WorldWideScience

Sample records for recognition selspot system

  1. Touchless palmprint recognition systems

    CERN Document Server

    Genovese, Angelo; Scotti, Fabio

    2014-01-01

    This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems.It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Const

  2. Cognitive object recognition system (CORS)

    Science.gov (United States)

    Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

    2010-04-01

    We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

  3. An audiovisual emotion recognition system

    Science.gov (United States)

    Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

    2007-12-01

    Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

  4. Automated road marking recognition system

    Science.gov (United States)

    Ziyatdinov, R. R.; Shigabiev, R. R.; Talipov, D. N.

    2017-09-01

    Development of the automated road marking recognition systems in existing and future vehicles control systems is an urgent task. One way to implement such systems is the use of neural networks. To test the possibility of using neural network software has been developed with the use of a single-layer perceptron. The resulting system based on neural network has successfully coped with the task both when driving in the daytime and at night.

  5. The Army word recognition system

    Science.gov (United States)

    Hadden, David R.; Haratz, David

    1977-01-01

    The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

  6. Combat Systems Department Employee Recognition System

    National Research Council Canada - National Science Library

    1996-01-01

    This handbook contains two types of information: guidelines and instructions. The guidelines provide a foundation of purpose, assumptions, principles, expectations and attributes the Employee Recognition System is designed to reflect...

  7. System of breast cancer recognition

    International Nuclear Information System (INIS)

    Rozhkova, N.I.

    1984-01-01

    The paper is concerned with the resUlts of the multimodality system of breast cancer recognition using methods, of clinical X-ray and cytological examinations. Altogether 1671 women were examined; breast cancer was detected in 165. Stage 1 was detected in 63 patients, Stage 2 in 34, Stage 3 in 34, and Stage 4 in 8. In 7% of the cases, tumors were inpalpable and could be detected by X-ray only. In 9.9% of the cases, the multicentric nature of tumor growth was established. In 71% tumors had a mixed histological structure. The system of breast cancer recognition provided for accurate diagnosis in 98% of the cases making it possible to avoid surgical intervention in 38%. Good diagnostic results are possible under conditions of a special mammology unit where a roentgenologist working in a close contact with surgeonns working in a close contact with surgeos and morphologists, performs the first stages of diagnosis beginning from clinical examination up to special methods that require X-ray control (paracentesis, ductography, pneumocystography, preoperative marking of the breast and marking of the remote sectors of the breast)

  8. SURVEY OF BIOMETRIC SYSTEMS USING IRIS RECOGNITION

    OpenAIRE

    S.PON SANGEETHA; DR.M.KARNAN

    2014-01-01

    The security plays an important role in any type of organization in today’s life. Iris recognition is one of the leading automatic biometric systems in the area of security which is used to identify the individual person. Biometric systems include fingerprints, facial features, voice recognition, hand geometry, handwriting, the eye retina and the most secured one presented in this paper, the iris recognition. Biometric systems has become very famous in security systems because it is not possi...

  9. System for automatic crate recognition

    Directory of Open Access Journals (Sweden)

    Radovan Kukla

    2012-01-01

    Full Text Available This contribution describes usage of computer vision and artificial intelligence methods for application. The method solves abuse of reverse vending machine. This topic has been solved as innovation voucher for the South Moravian Region. It was developed by Mendel university in Brno (Department of informatics – Faculty of Business and Economics and Department of Agricultural, Food and Environmental Engineering – Faculty of Agronomy together with the Czech subsidiary of Tomra. The project is focused on a possibility of integration industrial cameras and computers to process recognition of crates in the verse vending machine. The aim was the effective security system that will be able to save hundreds-thousands financial loss. As suitable development and runtime platform there was chosen product ControlWeb and VisionLab developed by Moravian Instruments Inc.

  10. Facial recognition in education system

    Science.gov (United States)

    Krithika, L. B.; Venkatesh, K.; Rathore, S.; Kumar, M. Harish

    2017-11-01

    Human beings exploit emotions comprehensively for conveying messages and their resolution. Emotion detection and face recognition can provide an interface between the individuals and technologies. The most successful applications of recognition analysis are recognition of faces. Many different techniques have been used to recognize the facial expressions and emotion detection handle varying poses. In this paper, we approach an efficient method to recognize the facial expressions to track face points and distances. This can automatically identify observer face movements and face expression in image. This can capture different aspects of emotion and facial expressions.

  11. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Directory of Open Access Journals (Sweden)

    Muhammad Hameed Siddiqi

    2013-12-01

    Full Text Available Over the last decade, human facial expressions recognition (FER has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.

  12. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Science.gov (United States)

    Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

    2013-01-01

    Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

  13. Improved pattern recognition systems by hybrid methods

    International Nuclear Information System (INIS)

    Duerr, B.; Haettich, W.; Tropf, H.; Winkler, G.; Fraunhofer-Gesellschaft zur Foerderung der Angewandten Forschung e.V., Karlsruhe

    1978-12-01

    This report describes a combination of statistical and syntactical pattern recongition methods. The hierarchically structured recognition system consists of a conventional statistical classifier, a structural classifier analysing the topological composition of the patterns, a stage reducing the number of hypotheses made by the first two stages, and a mixed stage based on a search for maximum similarity between syntactically generated prototypes and patterns. The stages work on different principles to avoid mistakes made in one stage in the other stages. This concept is applied to the recognition of numerals written without constraints. If no samples are rejected, a recognition rate of 99,5% is obtained. (orig.) [de

  14. Biometric Features in Person Recognition Systems

    Directory of Open Access Journals (Sweden)

    Edgaras Ivanovas

    2011-03-01

    Full Text Available Lately a lot of research effort is devoted for recognition of a human being using his biometric characteristics. Biometric recognition systems are used in various applications, e. g., identification for state border crossing or firearm, which allows only enrolled persons to use it. In this paper biometric characteristics and their properties are reviewed. Development of high accuracy system requires distinctive and permanent characteristics, whereas development of user friendly system requires collectable and acceptable characteristics. It is showed that properties of biometric characteristics do not influence research effort significantly. Properties of biometric characteristic features and their influence are discussed.Article in Lithuanian

  15. Automated pattern recognition system for noise analysis

    International Nuclear Information System (INIS)

    Sides, W.H. Jr.; Piety, K.R.

    1980-01-01

    A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition

  16. Recognition of boundary feedback systems

    DEFF Research Database (Denmark)

    Pedersen, Michael

    1989-01-01

    A system that has been the object of intense research is outlined. In view of that and recent progress of the theory of pseudodifferential boundary operator calculus, the author describes some features that could prove to be interesting in connection with the problems of boundary feedback stabili...... stabilizability. It is shown that it is possible to use the calculus to consider more general feedback systems in a variational setup.......A system that has been the object of intense research is outlined. In view of that and recent progress of the theory of pseudodifferential boundary operator calculus, the author describes some features that could prove to be interesting in connection with the problems of boundary feedback...

  17. Cross domains Arabic named entity recognition system

    Science.gov (United States)

    Al-Ahmari, S. Saad; Abdullatif Al-Johar, B.

    2016-07-01

    Named Entity Recognition (NER) plays an important role in many Natural Language Processing (NLP) applications such as; Information Extraction (IE), Question Answering (QA), Text Clustering, Text Summarization and Word Sense Disambiguation. This paper presents the development and implementation of domain independent system to recognize three types of Arabic named entities. The system works based on a set of domain independent grammar-rules along with Arabic part of speech tagger in addition to gazetteers and lists of trigger words. The experimental results shown, that the system performed as good as other systems with better results in some cases of cross-domains corpora.

  18. Privacy protection schemes for fingerprint recognition systems

    Science.gov (United States)

    Marasco, Emanuela; Cukic, Bojan

    2015-05-01

    The deployment of fingerprint recognition systems has always raised concerns related to personal privacy. A fingerprint is permanently associated with an individual and, generally, it cannot be reset if compromised in one application. Given that fingerprints are not a secret, potential misuses besides personal recognition represent privacy threats and may lead to public distrust. Privacy mechanisms control access to personal information and limit the likelihood of intrusions. In this paper, image- and feature-level schemes for privacy protection in fingerprint recognition systems are reviewed. Storing only key features of a biometric signature can reduce the likelihood of biometric data being used for unintended purposes. In biometric cryptosystems and biometric-based key release, the biometric component verifies the identity of the user, while the cryptographic key protects the communication channel. Transformation-based approaches only a transformed version of the original biometric signature is stored. Different applications can use different transforms. Matching is performed in the transformed domain which enable the preservation of low error rates. Since such templates do not reveal information about individuals, they are referred to as cancelable templates. A compromised template can be re-issued using a different transform. At image-level, de-identification schemes can remove identifiers disclosed for objectives unrelated to the original purpose, while permitting other authorized uses of personal information. Fingerprint images can be de-identified by, for example, mixing fingerprints or removing gender signature. In both cases, degradation of matching performance is minimized.

  19. Euro Banknote Recognition System for Blind People.

    Science.gov (United States)

    Dunai Dunai, Larisa; Chillarón Pérez, Mónica; Peris-Fajarnés, Guillermo; Lengua Lengua, Ismael

    2017-01-20

    This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.

  20. Euro Banknote Recognition System for Blind People

    Directory of Open Access Journals (Sweden)

    Larisa Dunai Dunai

    2017-01-01

    Full Text Available This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.

  1. Rotation-invariant neural pattern recognition system with application to coin recognition.

    Science.gov (United States)

    Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

    1992-01-01

    In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

  2. Device-Free Indoor Activity Recognition System

    Directory of Open Access Journals (Sweden)

    Mohammed Abdulaziz Aide Al-qaness

    2016-11-01

    Full Text Available In this paper, we explore the properties of the Channel State Information (CSI of WiFi signals and present a device-free indoor activity recognition system. Our proposed system uses only one ubiquitous router access point and a laptop as a detection point, while the user is free and neither needs to wear sensors nor carry devices. The proposed system recognizes six daily activities, such as walk, crawl, fall, stand, sit, and lie. We have built the prototype with an effective feature extraction method and a fast classification algorithm. The proposed system has been evaluated in a real and complex environment in both line-of-sight (LOS and none-line-of-sight (NLOS scenarios, and the results validate the performance of the proposed system.

