simulation code since aerodynamic flight and controllability were required for proper simulation. The RAGMOP program was the first attempt to incorporate the complex equations of the Space Shuttle into an optimization tool by using an optimization method based on steepest ascent techniques (but without the min-H methodology). Development of the complex partial derivatives associated with the Space Shuttle configuration and using techniques from the RAGMOP program, the ROBOT program was redesigned to incorporate these additional complexities. This redesign created the MASTRE program, which was referred to as the Minimum Hamiltonian Ascent Shuttle TRajectory Evaluation program at that time. Unique to this program were first-stage (or booster) nonlinear aerodynamics, upper-stage linear aerodynamics, engine control via moment balance, liquid and solid thrust forces, variable liquid throttling to maintain constant acceleration limits, and a total upgrade of the equations used in the forward and backward integration segments of the program. This modification of the MASTRE code has been used to simulate the new space vehicles associated with the National Launch Systems (NLS). Although not as complicated as the Space Shuttle, the simulation and analysis of the NLS vehicles required additional modifications to the MASTRE program in the areas of providing

  10. 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.

  11. Optimal Line Pressure Control for an Automatic Transmission-Based Parallel Hybrid Electric Vehicle considering Mode Change and Gear Shift

    OpenAIRE

    Minseok Song; Joseph Oh; Seokhwan Choi; Youngchul Kim; Hyunsoo Kim

    2014-01-01

    An optimal line pressure control algorithm was proposed for the fuel economy improvement of an AT-based parallel hybrid electric vehicle (HEV). By performing lever analysis at each gear step, the required line pressure was obtained considering the torque ratio of the friction elements. In addition, the required line pressure of the mode clutch was calculated. Based on these results, the optimal line pressure map at each gear step of the EV and HEV modes was presented. Using the line pressure ...

  12. 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...

  13. 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 .

  14. Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

    Science.gov (United States)

    Möller, Christiane; Pijnenburg, Yolande A L; van der Flier, Wiesje M; Versteeg, Adriaan; Tijms, Betty; de Munck, Jan C; Hafkemeijer, Anne; Rombouts, Serge A R B; van der Grond, Jeroen; van Swieten, John; Dopper, Elise; Scheltens, Philip; Barkhof, Frederik; Vrenken, Hugo; Wink, Alle Meije

    2016-06-01

    Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD

  15. 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)

  16. Automatic recognition and validation of the common carotid artery wall segmentation in 100 longitudinal ultrasound images: an integrated approach using feature selection, fitting and classification

    Science.gov (United States)

    Molinari, Filippo; Zeng, Guang; Suri, Jasjit S.

    2010-03-01

    Most of the algorithms for the common carotid artery (CCA) segmentation require human interaction. The aim of this study is to show a novel accurate algorithm for the computer-based automated tracing of CCA in longitudinal B-Mode ultrasound images. One hundred ultrasound B-Mode longitudinal images of the CCA were processed to delineate the region of interest containing the artery. The algorithm is based on geometric feature extraction, line fitting, and classification. Output of the algorithm is the tracings of the near and far adventitia layers. Performance of the algorithm was validated against human tracings (ground truth) and benchmarked with a previously developed automated technique. Ninety-eight images were correctly processed, resulting in an overall system error (with respect to ground truth) equal to 0.18 +/- 0.17 mm (near adventitia) and 0.17 +/- 0.24 mm (far adventitia). In far adventitia detection, our novel technique outperformed the current standard method, which showed overall system errors equal to 0.07 +/- 0.07 mm and 0.49 +/- 0.27 mm for near and far adventitia, respectively. We also showed that our new technique is quite insensitive to noise and has performance independent on the subset of images used for training the classifiers. Superior architecture of this methodology could constitute a general basis for the development of completely automatic CCA segmentation strategies.

  17. 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.

  18. 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.

  19. 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...

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

    OpenAIRE

    Baljit Singh Mokha; Satish Kumar

    2015-01-01

    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 differ...

  1. 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% .

  2. 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.

  3. 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 no...... software packages such as SPM, FSL, and FreeSurfer....

  4. 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...

  5. 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.

  6. Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

    Science.gov (United States)

    Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

    2015-12-01

    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.

  7. 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

  8. 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。

  9. 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

  10. 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...

  11. 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.

  12. 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...

  13. 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...

  14. 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。

  15. 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.

  16. 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.

  17. 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.)

  18. 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

  19. 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

  20. Text classification method review

    OpenAIRE

    Mahinovs, Aigars; Tiwari, Ashutosh; Roy, Rajkumar; Baxter, David

    2007-01-01

    With the explosion of information fuelled by the growth of the World Wide Web it is no longer feasible for a human observer to understand all the data coming in or even classify it into categories. With this growth of information and simultaneous growth of available computing power automatic classification of data, particularly textual data, gains increasingly high importance. This paper provides a review of generic text classification process, phases of that process and met...

  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. 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。

  3. 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…

  4. 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.

  5. 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.

  6. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles

    OpenAIRE

    Zhang, Linhuan; Ahamed, Tofael; Zhang, Yan; Gao, Pengbo; Takigawa, Tomohiro

    2016-01-01

    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 wa...

  7. Automatic counterfeit protection system code classification

    Science.gov (United States)

    Van Beusekom, Joost; Schreyer, Marco; Breuel, Thomas M.