  3. A Massively Parallel Face Recognition System

    Directory of Open Access Journals (Sweden)

    Lahdenoja Olli

    2007-01-01

    Full Text Available We present methods for processing the LBPs (local binary patterns with a massively parallel hardware, especially with CNN-UM (cellular nonlinear network-universal machine. In particular, we present a framework for implementing a massively parallel face recognition system, including a dedicated highly accurate algorithm suitable for various types of platforms (e.g., CNN-UM and digital FPGA. We study in detail a dedicated mixed-mode implementation of the algorithm and estimate its implementation cost in the view of its performance and accuracy restrictions.

  4. Edge detection techniques for iris recognition system

    International Nuclear Information System (INIS)

    Tania, U T; Motakabber, S M A; Ibrahimy, M I

    2013-01-01

    Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate

  5. A Massively Parallel Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ari Paasio

    2006-12-01

    Full Text Available We present methods for processing the LBPs (local binary patterns with a massively parallel hardware, especially with CNN-UM (cellular nonlinear network-universal machine. In particular, we present a framework for implementing a massively parallel face recognition system, including a dedicated highly accurate algorithm suitable for various types of platforms (e.g., CNN-UM and digital FPGA. We study in detail a dedicated mixed-mode implementation of the algorithm and estimate its implementation cost in the view of its performance and accuracy restrictions.

  6. Cross domains Arabic named entity recognition system

    KAUST Repository

    Al-Ahmari, S. Saad

    2016-07-11

    Named Entity Recognition (NER) plays an important role in many Natural Language Processing (NLP) applications such as; Information Extraction (IE), Question Answering (QA), Text Clustering, Text Summarization and Word Sense Disambiguation. This paper presents the development and implementation of domain independent system to recognize three types of Arabic named entities. The system works based on a set of domain independent grammar-rules along with Arabic part of speech tagger in addition to gazetteers and lists of trigger words. The experimental results shown, that the system performed as good as other systems with better results in some cases of cross-domains corpora. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

  7. Cross domains Arabic named entity recognition system

    KAUST Repository

    Al-Ahmari, S. Saad; Abdullatif Al-Johar, B.

    2016-01-01

    Named Entity Recognition (NER) plays an important role in many Natural Language Processing (NLP) applications such as; Information Extraction (IE), Question Answering (QA), Text Clustering, Text Summarization and Word Sense Disambiguation. This paper presents the development and implementation of domain independent system to recognize three types of Arabic named entities. The system works based on a set of domain independent grammar-rules along with Arabic part of speech tagger in addition to gazetteers and lists of trigger words. The experimental results shown, that the system performed as good as other systems with better results in some cases of cross-domains corpora. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

  8. Automatic TLI recognition system. Part 1: System description

    Energy Technology Data Exchange (ETDEWEB)

    Partin, J.K.; Lassahn, G.D.; Davidson, J.R.

    1994-05-01

    This report describes an automatic target recognition system for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system uses image data fusion and gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. This volume gives a general description of the ATR system.

  9. Non Audio-Video gesture recognition system

    DEFF Research Database (Denmark)

    Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis

    2016-01-01

    Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...

  10. Research on Face Recognition Based on Embedded System

    Directory of Open Access Journals (Sweden)

    Hong Zhao

    2013-01-01

    Full Text Available Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

  11. Performance Assessment of Dynaspeak Speech Recognition System on Inflight Databases

    National Research Council Canada - National Science Library

    Barry, Timothy

    2004-01-01

    .... To aid in the assessment of various commercially available speech recognition systems, several aircraft speech databases have been developed at the Air Force Research Laboratory's Human Effectiveness Directorate...

  12. Developing a Credit Recognition System for Chinese Higher Education Institutions

    Science.gov (United States)

    Li, Fuhui

    2015-01-01

    In recent years, a credit recognition system has been developing in Chinese higher education institutions. Much research has been done on this development, but it has been concentrated on system building, barriers/issues and international practices. The relationship between credit recognition system reforms and democratisation of higher education…

  13. DEVELOPMENT OF HOLE RECOGNITION SYSTEM FROM STEP FILE

    Directory of Open Access Journals (Sweden)

    C. F. Tan

    2017-11-01

    Full Text Available This paper describes the development of Hole Recognition System (HRS for Computer-Aided Process Planning (CAPP using a neutral data format produced by CAD system. The geometrical data of holes is retrieved from STandard for the Exchange of Product model data (STEP. Rule-based algorithm is used during recognising process. Current implementation of feature recognition is limited to simple hole feat ures. Test results are presented to demonstrate the capabilities of the feature recognition algorithm.

  14. Random-Profiles-Based 3D Face Recognition System

    Directory of Open Access Journals (Sweden)

    Joongrock Kim

    2014-03-01

    Full Text Available In this paper, a noble nonintrusive three-dimensional (3D face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

  15. Automated recognition system for power quality disturbances

    Science.gov (United States)

    Abdelgalil, Tarek

    The application of deregulation policies in electric power systems has resulted in the necessity to quantify the quality of electric power. This fact highlights the need for a new monitoring strategy which is capable of tracking, detecting, classifying power quality disturbances, and then identifying the source of the disturbance. The objective of this work is to design an efficient and reliable power quality monitoring strategy that uses the advances in signal processing and pattern recognition to overcome the deficiencies that exist in power quality monitoring devices. The purposed monitoring strategy has two stages. The first stage is to detect, track, and classify any power quality violation by the use of on-line measurements. In the second stage, the source of the classified power quality disturbance must be identified. In the first stage, an adaptive linear combiner is used to detect power quality disturbances. Then, the Teager Energy Operator and Hilbert Transform are utilized for power quality event tracking. After the Fourier, Wavelet, and Walsh Transforms are employed for the feature extraction, two approaches are then exploited to classify the different power quality disturbances. The first approach depends on comparing the disturbance to be classified with a stored set of signatures for different power quality disturbances. The comparison is developed by using Hidden Markov Models and Dynamic Time Warping. The second approach depends on employing an inductive inference to generate the classification rules directly from the data. In the second stage of the new monitoring strategy, only the problem of identifying the location of the switched capacitor which initiates the transients is investigated. The Total Least Square-Estimation of Signal Parameters via Rotational Invariance Technique is adopted to estimate the amplitudes and frequencies of the various modes contained in the voltage signal measured at the facility entrance. After extracting the

  16. Hybrid gesture recognition system for short-range use

    Science.gov (United States)

    Minagawa, Akihiro; Fan, Wei; Katsuyama, Yutaka; Takebe, Hiroaki; Ozawa, Noriaki; Hotta, Yoshinobu; Sun, Jun

    2012-03-01

    In recent years, various gesture recognition systems have been studied for use in television and video games[1]. In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is unknown to the user during operation, which imposes several restrictions on his/her actions. To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and motion analysis using image frame differences (motion-based approach)(for example, see[4]). We evaluated this system using sample users and classified the resulting errors into three categories: errors that depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture recognition systems.

  17. Fingerprint recognition system by use of graph matching

    Science.gov (United States)

    Shen, Wei; Shen, Jun; Zheng, Huicheng

    2001-09-01

    Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

  18. Embedded palmprint recognition system using OMAP 3530.

    Science.gov (United States)

    Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

    2012-01-01

    We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the central pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.

  19. Iris analysis for biometric recognition systems

    CERN Document Server

    Bodade, Rajesh M

    2014-01-01

    The book presents three most significant areas in Biometrics and Pattern Recognition. A step-by-step approach for design and implementation of Dual Tree Complex Wavelet Transform (DTCWT) plus Rotated Complex Wavelet Filters (RCWF) is discussed in detail. In addition to the above, the book provides detailed analysis of iris images and two methods of iris segmentation. It also discusses simplified study of some subspace-based methods and distance measures for iris recognition backed by empirical studies and statistical success verifications.

  20. Image quality assessment for video stream recognition systems

    Science.gov (United States)

    Chernov, Timofey S.; Razumnuy, Nikita P.; Kozharinov, Alexander S.; Nikolaev, Dmitry P.; Arlazarov, Vladimir V.

    2018-04-01

    Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

  1. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

    Science.gov (United States)

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  2. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2015-01-01

    Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  3. Increased Efficiency of Face Recognition System using Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Rajani Muraleedharan

    2006-02-01

    Full Text Available This research was inspired by the need of a flexible and cost effective biometric security system. The flexibility of the wireless sensor network makes it a natural choice for data transmission. Swarm intelligence (SI is used to optimize routing in distributed time varying network. In this paper, SI maintains the required bit error rate (BER for varied channel conditions while consuming minimal energy. A specific biometric, the face recognition system, is discussed as an example. Simulation shows that the wireless sensor network is efficient in energy consumption while keeping the transmission accuracy, and the wireless face recognition system is competitive to the traditional wired face recognition system in classification accuracy.

  4. An Evaluation of PC-Based Optical Character Recognition Systems.

    Science.gov (United States)

    Schreier, E. M.; Uslan, M. M.

    1991-01-01

    The review examines six personal computer-based optical character recognition (OCR) systems designed for use by blind and visually impaired people. Considered are OCR components and terms, documentation, scanning and reading, command structure, conversion, unique features, accuracy of recognition, scanning time, speed, and cost. (DB)

  5. UNCONSTRAINED HANDWRITING RECOGNITION : LANGUAGE MODELS, PERPLEXITY, AND SYSTEM PERFORMANCE

    NARCIS (Netherlands)

    Marti, U-V.; Bunke, H.

    2004-01-01

    In this paper we present a number of language models and their behavior in the recognition of unconstrained handwritten English sentences. We use the perplexity to compare the different models and their prediction power, and relate it to the performance of a recognition system under different

  6. 8th International Conference on Computer Recognition Systems

    CERN Document Server

    Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał; Zolnierek, Andrzej

    2013-01-01

    The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Biometrics Data Stream Classification and Big Data Analytics  Features, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications Pattern recognition and image processing in robotics  Speech and word recognition This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

  7. A Spoken English Recognition Expert System.

    Science.gov (United States)

    1983-09-01

    34Speech Recognition by Computer," Scientific American. New York: Scientific American, April 1981: 64-76. 16. Marcus, Mitchell P. A Theo of Syntactic...prob)...) Pcssible words for voice decoder to choose from are: gents dishes issues itches ewes folks foes comunications units eunichs error * farce

  8. Active Multimodal Sensor System for Target Recognition and Tracking.

    Science.gov (United States)

    Qu, Yufu; Zhang, Guirong; Zou, Zhaofan; Liu, Ziyue; Mao, Jiansen

    2017-06-28

    High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system.

  9. A novel handwritten character recognition system using gradient ...

    Indian Academy of Sciences (India)

    The issues faced by the handwritten character recognition systems are the similarity. ∗ ... tical/structural features have also been successfully used in character ..... The coordinates (xc, yc) of centroid are calculated by equations (4) and (5). xc =.

  10. Optimization Methods in Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    L. Povoda

    2016-09-01

    Full Text Available Emotions play big role in our everyday communication and contain important information. This work describes a novel method of automatic emotion recognition from textual data. The method is based on well-known data mining techniques, novel approach based on parallel run of SVM (Support Vector Machine classifiers, text preprocessing and 3 optimization methods: sequential elimination of attributes, parameter optimization based on token groups, and method of extending train data sets during practical testing and production release final tuning. We outperformed current state of the art methods and the results were validated on bigger data sets (3346 manually labelled samples which is less prone to overfitting when compared to related works. The accuracy achieved in this work is 86.89% for recognition of 5 emotional classes. The experiments were performed in the real world helpdesk environment, was processing Czech language but the proposed methodology is general and can be applied to many different languages.

  11. Automatic system for localization and recognition of vehicle plate numbers

    OpenAIRE

    Vázquez, N.; Nakano, M.; Pérez-Meana, H.

    2003-01-01

    This paper proposes a vehicle numbers plate identification system, which extracts the characters features of a plate from a captured image by a digital camera. Then identify the symbols of the number plate using a multilayer neural network. The proposed recognition system consists of two processes: The training process and the recognition process. During the training process, a database is created using 310 vehicular plate images. Then using this database a multilayer neural network is traine...

  12. Recognition

    DEFF Research Database (Denmark)

    Gimmler, Antje

    2017-01-01

    In this article, I shall examine the cognitive, heuristic and theoretical functions of the concept of recognition. To evaluate both the explanatory power and the limitations of a sociological concept, the theory construction must be analysed and its actual productivity for sociological theory mus...

  13. Automatic Number Plate Recognition System for IPhone Devices

    Directory of Open Access Journals (Sweden)

    Călin Enăchescu

    2013-06-01

    Full Text Available This paper presents a system for automatic number plate recognition, implemented for devices running the iOS operating system. The methods used for number plate recognition are based on existing methods, but optimized for devices with low hardware resources. To solve the task of automatic number plate recognition we have divided it into the following subtasks: image acquisition, localization of the number plate position on the image and character detection. The first subtask is performed by the camera of an iPhone, the second one is done using image pre-processing methods and template matching. For the character recognition we are using a feed-forward artificial neural network. Each of these methods is presented along with its results.

  14. The NA50 segmented target and vertex recognition system

    International Nuclear Information System (INIS)

    Bellaiche, F.; Cheynis, B.; Contardo, D.; Drapier, O.; Grossiord, J.Y.; Guichard, A.; Haroutunian, R.; Jacquin, M.; Ohlsson-Malek, F.; Pizzi, J.R.

    1997-01-01

    The NA50 segmented target and vertex recognition system is described. The segmented target consists of 7 sub-targets of 1-2 mm thickness. The vertex recognition system used to determine the sub-target where an interaction has occured is based upon quartz elements which produce Cerenkov light when traversed by charged particles from the interaction. The geometrical arrangement of the quartz elements has been optimized for vertex recognition in 208 Pb-Pb collisions at 158 GeV/nucleon. A simple algorithm provides a vertex recognition efficiency of better than 85% for dimuon trigger events collected with a 1 mm sub-target set-up. A method for recognizing interactions of projectile fragments (nuclei and/or groups of nucleons) is presented. The segmented target allows a large target thickness which together with a high beam intensity (∼10 7 ions/s) enables high statistics measurements. (orig.)

  15. Optical character recognition systems for different languages with soft computing

    CERN Document Server

    Chaudhuri, Arindam; Badelia, Pratixa; K Ghosh, Soumya

    2017-01-01

    The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.

  16. Adamantane in Drug Delivery Systems and Surface Recognition

    OpenAIRE

    Adela Štimac; Marina Šekutor; Kata Mlinarić-Majerski; Leo Frkanec; Ruža Frkanec

    2017-01-01

    The adamantane moiety is widely applied in design and synthesis of new drug delivery systems and in surface recognition studies. This review focuses on liposomes, cyclodextrins, and dendrimers based on or incorporating adamantane derivatives. Our recent concept of adamantane as an anchor in the lipid bilayer of liposomes has promising applications in the field of targeted drug delivery and surface recognition. The results reported here encourage the development of novel adamantane-based struc...

  17. Enhancement of Iris Recognition System Based on Phase Only Correlation

    Directory of Open Access Journals (Sweden)

    Nuriza Pramita

    2011-08-01

    Full Text Available Iris recognition system is one of biometric based recognition/identification systems. Numerous techniques have been implemented to achieve a good recognition rate, including the ones based on Phase Only Correlation (POC. Significant and higher correlation peaks suggest that the system recognizes iris images of the same subject (person, while lower and unsignificant peaks correspond to recognition of those of difference subjects. Current POC methods have not investigated minimum iris point that can be used to achieve higher correlation peaks. This paper proposed a method that used only one-fourth of full normalized iris size to achieve higher (or at least the same recognition rate. Simulation on CASIA version 1.0 iris image database showed that averaged recognition rate of the proposed method achieved 67%, higher than that of using one-half (56% and full (53% iris point. Furthermore, all (100% POC peak values of the proposed method was higher than that of the method with full iris points.

  18. A Malaysian Vehicle License Plate Localization and Recognition System

    Directory of Open Access Journals (Sweden)

    Ganapathy Velappa

    2008-02-01

    Full Text Available Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.

  19. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    Science.gov (United States)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  20. An artificial odor recognition system is developed for discriminating odors

    Directory of Open Access Journals (Sweden)

    Wisnu Jatmiko

    2002-12-01

    Full Text Available This artificial system consisted of 16 quartz resonator crystals as the sensor array, a frequency modulator and a frequency counter for each sensor that are connected directly to a microcomputer. We have already shown that the artificial odor recognition system with 4 sensors is high enough to discriminate simple odor correctly, however, when it was used to discriminate compound odors, the recognition capability of this system is dropped significantly to be about 40%. Results of experiments show that the developed artificial system with 16 sensors could discriminate compound aroma based on 6 gradient of alcohol concentrations with high recognition rate of 89.9% for non batch processing system, and 82.4% for batch processing of the classes of odors.

  1. Connected digit speech recognition system for Malayalam language

    Indian Academy of Sciences (India)

    A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer for Malayalam language. The system employs Perceptual ...

  2. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  3. A Malaysian Vehicle License Plate Localization and Recognition System

    OpenAIRE

    Ganapathy Velappa; Dennis LUI Wen Lik

    2008-01-01

    Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services,...

  4. Arm Motion Recognition and Exercise Coaching System for Remote Interaction

    Directory of Open Access Journals (Sweden)

    Hong Zeng

    2016-01-01

    Full Text Available Arm motion recognition and its related applications have become a promising human computer interaction modal due to the rapid integration of numerical sensors in modern mobile-phones. We implement a mobile-phone-based arm motion recognition and exercise coaching system that can help people carrying mobile-phones to do body exercising anywhere at any time, especially for the persons that have very limited spare time and are constantly traveling across cities. We first design improved k-means algorithm to cluster the collecting 3-axis acceleration and gyroscope data of person actions into basic motions. A learning method based on Hidden Markov Model is then designed to classify and recognize continuous arm motions of both learners and coaches, which also measures the action similarities between the persons. We implement the system on MIUI 2S mobile-phone and evaluate the system performance and its accuracy of recognition.

  5. Exhibits Recognition System for Combining Online Services and Offline Services

    Science.gov (United States)

    Ma, He; Liu, Jianbo; Zhang, Yuan; Wu, Xiaoyu

    2017-10-01

    In order to achieve a more convenient and accurate digital museum navigation, we have developed a real-time and online-to-offline museum exhibits recognition system using image recognition method based on deep learning. In this paper, the client and server of the system are separated and connected through the HTTP. Firstly, by using the client app in the Android mobile phone, the user can take pictures and upload them to the server. Secondly, the features of the picture are extracted using the deep learning network in the server. With the help of the features, the pictures user uploaded are classified with a well-trained SVM. Finally, the classification results are sent to the client and the detailed exhibition’s introduction corresponding to the classification results are shown in the client app. Experimental results demonstrate that the recognition accuracy is close to 100% and the computing time from the image uploading to the exhibit information show is less than 1S. By means of exhibition image recognition algorithm, our implemented exhibits recognition system can combine online detailed exhibition information to the user in the offline exhibition hall so as to achieve better digital navigation.

  6. Adamantane in Drug Delivery Systems and Surface Recognition.

    Science.gov (United States)

    Štimac, Adela; Šekutor, Marina; Mlinarić-Majerski, Kata; Frkanec, Leo; Frkanec, Ruža

    2017-02-16

    The adamantane moiety is widely applied in design and synthesis of new drug delivery systems and in surface recognition studies. This review focuses on liposomes, cyclodextrins, and dendrimers based on or incorporating adamantane derivatives. Our recent concept of adamantane as an anchor in the lipid bilayer of liposomes has promising applications in the field of targeted drug delivery and surface recognition. The results reported here encourage the development of novel adamantane-based structures and self-assembled supramolecular systems for basic chemical investigations as well as for biomedical application.