    2010-01-01

    Wide availability of cheap high-quality printing techniques make document forgery an easy task that can easily be done by most people using standard computer and printing hardware. To prevent the use of color laser printers or color copiers for counterfeiting e.g. money or other valuable documents, many of these machines print Counterfeit Protection System (CPS) codes on the page. These small yellow dots encode information about the specific printer and allow the questioned document examiner in cooperation with the manufacturers to track down the printer that was used to generate the document. However, the access to the methods to decode the tracking dots pattern is restricted. The exact decoding of a tracking pattern is often not necessary, as tracking the pattern down to the printer class may be enough. In this paper we present a method that detects what CPS pattern class was used in a given document. This can be used to specify the printer class that the document was printed on. Evaluation proved an accuracy of up to 91%.

  8. Automatic color map digitization by spectral classification

    Science.gov (United States)

    Chu, N. Y.; Anuta, P. E.

    1979-01-01

    A method of converting polygon map information into a digital form which does not require manual tracing of polygon edges is discussed. The maps must be in color-coded format with a unique color for each category in the map. Color scanning using a microdensitometer is employed and a three-channel color separation digital data set is generated. The digital data are then classified by using a Gaussian maximum likelihood classifier, and the resulting digitized map is evaluated. Very good agreement is observed between the classified and original map.

  9. 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...

  10. 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.

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

    Institute of Scientific and Technical Information of China (English)

    周彤; 彭彦昆

    2013-01-01

    , 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 recognition time of each sample was 0.879 s. The results showed that the algorithm could meet the beef marbling testing and grading in precision to the practical application. Moreover, this method is of great significance for the development of an automatic classification system.%  为了解决牛肉大理石花纹在人工分级中准确率和效率低的问题,该文基于计算机视觉和图像处理技术提出一种实用的牛肉大理石花纹自动评估和分级方法。通过图像解析,利用所提出的分级算法实现牛肉大理石花纹的快速提取,并计算反映大理石花纹丰富程度的10个特征参数。使用特征参数建立主成分回归模型,对牛肉大理石花纹等级进行预测,预测相关系数 Rv=0.88,预测标准差 SEP=0.56。校正时模型总体的回判正确率为97.0%,验证时总体的判别正确率为91.2%。在此基础上,开发了大理石花纹自

  12. 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

  13. 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.

  14. 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

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

    Directory of Open Access Journals (Sweden)

    Zutao Zhang

    2016-02-01

    Full Text Available 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.

  16. 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.

  17. Monitoring human and vehicle activities using airborne video

    Science.gov (United States)

    Cutler, Ross; Shekhar, Chandra S.; Burns, B.; Chellappa, Rama; Bolles, Robert C.; Davis, Larry S.

    2000-05-01

    Ongoing work in Activity Monitoring (AM) for the Airborne Video Surveillance (AVS) project is described. The goal for AM is to recognize activities of interest involving humans and vehicles using airborne video. AM consists of three major components: (1) moving object detection, tracking, and classification; (2) image to site-model registration; (3) activity recognition. Detecting and tracking humans and vehicles form airborne video is a challenging problem due to image noise, low GSD, poor contrast, motion parallax, motion blur, and camera blur, and camera jitter. We use frame-to- frame affine-warping stabilization and temporally integrated intensity differences to detect independent motion. Moving objects are initially tracked using nearest-neighbor correspondence, followed by a greedy method that favors long track lengths and assumes locally constant velocity. Object classification is based on object size, velocity, and periodicity of motion. Site-model registration uses GPS information and camera/airplane orientations to provide an initial geolocation with +/- 100m accuracy at an elevation of 1000m. A semi-automatic procedure is utilized to improve the accuracy to +/- 5m. The activity recognition component uses the geolocated tracked objects and the site-model to detect pre-specified activities, such as people entering a forbidden area and a group of vehicles leaving a staging area.

  18. 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...

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. An Autonomous Vehicle for Farming Using GPS

    Directory of Open Access Journals (Sweden)

    Neelam Rup Prakash

    2012-06-01

    Full Text Available This paper presents the automatic steering control of farming vehicle using GPS receiver. Automatic steering devices for farming vehicles like tractors, seeding vehicle, weed control vehicle, spraying machine vehicle etc. have the task to relieve the driver from the physical and mental stress of monotonous steering work. Simultaneously, they are intended to help him to exploit machines and farming vehicle closer to their full performance and improve the quality of work. Vehicles frequently have to be steered in and exact straight line and along rows in the farm land.GPS receiver fetches the information of positions (latitude and longitude of the farm land which needs to be cultivated. With the help of GPS and microcontroller (Arm9 we calculate the boundary of farm land, slope of straight line and angle of movement with the help of slope changes. The microcontroller generates the control signals to stepper motor for steering of vehicle.

  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. A survey on phrase structure learning methods for text classification

    OpenAIRE

    Prasad, Reshma; Sebastian, Mary Priya

    2014-01-01

    Text classification is a task of automatic classification of text into one of the predefined categories. The problem of text classification has been widely studied in different communities like natural language processing, data mining and information retrieval. Text classification is an important constituent in many information management tasks like topic identification, spam filtering, email routing, language identification, genre classification, readability assessment etc. The performance o...

  6. 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

  7. 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...

  8. 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

  9. 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 ...

  10. 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

  11. 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.

  12. 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.

  13. 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...

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Vehicle number plate recognition using multiple layer back propagation neural networks

    OpenAIRE

    tuti Asthana; Niresh Sharma, Rajdeep Singh

    2011-01-01

    Automatic Vehicle Number Plate Recognition is aspecial form of optical character recognition (OCR). Vehiclenumber plate recognition is a type of technology, mainlysoftware, which enables computer systems to read automaticallythe registration number of vehicles from digital pictures. Theproposed algorithm develops high quality recognition softwarefor the automatic recognition of vehicle license plates. In thisapproach Multilayer feed-forward back-propagation algorithmusing three hidden layers ...