  7. Adamantane in Drug Delivery Systems and Surface Recognition

    Directory of Open Access Journals (Sweden)

    Adela Štimac

    2017-02-01

    Full Text Available The adamantane moiety is widely applied in design and synthesis of new drug delivery systems and in surface recognition studies. This review focuses on liposomes, cyclodextrins, and dendrimers based on or incorporating adamantane derivatives. Our recent concept of adamantane as an anchor in the lipid bilayer of liposomes has promising applications in the field of targeted drug delivery and surface recognition. The results reported here encourage the development of novel adamantane-based structures and self-assembled supramolecular systems for basic chemical investigations as well as for biomedical application.

  8. Two Systems for Automatic Music Genre Recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2012-01-01

    We re-implement and test two state-of-the-art systems for automatic music genre classification; but unlike past works in this area, we look closer than ever before at their behavior. First, we look at specific instances where each system consistently applies the same wrong label across multiple...... trials of cross-validation. Second, we test the robustness of each system to spectral equalization. Finally, we test how well human subjects recognize the genres of music excerpts composed by each system to be highly genre representative. Our results suggest that neither high-performing system has...... a capacity to recognize music genre....

  9. 9th International Conference on Computer Recognition Systems

    CERN Document Server

    Jackowski, Konrad; Kurzyński, Marek; Woźniak, Michał; Żołnierek, Andrzej

    2016-01-01

    The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 79 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Features, learning, and classifiers Biometrics Data Stream Classification and Big Data Analytics Image processing and computer vision Medical applications Applications RGB-D perception: recent developments and applications This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.  .

  10. Intelligent Facial Recognition Systems: Technology advancements for security applications

    Energy Technology Data Exchange (ETDEWEB)

    Beer, C.L.

    1993-07-01

    Insider problems such as theft and sabotage can occur within the security and surveillance realm of operations when unauthorized people obtain access to sensitive areas. A possible solution to these problems is a means to identify individuals (not just credentials or badges) in a given sensitive area and provide full time personnel accountability. One approach desirable at Department of Energy facilities for access control and/or personnel identification is an Intelligent Facial Recognition System (IFRS) that is non-invasive to personnel. Automatic facial recognition does not require the active participation of the enrolled subjects, unlike most other biological measurement (biometric) systems (e.g., fingerprint, hand geometry, or eye retinal scan systems). It is this feature that makes an IFRS attractive for applications other than access control such as emergency evacuation verification, screening, and personnel tracking. This paper discusses current technology that shows promising results for DOE and other security applications. A survey of research and development in facial recognition identified several companies and universities that were interested and/or involved in the area. A few advanced prototype systems were also identified. Sandia National Laboratories is currently evaluating facial recognition systems that are in the advanced prototype stage. The initial application for the evaluation is access control in a controlled environment with a constant background and with cooperative subjects. Further evaluations will be conducted in a less controlled environment, which may include a cluttered background and subjects that are not looking towards the camera. The outcome of the evaluations will help identify areas of facial recognition systems that need further development and will help to determine the effectiveness of the current systems for security applications.

  11. A Context Dependent Automatic Target Recognition System

    Science.gov (United States)

    Kim, J. H.; Payton, D. W.; Olin, K. E.; Tseng, D. Y.

    1984-06-01

    This paper describes a new approach to automatic target recognizer (ATR) development utilizing artificial intelligent techniques. The ATR system exploits contextual information in its detection and classification processes to provide a high degree of robustness and adaptability. In the system, knowledge about domain objects and their contextual relationships is encoded in frames, separating it from low level image processing algorithms. This knowledge-based system demonstrates an improvement over the conventional statistical approach through the exploitation of diverse forms of knowledge in its decision-making process.

  12. Method for secure electronic voting system: face recognition based approach

    Science.gov (United States)

    Alim, M. Affan; Baig, Misbah M.; Mehboob, Shahzain; Naseem, Imran

    2017-06-01

    In this paper, we propose a framework for low cost secure electronic voting system based on face recognition. Essentially Local Binary Pattern (LBP) is used for face feature characterization in texture format followed by chi-square distribution is used for image classification. Two parallel systems are developed based on smart phone and web applications for face learning and verification modules. The proposed system has two tire security levels by using person ID followed by face verification. Essentially class specific threshold is associated for controlling the security level of face verification. Our system is evaluated three standard databases and one real home based database and achieve the satisfactory recognition accuracies. Consequently our propose system provides secure, hassle free voting system and less intrusive compare with other biometrics.

  13. Auditory signal design for automatic number plate recognition system

    NARCIS (Netherlands)

    Heydra, C.G.; Jansen, R.J.; Van Egmond, R.

    2014-01-01

    This paper focuses on the design of an auditory signal for the Automatic Number Plate Recognition system of Dutch national police. The auditory signal is designed to alert police officers of suspicious cars in their proximity, communicating priority level and location of the suspicious car and

  14. Design and implementation of face recognition system based on Windows

    Science.gov (United States)

    Zhang, Min; Liu, Ting; Li, Ailan

    2015-07-01

    In view of the basic Windows login password input way lacking of safety and convenient operation, we will introduce the biometrics technology, face recognition, into the computer to login system. Not only can it encrypt the computer system, also according to the level to identify administrators at all levels. With the enhancement of the system security, user input can neither be a cumbersome nor worry about being stolen password confidential.

  15. A system of automatic speaker recognition on a minicomputer

    International Nuclear Information System (INIS)

    El Chafei, Cherif

    1978-01-01

    This study describes a system of automatic speaker recognition using the pitch of the voice. The pre-treatment consists in the extraction of the speakers' discriminating characteristics taken from the pitch. The programme of recognition gives, firstly, a preselection and then calculates the distance between the speaker's characteristics to be recognized and those of the speakers already recorded. An experience of recognition has been realized. It has been undertaken with 15 speakers and included 566 tests spread over an intermittent period of four months. The discriminating characteristics used offer several interesting qualities. The algorithms concerning the measure of the characteristics on one hand, the speakers' classification on the other hand, are simple. The results obtained in real time with a minicomputer are satisfactory. Furthermore they probably could be improved if we considered other speaker's discriminating characteristics but this was unfortunately not in our possibilities. (author) [fr

  16. MITLL 2015 Language Recognition Evaluation System Description

    Science.gov (United States)

    2016-01-27

    912 8.18 qsl-rus Russian 2021 37.80 ara-ary Maghrebi 919 46.91 spa-car Carib. Spa. 194 30.59 ara-arz Egyptian 440 97.27 spa-eur Eur. Spa. 366 8.55...qsl-pol Polish 695 32.14 ara-arb MSA 912 8.18 qsl-rus Russian 2021 37.80 ara-ary Maghrebi 919 46.91 spa-car Carib. Spa. 194 30.59 ara-arz Egyptian ...BOTTLENECK I-VECTOR SYSTEM (BNF1) The Deep Neural Network architecture that we used for this system was composed of seven hidden layers. The sixth

  17. Application of Video Recognition Technology in Landslide Monitoring System

    Directory of Open Access Journals (Sweden)

    Qingjia Meng

    2018-01-01

    Full Text Available The video recognition technology is applied to the landslide emergency remote monitoring system. The trajectories of the landslide are identified by this system in this paper. The system of geological disaster monitoring is applied synthetically to realize the analysis of landslide monitoring data and the combination of video recognition technology. Landslide video monitoring system will video image information, time point, network signal strength, power supply through the 4G network transmission to the server. The data is comprehensively analysed though the remote man-machine interface to conduct to achieve the threshold or manual control to determine the front-end video surveillance system. The system is used to identify the target landslide video for intelligent identification. The algorithm is embedded in the intelligent analysis module, and the video frame is identified, detected, analysed, filtered, and morphological treatment. The algorithm based on artificial intelligence and pattern recognition is used to mark the target landslide in the video screen and confirm whether the landslide is normal. The landslide video monitoring system realizes the remote monitoring and control of the mobile side, and provides a quick and easy monitoring technology.

  18. Constraints in distortion-invariant target recognition system simulation

    Science.gov (United States)

    Iftekharuddin, Khan M.; Razzaque, Md A.

    2000-11-01

    Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

  19. Using a data fusion-based activity recognition framework to determine surveillance system requirements

    CSIR Research Space (South Africa)

    Le Roux, WH

    2007-07-01

    Full Text Available A technique is proposed to extract system requirements for a maritime area surveillance system, based on an activity recognition framework originally intended for the characterisation, prediction and recognition of intentional actions for threat...

  20. A Classification Framework for Large-Scale Face Recognition Systems

    OpenAIRE

    Zhou, Ziheng; Deravi, Farzin

    2009-01-01

    This paper presents a generic classification framework for large-scale face recognition systems. Within the framework, a data sampling strategy is proposed to tackle the data imbalance when image pairs are sampled from thousands of face images for preparing a training dataset. A modified kernel Fisher discriminant classifier is proposed to make it computationally feasible to train the kernel-based classification method using tens of thousands of training samples. The framework is tested in an...

  1. PALESTINE AUTOMOTIVE LICENSE IDENTITY RECOGNITION FOR INTELLIGENT PARKING SYSTEM

    OpenAIRE

    ANEES ABU SNEINEH; WAEL A. SALAH

    2017-01-01

    Providing employees with protection and security is one of the key concerns of any organization. This goal can be implemented mainly by managing and protecting employees’ cars in the parking area. Therefore, a parking area must be managed and organized with smart technologies and tools that can be applied and integrated in an intelligent parking system. This paper presents the tools based on image recognition technology that can be used to effectively control various parts of a parking sys...

  2. Automated recognition system for ELM classification in JET

    International Nuclear Information System (INIS)

    Duro, N.; Dormido, R.; Vega, J.; Dormido-Canto, S.; Farias, G.; Sanchez, J.; Vargas, H.; Murari, A.

    2009-01-01

    Edge localized modes (ELMs) are instabilities occurring in the edge of H-mode plasmas. Considerable efforts are being devoted to understanding the physics behind this non-linear phenomenon. A first characterization of ELMs is usually their identification as type I or type III. An automated pattern recognition system has been developed in JET for off-line ELM recognition and classification. The empirical method presented in this paper analyzes each individual ELM instead of starting from a temporal segment containing many ELM bursts. The ELM recognition and isolation is carried out using three signals: Dα, line integrated electron density and stored diamagnetic energy. A reduced set of characteristics (such as diamagnetic energy drop, ELM period or Dα shape) has been extracted to build supervised and unsupervised learning systems for classification purposes. The former are based on support vector machines (SVM). The latter have been developed with hierarchical and K-means clustering methods. The success rate of the classification systems is about 98% for a database of almost 300 ELMs.

  3. Industrial robots with sensors and object recognition systems

    International Nuclear Information System (INIS)

    Koehler, G.W.

    1978-01-01

    The previous development and the present status of industrial robots equipped with sensors and object recognition systems are described. This type of equipment allows flexible automation of many work stations in which industrial robots of the first generation, which are unable to react to changes in their respective environments automatically, apart from their being linked to other machines, could not be used because of the prevailing boundary conditions. A classification system facilitates an overview of the large number of technical solutions now available. The manifold possibilities of application of this equipment are demonstrated by a number of examples. As a result of the present state of development of the components required, and in view also of economic reasons, there is a trend towards special designs for a small number of specific purposes and towards stripped-down object recognition. systems with limited applications. A fitting description is offered of the term 'robot', which is now being used in various contexts, and an indication is made of the capabilities and components a machine to be called robot should have as a minimum. Finally, reference is made to some potential lines of development serving to reduce expediture and accelerate recognition processes. (orig.) [de

  4. Speech recognition systems on the Cell Broadband Engine

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Y; Jones, H; Vaidya, S; Perrone, M; Tydlitat, B; Nanda, A

    2007-04-20

    In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousands of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.

  5. Source Separation via Spectral Masking for Speech Recognition Systems

    Directory of Open Access Journals (Sweden)

    Gustavo Fernandes Rodrigues

    2012-12-01

    Full Text Available In this paper we present an insight into the use of spectral masking techniques in time-frequency domain, as a preprocessing step for the speech signal recognition. Speech recognition systems have their performance negatively affected in noisy environments or in the presence of other speech signals. The limits of these masking techniques for different levels of the signal-to-noise ratio are discussed. We show the robustness of the spectral masking techniques against four types of noise: white, pink, brown and human speech noise (bubble noise. The main contribution of this work is to analyze the performance limits of recognition systems  using spectral masking. We obtain an increase of 18% on the speech hit rate, when the speech signals were corrupted by other speech signals or bubble noise, with different signal-to-noise ratio of approximately 1, 10 and 20 dB. On the other hand, applying the ideal binary masks to mixtures corrupted by white, pink and brown noise, results an average growth of 9% on the speech hit rate, with the same different signal-to-noise ratio. The experimental results suggest that the masking spectral techniques are more suitable for the case when it is applied a bubble noise, which is produced by human speech, than for the case of applying white, pink and brown noise.

  6. An Edge-Based Macao License Plate Recognition System

    Directory of Open Access Journals (Sweden)

    Chi-Man Pun

    2011-04-01

    Full Text Available This paper presents a system to recognize Macao license plates. Sobel edge detector is employed to extract the vertical edges, and an edge composition algorithm is proposed to combine the edges into candidate plate regions. They are further examined on the existence of the character qMq by a verification algorithm. A row separation algorithm is also proposed to cater both one-row and two-row types of plates. Projection analysis and template matching methods are exploited to segment and recognize the characters. Various pre and post processing steps are proposed other than traditional implementation so as to improve the recognition accuracy. This work achieves a high recognition rate of 95%.

  7. Improving emotion recognition systems by embedding cardiorespiratory coupling

    International Nuclear Information System (INIS)

    Valenza, Gaetano; Lanatá, Antonio; Scilingo, Enzo Pasquale

    2013-01-01

    This work aims at showing improved performances of an emotion recognition system embedding information gathered from cardiorespiratory (CR) coupling. Here, we propose a novel methodology able to robustly identify up to 25 regions of a two-dimensional space model, namely the well-known circumplex model of affect (CMA). The novelty of embedding CR coupling information in an autonomic nervous system-based feature space better reveals the sympathetic activations upon emotional stimuli. A CR synchrogram analysis was used to quantify such a coupling in terms of number of heartbeats per respiratory period. Physiological data were gathered from 35 healthy subjects emotionally elicited by means of affective pictures of the international affective picture system database. In this study, we finely detected five levels of arousal and five levels of valence as well as the neutral state, whose combinations were used for identifying 25 different affective states in the CMA plane. We show that the inclusion of the bivariate CR measures in a previously developed system based only on monovariate measures of heart rate variability, respiration dynamics and electrodermal response dramatically increases the recognition accuracy of a quadratic discriminant classifier, obtaining more than 90% of correct classification per class. Finally, we propose a comprehensive description of the CR coupling during sympathetic elicitation adapting an existing theoretical nonlinear model with external driving. The theoretical idea behind this model is that the CR system is comprised of weakly coupled self-sustained oscillators that, when exposed to an external perturbation (i.e. sympathetic activity), becomes synchronized and less sensible to input variations. Given the demonstrated role of the CR coupling, this model can constitute a general tool which is easily embedded in other model-based emotion recognition systems. (paper)

  8. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1976-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

  9. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1975-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

  10. Human-inspired sound environment recognition system for assistive vehicles

    Science.gov (United States)

    González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

    2015-02-01

    Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance

  11. Military personnel recognition system using texture, colour, and SURF features

    Science.gov (United States)

    Irhebhude, Martins E.; Edirisinghe, Eran A.

    2014-06-01

    This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

  12. Application of robust face recognition in video surveillance systems

    Science.gov (United States)

    Zhang, De-xin; An, Peng; Zhang, Hao-xiang

    2018-03-01

    In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

  13. A Development of Hybrid Drug Information System Using Image Recognition

    Directory of Open Access Journals (Sweden)

    HwaMin Lee

    2015-04-01

    Full Text Available In order to prevent drug abuse or misuse cases and avoid over-prescriptions, it is necessary for medicine taker to be provided with detailed information about the medicine. In this paper, we propose a drug information system and develop an application to provide information through drug image recognition using a smartphone. We designed a contents-based drug image search algorithm using the color, shape and imprint of drug. Our convenient application can provide users with detailed information about drugs and prevent drug misuse.

  14. Increasing the information acquisition volume in iris recognition systems.

    Science.gov (United States)

    Barwick, D Shane

    2008-09-10

    A significant hurdle for the widespread adoption of iris recognition in security applications is that the typically small imaging volume for eye placement results in systems that are not user friendly. Separable cubic phase plates at the lens pupil have been shown to ameliorate this disadvantage by increasing the depth of field. However, these phase masks have limitations on how efficiently they can capture the information-bearing spatial frequencies in iris images. The performance gains in information acquisition that can be achieved by more general, nonseparable phase masks is demonstrated. A detailed design method is presented, and simulations using representative designs allow for performance comparisons.

  15. PALESTINE AUTOMOTIVE LICENSE IDENTITY RECOGNITION FOR INTELLIGENT PARKING SYSTEM

    Directory of Open Access Journals (Sweden)

    ANEES ABU SNEINEH

    2017-05-01

    Full Text Available Providing employees with protection and security is one of the key concerns of any organization. This goal can be implemented mainly by managing and protecting employees’ cars in the parking area. Therefore, a parking area must be managed and organized with smart technologies and tools that can be applied and integrated in an intelligent parking system. This paper presents the tools based on image recognition technology that can be used to effectively control various parts of a parking system. An intelligent automotive parking system is effectively implemented by integrating image processing technologies and an Arduino controller. Results show that intelligent parking is successfully implemented based on car ID image capture to meet the need for managing and organizing car parking systems.

  16. Formal Implementation of a Performance Evaluation Model for the Face Recognition System

    Directory of Open Access Journals (Sweden)

    Yong-Nyuo Shin

    2008-01-01

    Full Text Available Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

  17. Real-time image restoration for iris recognition systems.

    Science.gov (United States)

    Kang, Byung Jun; Park, Kang Ryoung

    2007-12-01

    In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: 1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; 2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; 3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; 4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and 5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.

  18. New neural-networks-based 3D object recognition system

    Science.gov (United States)

    Abolmaesumi, Purang; Jahed, M.

    1997-09-01

    Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

  19. Business model for sensor-based fall recognition systems.

    Science.gov (United States)

    Fachinger, Uwe; Schöpke, Birte

    2014-01-01

    AAL systems require, in addition to sophisticated and reliable technology, adequate business models for their launch and sustainable establishment. This paper presents the basic features of alternative business models for a sensor-based fall recognition system which was developed within the context of the "Lower Saxony Research Network Design of Environments for Ageing" (GAL). The models were developed parallel to the R&D process with successive adaptation and concretization. An overview of the basic features (i.e. nine partial models) of the business model is given and the mutual exclusive alternatives for each partial model are presented. The partial models are interconnected and the combinations of compatible alternatives lead to consistent alternative business models. However, in the current state, only initial concepts of alternative business models can be deduced. The next step will be to gather additional information to work out more detailed models.

  20. Entrance C - New Automatic Number Plate Recognition System

    CERN Multimedia

    2013-01-01

    Entrance C (Satigny) is now equipped with a latest-generation Automatic Number Plate Recognition (ANPR) system and a fast-action road gate.   During the month of August, Entrance C will be continuously open from 7.00 a.m. to 7.00 p.m. (working days only). The security guards will open the gate as usual from 7.00 a.m. to 9.00 a.m. and from 5.00 p.m. to 7.00 p.m. For the rest of the working day (9.00 a.m. to 5.00 p.m.) the gate will operate automatically. Please observe the following points:       Stop at the STOP sign on the ground     Position yourself next to the card reader for optimal recognition     Motorcyclists must use their CERN card     Cyclists may not activate the gate and should use the bicycle turnstile     Keep a safe distance from the vehicle in front of you   If access is denied, please check that your vehicle regist...