  2. 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...

  3. 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.

  4. 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.

  5. Vehicle License Plate Recognition System

    Directory of Open Access Journals (Sweden)

    Meenakshi

    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.

  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 Color Recognition using Convolutional Neural Network

    OpenAIRE

    Rachmadi, Reza Fuad; Purnama, I. Ketut Eddy

    2015-01-01

    Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture. The...

  8. 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.

  9. Vehicle to Vehicle Services

    DEFF Research Database (Denmark)

    Brønsted, Jeppe Rørbæk

    2008-01-01

    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...... connectivity, mobility, and availability of services. The dissertation consists of two parts. Part I gives an overview of service oriented architecture for pervasive computing systems and describes the contributions of the publications listed in part II. We investigate architecture for vehicular technology...... 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...

  10. 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.

  11. 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...

  12. 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...

  13. Automatic Multilevel Medical Image Annotation and Retrieval

    OpenAIRE

    Mueen, A.; Zainuddin, R.; Baba, M. Sapiyan

    2007-01-01

    Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for...

  14. Automatic detection of abnormalities in mammograms

    OpenAIRE

    Suhail, Zobia; Sarwar, Mansoor; Murtaza, Kashif

    2015-01-01

    Background In recent years, an increased interest has been seen in the area of medical image processing and, as a consequence, Computer Aided Diagnostic (CAD) systems. The basic purpose of CAD systems is to assist doctors in the process of diagnosis. CAD systems, however, are quite expensive, especially, in most of the developing countries. Our focus is on developing a low-cost CAD system. Today, most of the CAD systems regarding mammogram classification target automatic detection of calcific...

  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. Web Page Classification Using SVM and FURIA

    OpenAIRE

    P. Madhubala; K. Murugesan

    2015-01-01

    Text Classification classifies a document, under a predefined category. Mostly, an automatic text classification is an important application taken as a research topic, since the inception of digital documents. In this study, Hypernyms, superordinate words are identified in web and clubbed with entailment rule acquisition. Available tree of hyponym words in the document has been created and used with dependency tree. Features extraction is performed with weighted Term Frequency-Inverse Documen...

  18. 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.

  19. Automatic Keywords Extraction for Punjabi Language

    Directory of Open Access Journals (Sweden)

    Vishal Gupta

    2011-09-01

    Full Text Available Automatic keywords extraction is the task to identify a small set of words, key phrases, keywords, or key segments from a document that can describe the meaning of the document. Keywords are useful tools as they give the shortest summary of the document. This paper concentrates on Automatic keywords extraction for Punjabi language text. It includes various phases like removing stop words, Identification of Punjabi nouns and noun stemming, Calculation of Term Frequency and Inverse Sentence Frequency (TF-ISF, Punjabi keywords as nouns with high TF-ISF score and title/headline feature for Punjabi text. The extracted keywords are very much helpful in automatic indexing, text summarization, information retrieval, classification, clustering, topic detection and tracking and web searches etc.

  20. 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.

  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. Strategic Classification

    OpenAIRE

    Hardt, Moritz; Megiddo, Nimrod; Papadimitriou, Christos; Wootters, Mary

    2015-01-01

    Machine learning relies on the assumption that unseen test instances of a classification problem follow the same distribution as observed training data. However, this principle can break down when machine learning is used to make important decisions about the welfare (employment, education, health) of strategic individuals. Knowing information about the classifier, such individuals may manipulate their attributes in order to obtain a better classification outcome. As a result of this behavior...

  3. 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.

  4. 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.

  5. Moving Vehicle Recognition and Feature Extraction From Tunnel Monitoring Videos

    OpenAIRE

    Aiyan Lu; Luo Zhong; Lin Li; Qingbo Wang

    2013-01-01

    In recent decades, many government agencies and famous universities are researching the intelligent traffic video monitoring system. According to the tunnel monitoring video, this paper uses the combination of background subtraction method and three frame differencing method for moving vehicle detection , and designs the geometric parameters and combined parameters for vehicle classification, finally makes up a vehicle classifier, based on these characteristics parameters.  

  6. Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies

    CERN Document Server

    McMahon, J; Mahon, John Mc

    1995-01-01

    An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have sho...

  7. 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%.

  8. 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.

  9. 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 ...

  10. 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.

  11. 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...

  12. Sports Type Classification using Signature Heatmaps

    DEFF Research Database (Denmark)

    Gade, Rikke; Moeslund, Thomas B.

    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 two weeks of video are very promising with a correct classification of 90.76 %....

  13. 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.

  14. Identification of Car Passengers with RFID for Automatic Crash Notification

    OpenAIRE

    Ouyang, Dongfang

    2009-01-01

    Automatic Crash Notification is a system designed to be used in a crash situation. When a crash occurs, the intelligent system is activated and automatically sends select crash details to the appropriate Emergency Medical Service Center. These details can be the position of the vehicle and the likely severity of the damage. Using the information, the medical treatment resources demanded for the accident is assessed at Emergency Center. Accordingly, first-aid facilities are promptly and proper...

  15. Nature Conservation Drones for Automatic Localization and Counting of Animals

    OpenAIRE

    Gemert, van, M.J.C.; Verschoor, C.R.; Mettes, P.; Epema, K.; Koh, L. P.; Wich, S.