  1. Point spread function engineering for iris recognition system design.

    Science.gov (United States)

    Ashok, Amit; Neifeld, Mark A

    2010-04-01

    Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling. A task-specific optimization framework is used to engineer the optical PSF and optimize the postprocessing parameters to minimize the FRR. The optimized Zernike phase enhanced lens (ZPEL) imager design with one frame yields an improvement of nearly 33% relative to a thin observation module by bounded optics (TOMBO) imager with one frame. With four frames the optimized ZPEL imager achieves a FRR equal to that of the conventional imager without undersampling. Further, the ZPEL imager design using 16 frames yields a FRR that is actually 15% lower than that obtained with the conventional imager without undersampling.

  2. Simple test system for single molecule recognition force microscopy

    International Nuclear Information System (INIS)

    Riener, Christian K.; Stroh, Cordula M.; Ebner, Andreas; Klampfl, Christian; Gall, Alex A.; Romanin, Christoph; Lyubchenko, Yuri L.; Hinterdorfer, Peter; Gruber, Hermann J.

    2003-01-01

    We have established an easy-to-use test system for detecting receptor-ligand interactions on the single molecule level using atomic force microscopy (AFM). For this, avidin-biotin, probably the best characterized receptor-ligand pair, was chosen. AFM sensors were prepared containing tethered biotin molecules at sufficiently low surface concentrations appropriate for single molecule studies. A biotin tether, consisting of a 6 nm poly(ethylene glycol) (PEG) chain and a functional succinimide group at the other end, was newly synthesized and covalently coupled to amine-functionalized AFM tips. In particular, PEG 800 diamine was glutarylated, the mono-adduct NH 2 -PEG-COOH was isolated by ion exchange chromatography and reacted with biotin succinimidylester to give biotin-PEG-COOH which was then activated as N-hydroxysuccinimide (NHS) ester to give the biotin-PEG-NHS conjugate which was coupled to the aminofunctionalized AFM tip. The motional freedom provided by PEG allows for free rotation of the biotin molecule on the AFM sensor and for specific binding to avidin which had been adsorbed to mica surfaces via electrostatic interactions. Specific avidin-biotin recognition events were discriminated from nonspecific tip-mica adhesion by their typical unbinding force (∼40 pN at 1.4 nN/s loading rate), unbinding length (<13 nm), the characteristic nonlinear force-distance relation of the PEG linker, and by specific block with excess of free d-biotin. The convenience of the test system allowed to evaluate, and compare, different methods and conditions of tip aminofunctionalization with respect to specific binding and nonspecific adhesion. It is concluded that this system is well suited as calibration or start-up kit for single molecule recognition force microscopy

  3. HMM Adaptation for Improving a Human Activity Recognition System

    Directory of Open Access Journals (Sweden)

    Rubén San-Segundo

    2016-09-01

    Full Text Available When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs. This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity, and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.

  4. Using Face Recognition System in Ship Protection Process

    Directory of Open Access Journals (Sweden)

    Miroslav Bača

    2006-03-01

    Full Text Available The process of security improvement is a huge problem especiallyin large ships. Terrorist attacks and everyday threatsagainst life and property destroy transport and tourist companies,especially large tourist ships. Every person on a ship can berecognized and identified using something that the personknows or by means of something the person possesses. The bestresults will be obtained by using a combination of the person'sknowledge with one biometric characteristic. Analyzing theproblem of biometrics in ITS security we can conclude that facerecognition process supported by one or two traditional biometriccharacteristics can give very good results regarding ship security.In this paper we will describe a biometric system basedon face recognition. Special focus will be given to crew member'sbiometric security in crisis situation like kidnapping, robbelyor illness.

  5. Automated Degradation Diagnosis in Character Recognition System Subject to Camera Vibration

    Directory of Open Access Journals (Sweden)

    Chunmei Liu

    2014-01-01

    Full Text Available Degradation diagnosis plays an important role for degraded character processing, which can tell the recognition difficulty of a given degraded character. In this paper, we present a framework for automated degraded character recognition system by statistical syntactic approach using 3D primitive symbol, which is integrated by degradation diagnosis to provide accurate and reliable recognition results. Our contribution is to design the framework to build the character recognition submodels corresponding to degradation subject to camera vibration or out of focus. In each character recognition submodel, statistical syntactic approach using 3D primitive symbol is proposed to improve degraded character recognition performance. In the experiments, we show attractive experimental results, highlighting the system efficiency and recognition performance by statistical syntactic approach using 3D primitive symbol on the degraded character dataset.

  6. Face recognition system and method using face pattern words and face pattern bytes

    Science.gov (United States)

    Zheng, Yufeng

    2014-12-23

    The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

  7. Development of a System for Automatic Recognition of Speech

    Directory of Open Access Journals (Sweden)

    Roman Jarina

    2003-01-01

    Full Text Available The article gives a review of a research on processing and automatic recognition of speech signals (ARR at the Department of Telecommunications of the Faculty of Electrical Engineering, University of iilina. On-going research is oriented to speech parametrization using 2-dimensional cepstral analysis, and to an application of HMMs and neural networks for speech recognition in Slovak language. The article summarizes achieved results and outlines future orientation of our research in automatic speech recognition.

  8. Container-code recognition system based on computer vision and deep neural networks

    Science.gov (United States)

    Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

    2018-04-01

    Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

  9. Human Iris Recognition System using Wavelet Transform and LVQ

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kwan Yong; Lim, Shin Young [Electronics and Telecommunications Research Institute (Korea); Cho, Seong Won [Hongik University (Korea)

    2000-07-01

    The popular methods to check the identity of individuals include passwords and ID cards. These conventional methods for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way. (author). 14 refs., 13 figs., 7 tabs.

  10. Poka Yoke system based on image analysis and object recognition

    Science.gov (United States)

    Belu, N.; Ionescu, L. M.; Misztal, A.; Mazăre, A.

    2015-11-01

    Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

  11. Contextual System of Symbol Structural Recognition based on an Object-Process Methodology

    OpenAIRE

    Delalandre, Mathieu

    2005-01-01

    We present in this paper a symbol recognition system for the graphic documents. This one is based on a contextual approach for symbol structural recognition exploiting an Object-Process Methodology. It uses a processing library composed of structural recognition processings and contextual evaluation processings. These processings allow our system to deal with the multi-representation of symbols. The different processings are controlled, in an automatic way, by an inference engine during the r...

  12. Intelligent color recognition system using micro-controller

    International Nuclear Information System (INIS)

    Mohd Ashhar Khalid; Khairiah Yazid; Nur Aira Abd Rahman; Azaman Ahmad

    2006-01-01

    Color is widely used in categorizing the quality of products as well as a marker for automatic selection and discrimination of products. Most of color recognizing process is done manually and due to the fact that human perceived color differently, different of opinion frequently occur. This paper deals with the development of an intelligent color recognition system used for discriminating the ripeness of oil palm fruits into three categories namely ripe, under-ripe and un-ripe. In deciding the categories of fruit a sample belong, a technique of decision making similar to human thinking called neural network has been implemented. Implementation of neural network using a micro-controller is not so common, due to a limited capability in floating point calculation. To overcome the problem, a floating-point co-processor specially designed for micro-controller is used. The paper will report the system design and the network training and implementation methods. The effectiveness of the system compared to human decision method is also reported. (Author)

  13. Ethical aspects of face recognition systems in public places.

    NARCIS (Netherlands)

    Brey, Philip A.E.

    2004-01-01

    This essay examines ethical aspects of the use of facial recognition technology for surveillance purposes in public and semipublic areas, focusing particularly on the balance between security and privacy and civil liberties. As a case study, the FaceIt facial recognition engine of Identix

  14. Social context predicts recognition systems in ant queens

    DEFF Research Database (Denmark)

    Dreier, Stéphanie Agnès Jeanine; d'Ettorre, Patrizia

    2009-01-01

    Recognition of group-members is a key feature of sociality. Ants use chemical communication to discriminate nestmates from intruders, enhancing kin cooperation and preventing parasitism. The recognition code is embedded in their cuticular chemical profile, which typically varies between colonies....... We predicted that ants might be capable of accurate recognition in unusual situations when few individuals interact repeatedly, as new colonies started by two to three queens. Individual recognition would be favoured by selection when queens establish dominance hierarchies, because repeated fights...... for dominance are costly; but it would not evolve in absence of hierarchies. We previously showed that Pachycondyla co-founding queens, which form dominance hierarchies, have accurate individual recognition based on chemical cues. Here, we used the ant Lasius niger to test the null hypothesis that individual...

  15. On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information

    Science.gov (United States)

    Stathopoulou, I.-O.; Alepis, E.; Tsihrintzis, G. A.; Virvou, M.

    Towards realizing a multimodal affect recognition system, we are considering the advantages of assisting a visual-facial expression recognition system with keyboard-stroke pattern information. Our work is based on the assumption that the visual-facial and keyboard modalities are complementary to each other and that their combination can significantly improve the accuracy in affective user models. Specifically, we present and discuss the development and evaluation process of two corresponding affect recognition subsystems, with emphasis on the recognition of 6 basic emotional states, namely happiness, sadness, surprise, anger and disgust as well as the emotion-less state which we refer to as neutral. We find that emotion recognition by the visual-facial modality can be aided greatly by keyboard-stroke pattern information and the combination of the two modalities can lead to better results towards building a multimodal affect recognition system.

  16. Automatic Speech Acquisition and Recognition for Spacesuit Audio Systems

    Science.gov (United States)

    Ye, Sherry

    2015-01-01

    NASA has a widely recognized but unmet need for novel human-machine interface technologies that can facilitate communication during astronaut extravehicular activities (EVAs), when loud noises and strong reverberations inside spacesuits make communication challenging. WeVoice, Inc., has developed a multichannel signal-processing method for speech acquisition in noisy and reverberant environments that enables automatic speech recognition (ASR) technology inside spacesuits. The technology reduces noise by exploiting differences between the statistical nature of signals (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, ASR accuracy can be improved to the level at which crewmembers will find the speech interface useful. System components and features include beam forming/multichannel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, and ASR decoding. Arithmetic complexity models were developed and will help designers of real-time ASR systems select proper tasks when confronted with constraints in computational resources. In Phase I of the project, WeVoice validated the technology. The company further refined the technology in Phase II and developed a prototype for testing and use by suited astronauts.

  17. Implementation of CT and IHT Processors for Invariant Object Recognition System

    Directory of Open Access Journals (Sweden)

    J. Turan jr.

    2004-12-01

    Full Text Available This paper presents PDL or ASIC implementation of key modules ofinvariant object recognition system based on the combination of theIncremental Hough transform (IHT, correlation and rapid transform(RT. The invariant object recognition system was represented partiallyin C++ language for general-purpose processor on personal computer andpartially described in VHDL code for implementation in PLD or ASIC.

  18. The effect of image resolution on the performance of a face recognition system

    NARCIS (Netherlands)

    Boom, B.J.; Beumer, G.M.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    2006-01-01

    In this paper we investigate the effect of image resolution on the error rates of a face verification system. We do not restrict ourselves to the face recognition algorithm only, but we also consider the face registration. In our face recognition system, the face registration is done by finding

  19. Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems.

    Science.gov (United States)

    Fang, Fuming; Shinozaki, Takahiro

    2018-01-01

    Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data.

  20. Recognition of medical errors' reporting system dimensions in educational hospitals.

    Science.gov (United States)

    Yarmohammadian, Mohammad H; Mohammadinia, Leila; Tavakoli, Nahid; Ghalriz, Parvin; Haghshenas, Abbas

    2014-01-01

    Nowadays medical errors are one of the serious issues in the health-care system and carry to account of the patient's safety threat. The most important step for achieving safety promotion is identifying errors and their causes in order to recognize, correct and omit them. Concerning about repeating medical errors and harms, which were received via theses errors concluded to designing and establishing medical error reporting systems for hospitals and centers that are presenting therapeutic services. The aim of this study is the recognition of medical errors' reporting system dimensions in educational hospitals. This research is a descriptive-analytical and qualities' study, which has been carried out in Shahid Beheshti educational therapeutic center in Isfahan during 2012. In this study, relevant information was collected through 15 face to face interviews. That each of interviews take place in about 1hr and creation of five focused discussion groups through 45 min for each section, they were composed of Metron, educational supervisor, health officer, health education, and all of the head nurses. Concluded data interviews and discussion sessions were coded, then achieved results were extracted in the presence of clear-sighted persons and after their feedback perception, they were categorized. In order to make sure of information correctness, tables were presented to the research's interviewers and final the corrections were confirmed based on their view. The extracted information from interviews and discussion groups have been divided into nine main categories after content analyzing and subject coding and their subsets have been completely expressed. Achieved dimensions are composed of nine domains of medical error concept, error cases according to nurses' prospection, medical error reporting barriers, employees' motivational factors for error reporting, purposes of medical error reporting system, error reporting's challenges and opportunities, a desired system

  1. Multi-Stage System for Automatic Target Recognition

    Science.gov (United States)

    Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

    2010-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an

  2. Implementation of age and gender recognition system for intelligent digital signage

    Science.gov (United States)

    Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Hyunduk

    2015-12-01

    Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

  3. Structural insight into RNA recognition motifs: versatile molecular Lego building blocks for biological systems.

    Science.gov (United States)

    Muto, Yutaka; Yokoyama, Shigeyuki

    2012-01-01

    'RNA recognition motifs (RRMs)' are common domain-folds composed of 80-90 amino-acid residues in eukaryotes, and have been identified in many cellular proteins. At first they were known as RNA binding domains. Through discoveries over the past 20 years, however, the RRMs have been shown to exhibit versatile molecular recognition activities and to behave as molecular Lego building blocks to construct biological systems. Novel RNA/protein recognition modes by RRMs are being identified, and more information about the molecular recognition by RRMs is becoming available. These RNA/protein recognition modes are strongly correlated with their biological significance. In this review, we would like to survey the recent progress on these versatile molecular recognition modules. Copyright © 2012 John Wiley & Sons, Ltd.

  4. Multi-font printed Mongolian document recognition system

    Science.gov (United States)

    Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

    2009-01-01

    Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

  5. AUTOMATIC SPEECH RECOGNITION SYSTEM CONCERNING THE MOROCCAN DIALECTE (Darija and Tamazight)

    OpenAIRE

    A. EL GHAZI; C. DAOUI; N. IDRISSI

    2012-01-01

    In this work we present an automatic speech recognition system for Moroccan dialect mainly: Darija (Arab dialect) and Tamazight. Many approaches have been used to model the Arabic and Tamazightphonetic units. In this paper, we propose to use the hidden Markov model (HMM) for modeling these phoneticunits. Experimental results show that the proposed approach further improves the recognition.

  6. 42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 2 2010-10-01 2010-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL PROVISIONS SPECIAL PROGRAMS AND PROJECTS Recognition of State...

  7. Designing a Low-Resolution Face Recognition System for Long-Range Surveillance

    NARCIS (Netherlands)

    Peng, Y.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    2016-01-01

    Most face recognition systems deal well with high-resolution facial images, but perform much worse on low-resolution facial images. In low-resolution face recognition, there is a specific but realistic surveillance scenario: a surveillance camera monitoring a large area. In this scenario, usually

  8. Enhance Criminal Investigation by Proposed Fingerprint Recognition System

    International Nuclear Information System (INIS)

    Hashem, S.H.; Maolod, A.T.; Mohammad, A.A.

    2014-01-01

    Law enforcement officers and forensic specialists spend hours thinking about how fingerprints solve crimes, and trying to find, collect, record and compare these unique identifiers that can connect a specific person to a specific crime. These individuals understand that a basic human feature that most people take for granted, can be one of the most effective tools in crime solving.This research exploits our previous work to be applicable in criminal investigation field. The present study aims to solve the advance crime by strength fingerprint’s criminal investigation to control the alterations happen intentionally to criminals’ fingerprint. That done by suggest strategy introduce an optimal fingerprint image feature’s vector to the person and then considers it to be stored in database for future matching. Selecting optimal fingerprint feature’s vector strategy deal with considering 10 fingerprints for each criminal person (take the fingerprint in different time and different circumstance of criminal such as finger is dirty, wet, trembling, etc.). Proposal begun with apply a proposed enrollment on all 10 fingerprint for each criminal, the enrollment include the following consequence steps; begin with preprocessing step for each of 10 images including enhancement, then two level of feature extraction (first level to extract arches, whorls, and loops, where second level extract minutiae), after that applying proposed Genetic Algorithm to select optimal fingerprint, master fingerprint, which in our point of view present the most universal image which include more detailed features to recognition. Master fingerprint will be feature’s vector which stored in database. Then apply the proposed matching by testing fingerprints with these stored in database.While, measuring of criminal fingerprint investigation performance by calculating False Reject Rate (FRR)and False Accept Rate (FAR) for the traditional system and the proposed in criminal detection field. The

  9. 2nd International Symposium on Signal Processing and Intelligent Recognition Systems

    CERN Document Server

    Bandyopadhyay, Sanghamitra; Krishnan, Sri; Li, Kuan-Ching; Mosin, Sergey; Ma, Maode

    2016-01-01

    This Edited Volume contains a selection of refereed and revised papers originally presented at the second International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2015), December 16-19, 2015, Trivandrum, India. The program committee received 175 submissions. Each paper was peer reviewed by at least three or more independent referees of the program committee and the 59 papers were finally selected. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. The book is directed to the researchers and scientists engaged in various field of signal processing and related areas. .

  10. A computer aided treatment event recognition system in radiation therapy

    International Nuclear Information System (INIS)

    Xia, Junyi; Mart, Christopher; Bayouth, John

    2014-01-01

    Purpose: To develop an automated system to safeguard radiation therapy treatments by analyzing electronic treatment records and reporting treatment events. Methods: CATERS (Computer Aided Treatment Event Recognition System) was developed to detect treatment events by retrieving and analyzing electronic treatment records. CATERS is designed to make the treatment monitoring process more efficient by automating the search of the electronic record for possible deviations from physician's intention, such as logical inconsistencies as well as aberrant treatment parameters (e.g., beam energy, dose, table position, prescription change, treatment overrides, etc). Over a 5 month period (July 2012–November 2012), physicists were assisted by the CATERS software in conducting normal weekly chart checks with the aims of (a) determining the relative frequency of particular events in the authors’ clinic and (b) incorporating these checks into the CATERS. During this study period, 491 patients were treated at the University of Iowa Hospitals and Clinics for a total of 7692 fractions. Results: All treatment records from the 5 month analysis period were evaluated using all the checks incorporated into CATERS after the training period. About 553 events were detected as being exceptions, although none of them had significant dosimetric impact on patient treatments. These events included every known event type that was discovered during the trial period. A frequency analysis of the events showed that the top three types of detected events were couch position override (3.2%), extra cone beam imaging (1.85%), and significant couch position deviation (1.31%). The significant couch deviation is defined as the number of treatments where couch vertical exceeded two times standard deviation of all couch verticals, or couch lateral/longitudinal exceeded three times standard deviation of all couch laterals and longitudinals. On average, the application takes about 1 s per patient when

  11. A computer aided treatment event recognition system in radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    Xia, Junyi, E-mail: junyi-xia@uiowa.edu; Mart, Christopher [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, Iowa 52242 (United States); Bayouth, John [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, Iowa 52242 and Department of Human Oncology, University of Wisconsin - Madison, 600 Highland Avenue, K4/B55, Madison, Wisconsin 53792-0600 (United States)

    2014-01-15

    Purpose: To develop an automated system to safeguard radiation therapy treatments by analyzing electronic treatment records and reporting treatment events. Methods: CATERS (Computer Aided Treatment Event Recognition System) was developed to detect treatment events by retrieving and analyzing electronic treatment records. CATERS is designed to make the treatment monitoring process more efficient by automating the search of the electronic record for possible deviations from physician's intention, such as logical inconsistencies as well as aberrant treatment parameters (e.g., beam energy, dose, table position, prescription change, treatment overrides, etc). Over a 5 month period (July 2012–November 2012), physicists were assisted by the CATERS software in conducting normal weekly chart checks with the aims of (a) determining the relative frequency of particular events in the authors’ clinic and (b) incorporating these checks into the CATERS. During this study period, 491 patients were treated at the University of Iowa Hospitals and Clinics for a total of 7692 fractions. Results: All treatment records from the 5 month analysis period were evaluated using all the checks incorporated into CATERS after the training period. About 553 events were detected as being exceptions, although none of them had significant dosimetric impact on patient treatments. These events included every known event type that was discovered during the trial period. A frequency analysis of the events showed that the top three types of detected events were couch position override (3.2%), extra cone beam imaging (1.85%), and significant couch position deviation (1.31%). The significant couch deviation is defined as the number of treatments where couch vertical exceeded two times standard deviation of all couch verticals, or couch lateral/longitudinal exceeded three times standard deviation of all couch laterals and longitudinals. On average, the application takes about 1 s per patient when

  12. An enhanced iris recognition and authentication system using ...

    African Journals Online (AJOL)

    Iris recognition and authentication has a major issue in its code generation and verification accuracy, in order to enhance the authentication process, a binary bit sequence of iris is generated, which contain several vital information that is used to calculate the Mean Energy and Maximum Energy that goes into the eye with an ...