    2014-01-01

    This paper is concerned with nature conservation by automatically monitoring animal distribution and animal abundance. Typically, such conservation tasks are performed manually on foot or after an aerial recording from a manned aircraft. Such manual approaches are expensive, slow and labor intensive. In this paper, we investigate the combination of small unmanned aerial vehicles (UAVs or “drones”) with automatic object recognition techniques as a viable solution to manual animal surveying. Si...

  16. 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.

  17. Electric vehicles

    Science.gov (United States)

    1990-03-01

    Quiet, clean, and efficient, electric vehicles (EVs) may someday become a practical mode of transportation for the general public. Electric vehicles can provide many advantages for the nation's environment and energy supply because they run on electricity, which can be produced from many sources of energy such as coal, natural gas, uranium, and hydropower. These vehicles offer fuel versatility to the transportation sector, which depends almost solely on oil for its energy needs. Electric vehicles are any mode of transportation operated by a motor that receives electricity from a battery or fuel cell. EVs come in all shapes and sizes and may be used for different tasks. Some EVs are small and simple, such as golf carts and electric wheel chairs. Others are larger and more complex, such as automobile and vans. Some EVs, such as fork lifts, are used in industries. In this fact sheet, we will discuss mostly automobiles and vans. There are also variations on electric vehicles, such as hybrid vehicles and solar-powered vehicles. Hybrid vehicles use electricity as their primary source of energy, however, they also use a backup source of energy, such as gasoline, methanol or ethanol. Solar-powered vehicles are electric vehicles that use photovoltaic cells (cells that convert solar energy to electricity) rather than utility-supplied electricity to recharge the batteries. These concepts are discussed.

  18. 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.

  19. 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

  20. 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...

  1. 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...

  2. Automatic mobile inspecting and monitoring device

    International Nuclear Information System (INIS)

    Purpose: To eliminate a treating operation of a signal transmission cable for an automatic mobile inspecting and monitoring device. Constitution: Signals from respective pieces monitoring equipment carried on a monitoring vehicle are temporarily stored in a memory by the operation of an arithmetic controller. When the vehicle reaches each repeating station, the stored contents of the memory are sequentially read by an instruction from the controller, and are applied through a connector to the repeating station. The station is connected through a connecting cable penetrating a reactor container to an operation and monitor board. Thus a long cable for connecting the vehicle to the penetration is unnecessary, thereby eliminating the signal transmission cable treating operation. (Yoshino, Y.)

  3. Investigating text message classification using case-based reasoning

    OpenAIRE

    Healy, Matt, (Thesis)

    2007-01-01

    Text classification is the categorization of text into a predefined set of categories. Text classification is becoming increasingly important given the large volume of text stored electronically e.g. email, digital libraries and the World Wide Web (WWW). These documents represent a massive amount of information that can be accessed easily. To gain benefit from using this information requires organisation. One way of organising it automatically is to use text classification. A number of well k...

  4. Decomposition and classification of electroencephalography data

    DEFF Research Database (Denmark)

    Frølich, Laura

    This thesis is about linear and multi-linear analyses of electroencephalography (EEG) data and classification of estimated EEG sources. One contribution consists of an automatic classification method for independent components (ICs) of EEG data and a freely available implementation as an EEGLab...... plug-in, “IC Classification into Multiple Artefact Classes” (IC_MARC). Four artefact classes (blinks, heart beats, lateral eye movements, and muscle contractions), a neural class, and a mixed class (representing none or a mix of the other classes) were considered. We showed that classification is...... possible between subjects within studies over all classes. When generalising across studies a high classification rate of neural vs. non-neural ICs was retained but the multi-class performance dropped. In another study, we used IC_MARC to compare the ability to separate artefactual from neural sources of...

  5. 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)...

  6. Classifying Classification

    Science.gov (United States)

    Novakowski, Janice

    2009-01-01

    This article describes the experience of a group of first-grade teachers as they tackled the science process of classification, a targeted learning objective for the first grade. While the two-year process was not easy and required teachers to teach in a new, more investigation-oriented way, the benefits were great. The project helped teachers and…

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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.

  12. Automatic Implantable Cardiac Defibrillator

    Medline Plus

    Full Text Available Automatic Implantable Cardiac Defibrillator February 19, 2009 Halifax Health Medical Center, Daytona Beach, FL Welcome to Halifax Health Daytona Beach, Florida. Over the next hour you' ...

  13. 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)

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. Robotic vehicle

    Science.gov (United States)

    Box, W. Donald

    1997-01-01

    A robotic vehicle for travel through a conduit. The robotic vehicle includes forward and rear housings each having a hub portion, and each being provided with surface engaging mechanisms for selectively engaging the walls of the conduit such that the housings can be selectively held in stationary positions within the conduit. The surface engaging mechanisms of each housing includes a plurality of extendable appendages, each of which is radially extendable relative to the operatively associated hub portion between a retracted position and a radially extended position. The robotic vehicle also includes at least three selectively extendable members extending between the forward and rear housings, for selectively changing the distance between the forward and rear housings to effect movement of the robotic vehicle.

  19. Two Systems for Automatic Music Genre Recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2012-01-01

    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......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...

  20. Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses Evaluación empírica de tres métodos de aprendizaje automático para clasificar automáticamente diagnósticos de neoplasias

    Directory of Open Access Journals (Sweden)

    José Luis Jara

    2011-12-01

    Full Text Available Diagnoses are a valuable source of information for evaluating a health system. However, they are not used extensively by information systems because diagnoses are normally written in natural language. This work empirically evaluates three machine learning methods to automatically assign codes from the International Classification of Diseases (10th Revision to 3,335 distinct diagnoses of neoplasms obtained from UMLS®. This evaluation is conducted on three different types of preprocessing. The results are encouraging: a well-known rule induction method and maximum entropy models achieve 90% accuracy in a balanced cross-validation experiment.Los diagnósticos médicos son una fuente valiosa de información para evaluar el funcionamiento de un sistema de salud. Sin embargo, su utilización en sistemas de información se ve dificultada porque éstos se encuentran normalmente escritos en lenguaje natural. Este trabajo evalúa empíricamente tres métodos de Aprendizaje Automático para asignar códigos de acuerdo a la Clasificación Internacional de Enfermedades (décima versión a 3.335 diferentes diagnósticos de neoplasias extraídos desde UMLS®. Esta evaluación se realiza con tres tipos distintos de preprocesamiento. Los resultados son alentadores: un conocido método de inducción de reglas de decisión y modelos de entropía máxima obtienen alrededor de 90% accuracy en una validación cruzada balanceada.

  1. Performance of an Automated-Mixed-Traffic-Vehicle /AMTV/ System. [urban people mover

    Science.gov (United States)

    Peng, T. K. C.; Chon, K.

    1978-01-01

    This study analyzes the operation and evaluates the expected performance of a proposed automatic guideway transit system which uses low-speed Automated Mixed Traffic Vehicles (AMTV's). Vehicle scheduling and headway control policies are evaluated with a transit system simulation model. The effect of mixed-traffic interference on the average vehicle speed is examined with a vehicle-pedestrian interface model. Control parameters regulating vehicle speed are evaluated for safe stopping and passenger comfort.

  2. 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...

  3. 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.

  4. 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...

  5. Music Genre Classification Systems - A Computational Approach

    DEFF Research Database (Denmark)

    Ahrendt, Peter

    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 systems which use the raw audio signal as input to estimate the corresponding genre. This is in...... contrast to systems which use e.g. a symbolic representation or textual information about the music. The approach to music genre classification systems has here been system-oriented. In other words, all the different aspects of the systems have been considered and it is emphasized that the systems should...

  6. 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.

  7. PLANNING BASED ON CLASSIFICATION BY INDUCTION GRAPH

    Directory of Open Access Journals (Sweden)

    Sofia Benbelkacem

    2013-11-01

    Full Text Available In Artificial Intelligence, planning refers to an area of research that proposes to develop systems that can automatically generate a result set, in the form of an integrated decisionmaking system through a formal procedure, known as plan. Instead of resorting to the scheduling algorithms to generate plans, it is proposed to operate the automatic learning by decision tree to optimize time. In this paper, we propose to build a classification model by induction graph from a learning sample containing plans that have an associated set of descriptors whose values change depending on each plan. This model will then operate for classifying new cases by assigning the appropriate plan.

  8. 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.

  9. Advanced Path Following Control of an Overactuated Robotic Vehicle

    OpenAIRE

    Ritzer, Peter; Winter, Christoph; Brembeck, Jonathan

    2015-01-01

    This work describes an advanced path following control strategy enabling overactuated robotic vehicles like the ROboMObil (ROMO) [1] to automatically follow predefined paths while all states of the vehicle's planar motion are controlled. This strategy is useful for autonomous vehicles which are guided along online generated paths including severe driving maneuvers caused by e.g. obstacle avoidance. The proposed approach combines path following, i.e. tracking a plane curve without a priori tim...

  10. 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

  11. 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…

  12. 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 cla...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  13. 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.

  14. 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

  15. Automatic Program Development

    DEFF Research Database (Denmark)

    Automatic Program Development is a tribute to Robert Paige (1947-1999), our accomplished and respected colleague, and moreover our good friend, whose untimely passing was a loss to our academic and research community. We have collected the revised, updated versions of the papers published in his...... honor in the Higher-Order and Symbolic Computation Journal in the years 2003 and 2005. Among them there are two papers by Bob: (i) a retrospective view of his research lines, and (ii) a proposal for future studies in the area of the automatic program derivation. The book also includes some papers by...... members of the IFIP Working Group 2.1 of which Bob was an active member. All papers are related to some of the research interests of Bob and, in particular, to the transformational development of programs and their algorithmic derivation from formal specifications. Automatic Program Development offers a...

  16. Text Classification: A Sequential Reading Approach

    CERN Document Server

    Dulac-Arnold, Gabriel; Gallinari, Patrick

    2011-01-01

    We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document sentences sequentially and learns to stop as soon as enough information was read for deciding. The proposed algorithm is based on a modelisation of Text Classification as a Markov Decision Process and learns by using Reinforcement Learning. Experiments on four different classical mono-label corpora show that the proposed approach performs comparably to classical SVM approaches for large training sets, and better for small training sets. In addition, the model automatically adapts its reading process to the quantity of training information provided.

  17. Performance Comparison of Musical Instrument Family Classification Using Soft Set

    Directory of Open Access Journals (Sweden)

    Saima Anwar Lashari

    2012-08-01

    Full Text Available Nowadays, it appears essential to design automatic and efficacious classification algorithm for the musical instruments. Automatic classification of musical instruments is made by extracting relevant features from the audio samples, afterward classification algorithm is used (using these extracted features to identify into which of a set of classes, the sound sample is possible to fit. The aim of this paper is to demonstrate the viability of soft set for audio signal classification. A dataset of 104 (single monophonic notes pieces of Traditional Pakistani musical instruments were designed. Feature extraction is done using two feature sets namely perception based and mel-frequency cepstral coefficients (MFCCs. In a while, two different classification techniques are applied for classification task, which are soft set (comparison table and fuzzy soft set (similarity measurement. Experimental results show that both classifiers can perform well on numerical data. However, soft set achieved accuracy up to 94.26% with best generated dataset. Consequently, these promising results provide new possibilities for soft set in classifying musical instrument sounds. Based on the analysis of the results, this study offers a new view on automatic instrument classification

  18. 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

  19. 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

  20. 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 ...

  1. 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...

  2. Contribution to Application of the Automatic Classification of Seismological Signals

    Czech Academy of Sciences Publication Activity Database

    Kaláb, Zdeněk; Častová, N.; Lyubushin, A. A.

    Ostrava : ÚGN AV ČR, 2005 - (Kožušníková, A.), s. 48-57 ISBN 80-86407-09-8 R&D Projects: GA AV ČR(CZ) KSK3012103 Institutional research plan: CEZ:AV0Z30860518 Keywords : seismological signals * wavelets theory Subject RIV: DC - Siesmology, Volcanology, Earth Structure

  3. 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.

  4. 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...

  5. 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

  6. Classification and knowledge

    Science.gov (United States)

    Kurtz, Michael J.

    1989-01-01

    Automated procedures to classify objects are discussed. The classification problem is reviewed, and the relation of epistemology and classification is considered. The classification of stellar spectra and of resolved images of galaxies is addressed.

  7. Hazard classification methodology

    International Nuclear Information System (INIS)

    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  8. 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.

  9. Classification of sports types from tracklets

    DEFF Research Database (Denmark)

    Gade, Rikke; Moeslund, Thomas B.

    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 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 %....

  10. On the recognition of compromise in sensing systems: rewired acoustic arrays and distorted route estimation and classification

    Science.gov (United States)

    Thornley, David J.; Damarla, Thyagaraju; Srivastava, Mani B.; Mylaraswami, Dinkar

    2009-09-01

    A group of acoustic arrays that provide direction of approach estimates also support classification of vehicles using the beams formed during that estimation. Successful simultaneous tracking and classification has demonstrated the value of such a sensing resource as a UGS installation. We now consider potential attacks on the integrity of such an installation, describing the effect of compromised acoustic arrays in the data analysis and tracking and classification results. We indicate how these can be automatically recognized, and note that calibration methods intended for deployment time can be used for recovery during operation, which opens the door to methods for recovery from the compromise without re-configuring the equipment, using abductive reasoning to discover the necessary re-processing structure. By rotating an acoustic array, the tracking stability and implied path of a tracked entity can be distorted while leaving the data and analysis from individual arrays self-consistent. Less structured modifications, such as unstructured re-ordering of microphone connections, impact the basic data analysis. We examine the effect of these classes of attack on the integrity of a set of unattended acoustic arrays, and consider the steps necessary for detection, diagnosis, and recovering an effective sensing system. Understaning these steps plays an important part in reasoning in support of balance of investment, planning, operation and post-hoc analysis.

  11. A multi-attribute based methodology for vehicle detection and identification

    Science.gov (United States)

    Elangovan, Vinayak; Alsaidi, Bashir; Shirkhodaie, Amir

    2013-05-01

    Robust vehicle detection and identification is required for the intelligent persistent surveillance systems. In this paper, we present a Multi-attribute Vehicle Detection and Identification technique (MVDI) for detection and classification of stationary vehicles. The proposed model uses a supervised Hamming Neural Network (HNN) for taxonomy of shape of the vehicle. Vehicles silhouette features are employed for the training of the HNN from a large array of training vehicle samples in different type, scale, and color variation. Invariant vehicle silhouette attributes are used as features for training of the HNN which is based on an internal Hamming Distance and shape features to determine degree of similarity of a test vehicle against those it's selectively trained with. Upon detection of class of the vehicle, the other vehicle attributes such as: color and orientation are determined. For vehicle color detection, provincial regions of the vehicle body are used for matching color of the vehicle. For the vehicle orientation detection, the key structural features of the vehicle are extracted and subjected to classification based on color tune, geometrical shape, and tire region detection. The experimental results show the technique is promising and has robustness for detection and identification of vehicle based on their multi-attribute features. Furthermore this paper demonstrates the importance of the vehicle attributes detection towards the identification of Human-Vehicle Interaction events.

  12. A prototype for classification of classical music using neural networks

    OpenAIRE

    Malheiro, Ricardo; Paiva, R. P.; Mendes, A. J.; Mendes, T.; A. Cardoso

    2004-01-01

    As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth an...

  13. Web Content Classification with Topic and Sentiment Analysis

    OpenAIRE

    Liu, Shuhua; Forss, Thomas; Bjork, Kaj-Mikael

    2014-01-01

    Automatic classification of web content has been studied extensively, using different learning methods and tools, investigating different datasets to serve different purposes. Most of the studies have made use of content and structural features of web pages. In this study we present a new approach for automatically classifying web pages into pre-defined topic categories. We apply text summarization and sentiment analysis techniques to extract topic and sentiment indicators of web pages. We th...

  14. Automatic Dance Lesson Generation

    Science.gov (United States)

    Yang, Yang; Leung, H.; Yue, Lihua; Deng, LiQun

    2012-01-01

    In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation…

  15. 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...

  16. Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals.

    Science.gov (United States)

    Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young

    2014-01-01

    Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. PMID:25264954

  17. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

    Directory of Open Access Journals (Sweden)

    Boon-Giin Lee

    2014-09-01

    Full Text Available Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.

  18. Malware Classification based on Call Graph Clustering

    OpenAIRE

    Kinable, Joris; Kostakis, Orestis

    2010-01-01

    Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations...

  19. 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...

  20. On the relevance of spectral features for instrument classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai;

    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 different models about the spectral characterization of musical instruments. The first assumes a constant envelope of...

  1. Classification of welding defects in metals using artificial neural network

    International Nuclear Information System (INIS)

    This paper discusses the automatic recognition of the return signal with metal welding defects such as cracks, slag and porosity. Normal samples are used as reference benchmarks. A total of 12 features were used to characterize the types of damages. Classification process is done by using feed forward artificial neural network back propagation. The process of acquisition and data processing were carried out fully automatically. There are artificial neural classification processes using MATLAB software has been successfully undertaken in which the system can identify defects that are owned by more than 90% accuracy. (author)

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  6. Machine Learning Methods for Spam E-Mail Classification

    OpenAIRE

    W. A. Awad; S. M. Elseuofi

    2011-01-01

    The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in avery successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are ...

  7. KACST Arabic Text Classification Project: Overview and Preliminary Results

    OpenAIRE

    Althubaity, A.; Almuhareb, A.; Alharbi, S.; Al-Rajeh, A.; Khorsheed , M.

    2008-01-01

    Electronically formatted Arabic free-texts can be found in abundance these days on the World Wide Web, often linked to commercial enterprises and/or government organizations. Vast tracts of knowledge and relations lie hidden within these texts, knowledge that can be exploited once the correct intelligent tools have been identified and applied. For example, text mining may help with text classification and categorization. Text classification aims to automatically assign text to a predefined ca...

  8. 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.

  9. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

    Science.gov (United States)

    Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

    2015-04-01

    Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

  10. 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.

  11. Intelligent Fatigue Detection and Automatic Vehicle Control System

    OpenAIRE

    Monali Gulhane; P.S.Mohod

    2014-01-01

    This paper describes method for detecting the early signs of fatigue in train drivers. As soon as the train driver is falling in symptoms of fatigue immediate message will be transfer to the control room indicating the status of the drivers. In addition of the advance technology of heart rate sensors is also added in the system for correct detection of status of driver if in either case driver is falling to fatigue due to any sever medical problems .The fatigue is detected in the system by th...

  12. INTELLIGENT FATIGUE DETECTION AND AUTOMATIC VEHICLE CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    Monali Gulhane

    2014-10-01

    Full Text Available This paper describes method for detecting the early signs of fatigue in train drivers. As soon as the train driver is falling in symptoms of fatigue immediate message will be transfer to the control room indicating the status of the drivers. In addition of the advance technology of heart rate sensors is also added in the system for correct detection of status of driver if in either case driver is falling to fatigue due to any sever medical problems .The fatigue is detected in the system by the image processing method of comparing the image(frames in the video and by using the human features we are able to estimate the indirect way of detecting fatigue. The technique also focuses on modes of person when driving the train i.e. awake, drowsy state or sleepy and sleep state. The system is very efficient to detect the fatigue and control the train also train can be controlled if it cross any such signal by which the train may collide on another train

  13. Measuring Service Reliability Using Automatic Vehicle Location Data

    OpenAIRE

    2014-01-01

    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 ...

  14. 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...

  15. The automatic NMR gaussmeter

    International Nuclear Information System (INIS)

    The paper describes the automatic gaussmeter operating according to the principle of nuclear magnetic resonance. There have been discussed the operating principle, the block diagram and operating parameters of the meter. It can be applied to measurements of induction in electromagnets of wide-line radio-spectrometers EPR and NMR and in calibration stands of magnetic induction values. Frequency range of an autodyne oscillator from 0,6 up to 86 MHz for protons is corresponding to the field range from 0.016 up to 2T. Applicaton of other nuclei, such as 7Li and 2D is also foreseen. The induction measurement is carried over automatically, and the NMR signal and value of measured induction are displayed on a monitor screen. (author)

  16. 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.

  17. Automatic Wall Painting Robot

    OpenAIRE

    P.KEERTHANAA, K.JEEVITHA, V.NAVINA, G.INDIRA, S.JAYAMANI

    2013-01-01

    The Primary Aim Of The Project Is To Design, Develop And Implement Automatic Wall Painting Robot Which Helps To Achieve Low Cost Painting Equipment. Despite The Advances In Robotics And Its Wide Spreading Applications, Interior Wall Painting Has Shared Little In Research Activities. The Painting Chemicals Can Cause Hazards To The Human Painters Such As Eye And Respiratory System Problems. Also The Nature Of Painting Procedure That Requires Repeated Work And Hand Rising Makes It Boring, Time A...

  18. 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...

  19. Automatic Inductive Programming Tutorial

    OpenAIRE

    Aler, Ricardo

    2006-01-01

    Computers that can program themselves is an old dream of Artificial Intelligence, but only nowadays there is some progress of remark. In relation to Machine Learning, a computer program is the most powerful structure that can be learned, pushing the final goal well beyond neural networks or decision trees. There are currently many separate areas, working independently, related to automatic programming, both deductive and inductive. The first goal of this tutorial is to give to the attendants ...

  20. 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....

  1. 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...

  2. Automaticity or active control

    DEFF Research Database (Denmark)

    Tudoran, Ana Alina; Olsen, Svein Ottar

    This study addresses the quasi-moderating role of habit strength in explaining action loyalty. A model of loyalty behaviour is proposed that extends the traditional satisfaction–intention–action loyalty network. Habit strength is conceptualised as a cognitive construct to refer to the psychologic......, respectively, between intended loyalty and action loyalty. At high levels of habit strength, consumers are more likely to free up cognitive resources and incline the balance from controlled to routine and automatic-like responses....

  3. Automatic digital image registration

    Science.gov (United States)

    Goshtasby, A.; Jain, A. K.; Enslin, W. R.

    1982-01-01

    This paper introduces a general procedure for automatic registration of two images which may have translational, rotational, and scaling differences. This procedure involves (1) segmentation of the images, (2) isolation of dominant objects from the images, (3) determination of corresponding objects in the two images, and (4) estimation of transformation parameters using the center of gravities of objects as control points. An example is given which uses this technique to register two images which have translational, rotational, and scaling differences.

  4. Automatic topic segmentation and labeling in multiparty dialogue

    OpenAIRE

    Hsueh, Pei-Yun; Moore, Johanna D.

    2006-01-01

    This study concerns how to segment a scenario-driven multiparty dialogue and how to label these segments automatically. We apply approaches that have been proposed for identifying topic boundaries at a coarser level to the problem of identifying agenda-based topic boundaries in scenario-based meetings. We also develop conditional models to classify segments into topic classes. Experiments in topic segmentation show that a supervised classification approach that combines lexical and conversati...

  5. Automatic Generation of Thematically Focused Information Portals from Web Data

    OpenAIRE

    Sizov, Sergej

    2005-01-01

    Finding the desired information on the Web is often a hard and time-consuming task. This thesis presents the methodology of automatic generation of thematically focused portals from Web data. The key component of the proposed Web retrieval framework is the thematically focused Web crawler that is interested only in a specific, typically small, set of topics. The focused crawler uses classification methods for filtering of fetched documents and identifying most likely relevant Web source...

  6. 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.

  7. 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.

  8. 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.

  9. 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...

  10. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles

    Directory of Open Access Journals (Sweden)

    Linhuan Zhang

    2016-04-01

    Full Text Available 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.

  11. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles.

    Science.gov (United States)

    Zhang, Linhuan; Ahamed, Tofael; Zhang, Yan; Gao, Pengbo; Takigawa, Tomohiro

    2016-01-01

    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. PMID:27110793

  12. Cosmeceutical vehicles.

    Science.gov (United States)

    Epstein, Howard

    2009-01-01

    Consumers will pay a premium for high-performance skin and hair care products. The demand exists, and in return for the high cost, consumers expect the product to perform as claimed and to meet aesthetic standards beyond many products found in the mass market. To be successful in this highly competitive market, products must function as claimed or consumers will not repurchase. Effective contemporary high-end products must be properly formulated in nonirritating vehicles that consumers will perceive as elegant. PMID:19695476

  13. Experiments in Image Segmentation for Automatic US License Plate Recognition

    OpenAIRE

    Diaz Acosta, Beatriz

    2004-01-01

    License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially availab...

  14. 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...

  15. Visual Vehicle Identification Using Modern Smart Glasses

    OpenAIRE

    Malmgren, Andreas

    2015-01-01

    In recent years wearable devices have been advancing at a rapid pace and one of the largest growing segments is the smart glass segment. In this thesis the feasibility of today’s ARM-based smart glasses are evaluated for automatic license plate recognition (ALPR). The license plate is by far the most prominent visual feature to identify a spe- cific vehicle, and exists on both old and newly produced vehicles. This thesis propose an ALPR system based on a sequence of vertical edge detection, a...

  16. 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...

  17. Automatic radioactive waste recycling

    International Nuclear Information System (INIS)

    The production of a plutonium ingot by calcium reduction process at CEA/Valduc generates a residue called 'slag'. This article introduces the recycling unit which is dedicated to the treatment of slags. The aim is to separate and to recycle the plutonium trapped in this bulk on the one hand, and to generate a disposable waste from the slag on the other hand. After a general introduction of the facilities, some elements will be enlightened, particularly the dissolution step, the filtration and the drying equipment. Reflections upon technological constraints will be proposed, and the benefits of a fully automatic recycling unit of nuclear waste will also be stressed. (authors)

  18. 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.

  19. Morphological classification of nanoceramic aggregates

    Science.gov (United States)

    Crosta, Giovanni F.; Kang, Bongwoo; Ospina, Carolina; Sung, Changmo

    2005-01-01

    Aluminum silicate nanoaggregates grown at near-room temperature on an organic template under a variety of experimental conditions have been imaged by transmission electron microscopy. Images have been automatically classified by an algorithm based on "spectrum enhancement", multivariate statistics and supervised optimization. Spectrum enhancement consists of subtracting, in the log scale, a known function of wavenumber from the angle averaged power spectral density of the image. Enhanced spectra of each image, after polynomial interpolation, have been regarded as morphological descriptors and as such submitted to principal components analysis nested with a multiobjective parameter optimization algorithm. The latter has maximized pairwise discrimination between classes of materials. The role of the organic template and of a reaction parameter on aggregate morphology has been assessed at two magnification scales. Classification results have also been related to crystal structure data derived from selected area electron diffraction patterns.

  20. Second language writing classification system based on word-alignment distribution

    OpenAIRE

    Katsunori Kotani; Takehiko Yoshimi

    2010-01-01

    The present paper introduces an automatic classification system for assisting second language(L2) writing evaluation. This system, which classifies sentences written by L2 learners as eithernative speaker-like or learner-like sentences, is constructed by machine learning algorithmsusing word-alignment distributions as classification features for detecting word-bywordtranslated expressions. The experimental results demonstrated that our classificationsystem provided adequate classification res...