  13. Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

    Science.gov (United States)

    Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

    2018-01-01

    Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

  14. Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2018-04-01

    Full Text Available Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD method for an iris recognition system (iPAD using a near infrared light (NIR camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED. Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM. Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

  15. Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

    Science.gov (United States)

    Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

    2018-04-24

    Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

  16. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    Science.gov (United States)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  17. Optical-electronic shape recognition system based on synergetic associative memory

    Science.gov (United States)

    Gao, Jun; Bao, Jie; Chen, Dingguo; Yang, Youqing; Yang, Xuedong

    2001-04-01

    This paper presents a novel optical-electronic shape recognition system based on synergetic associative memory. Our shape recognition system is composed of two parts: the first one is feature extraction system; the second is synergetic pattern recognition system. Hough transform is proposed for feature extraction of unrecognized object, with the effects of reducing dimensions and filtering for object distortion and noise, synergetic neural network is proposed for realizing associative memory in order to eliminate spurious states. Then we adopt an approach of optical- electronic realization to our system that can satisfy the demands of real time, high speed and parallelism. In order to realize fast algorithm, we replace the dynamic evolution circuit with adjudge circuit according to the relationship between attention parameters and order parameters, then implement the recognition of some simple images and its validity is proved.

  18. Effects of emotional and perceptual-motor stress on a voice recognition system's accuracy: An applied investigation

    Science.gov (United States)

    Poock, G. K.; Martin, B. J.

    1984-02-01

    This was an applied investigation examining the ability of a speech recognition system to recognize speakers' inputs when the speakers were under different stress levels. Subjects were asked to speak to a voice recognition system under three conditions: (1) normal office environment, (2) emotional stress, and (3) perceptual-motor stress. Results indicate a definite relationship between voice recognition system performance and the type of low stress reference patterns used to achieve recognition.

  19. Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

    Directory of Open Access Journals (Sweden)

    Hong Zhang

    2013-01-01

    Full Text Available With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognition.

  20. Voice Activity Detection. Fundamentals and Speech Recognition System Robustness

    OpenAIRE

    Ramirez, J.; Gorriz, J. M.; Segura, J. C.

    2007-01-01

    This chapter has shown an overview of the main challenges in robust speech detection and a review of the state of the art and applications. VADs are frequently used in a number of applications including speech coding, speech enhancement and speech recognition. A precise VAD extracts a set of discriminative speech features from the noisy speech and formulates the decision in terms of well defined rule. The chapter has summarized three robust VAD methods that yield high speech/non-speech discri...

  1. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  2. The A2iA French handwriting recognition system at the Rimes-ICDAR2011 competition

    Science.gov (United States)

    Menasri, Farès; Louradour, Jérôme; Bianne-Bernard, Anne-Laure; Kermorvant, Christopher

    2012-01-01

    This paper describes the system for the recognition of French handwriting submitted by A2iA to the competition organized at ICDAR2011 using the Rimes database. This system is composed of several recognizers based on three different recognition technologies, combined using a novel combination method. A framework multi-word recognition based on weighted finite state transducers is presented, using an explicit word segmentation, a combination of isolated word recognizers and a language model. The system was tested both for isolated word recognition and for multi-word line recognition and submitted to the RIMES-ICDAR2011 competition. This system outperformed all previously proposed systems on these tasks.

  3. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  4. Pengoperasian Beban Listrik Fase Tunggal Terkendali Melalui Minimum System Berbasis Mikrokontroler Dan Sensor Voice Recognition (Vr)

    OpenAIRE

    Goeritno, Arief; Ginting, Sandy Ferdiansyah; Yatim, Rakhmad

    2017-01-01

    Minimum system berbasis mikrokontroler dan sensor voice recognition (VR) sebagai pengendali aktuator telah digunakan untuk pengoperasian beban listrik fase tunggal. Minimum system adalah suatu sistem yang tersusun melalui 2 (dua) tahapan, yaitu (a) diagram rangkaian dan bentuk fisis board dan (b) pengawatan terintegrasi terhadap minimum system pada sistem mikrokontroler ATmega16. Keberadaan sistem mikrokontroler pada minimum system perlu program tertanam melalui pemrograman berbasis bahasa ...

  5. Neuro System Structure for Vehicle Recognition and Count in Floating Bridge Specific Conditions

    Directory of Open Access Journals (Sweden)

    Slobodan Beroš

    2012-10-01

    Full Text Available The paper presents the research of the sophisticated vehiclerecognition and count system based on the application of theneural network. The basic elements of neural network andadaptive logic network for object recognition are discussed. Theadaptive logic network solution ability based on simple digitalcircuits as crucial in real-time applications is pointed out. Thesimulation based on the use of reduced high level noise pictureand a tree 2. 7. software have shown excellent results. The consideredand simulated adaptive neural network based systemwith its good recognition and convergence is a useful real-timesolution for vehicle recognition and count in the floating bridgesevere conditions.

  6. Container code recognition in information auto collection system of container inspection

    International Nuclear Information System (INIS)

    Su Jianping; Chen Zhiqiang; Zhang Li; Gao Wenhuan; Kang Kejun

    2003-01-01

    Now custom needs electrical application and automatic detection. Container inspection should not only give the image of the goods, but also auto-attain container's code and weight. Its function and track control, information transfer make up the Information Auto Collection system of Container Inspection. Code Recognition is the point. The article is based on model match, the close property of character, and uses it to recognize. Base on checkout rule, design the adjustment arithmetic, form the whole recognition strategy. This strategy can achieve high recognition ratio and robust property

  7. Interactions of the humoral pattern recognition molecule PTX3 with the complement system

    DEFF Research Database (Denmark)

    Doni, Andrea; Garlanda, Cecilia; Bottazzi, Barbara

    2012-01-01

    The innate immune system comprises a cellular and a humoral arm. The long pentraxin PTX3 is a fluid phase pattern recognition molecule, which acts as an essential component of the humoral arm of innate immunity. PTX3 has antibody-like properties including interactions with complement components....... PTX3 interacts with C1q, ficolin-1 and ficolin-2 as well as mannose-binding lectin, recognition molecules in the classical and lectin complement pathways. The formation of these heterocomplexes results in cooperative pathogen recognition and complement activation. Interactions with C4b binding protein...

  8. Named Entity Recognition in a Hungarian NL Based QA System

    Science.gov (United States)

    Tikkl, Domonkos; Szidarovszky, P. Ferenc; Kardkovacs, Zsolt T.; Magyar, Gábor

    In WoW project our purpose is to create a complex search interface with the following features: search in the deep web content of contracted partners' databases, processing Hungarian natural language (NL) questions and transforming them to SQL queries for database access, image search supported by a visual thesaurus that describes in a structural form the visual content of images (also in Hungarian). This paper primarily focuses on a particular problem of question processing task: the entity recognition. Before going into details we give a short overview of the project's aims.

  9. The use of open and machine vision technologies for development of gesture recognition intelligent systems

    Science.gov (United States)

    Cherkasov, Kirill V.; Gavrilova, Irina V.; Chernova, Elena V.; Dokolin, Andrey S.

    2018-05-01

    The article is devoted to reflection of separate aspects of intellectual system gesture recognition development. The peculiarity of the system is its intellectual block which completely based on open technologies: OpenCV library and Microsoft Cognitive Toolkit (CNTK) platform. The article presents the rationale for the choice of such set of tools, as well as the functional scheme of the system and the hierarchy of its modules. Experiments have shown that the system correctly recognizes about 85% of images received from sensors. The authors assume that the improvement of the algorithmic block of the system will increase the accuracy of gesture recognition up to 95%.

  10. Man-system interface based on automatic speech recognition: integration to a virtual control desk

    Energy Technology Data Exchange (ETDEWEB)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2009-07-01

    This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

  11. Man-system interface based on automatic speech recognition: integration to a virtual control desk

    International Nuclear Information System (INIS)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C.; Nomiya, Diogo V.

    2009-01-01

    This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

  12. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  13. Automatic micropropagation of plants--the vision-system: graph rewriting as pattern recognition

    Science.gov (United States)

    Schwanke, Joerg; Megnet, Roland; Jensch, Peter F.

    1993-03-01

    The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.

  14. Multi-Modal Activity Recognition Systems with Minimal Training Data and Unobtrusive Environmental Instrumentations

    OpenAIRE

    Bauer, Gerald

    2014-01-01

    The recognition of day-to-day activities is still a very challenging and important research topic. During recent years, a lot of research has gone into designing and realizing smart environ- ments in different application areas such as health care, maintenance, sports or smart homes. As a result, a large amount of sensor modalities were developed, different types of activity and context recognition services were implemented and the resulting systems were benchmarked using state-of-the-art eva...

  15. Authentication: From Passwords to Biometrics: An implementation of a speaker recognition system on Android

    OpenAIRE

    Heimark, Erlend

    2012-01-01

    We implement a biometric authentication system on the Android platform, which is based on text-dependent speaker recognition. The Android version used in the application is Android 4.0. The application makes use of the Modular Audio Recognition Framework, from which many of the algorithms are adapted in the processes of preprocessing and feature extraction. In addition, we employ the Dynamic Time Warping (DTW) algorithm for the comparison of different voice features. A training procedure is i...

  16. Extending the Capture Volume of an Iris Recognition System Using Wavefront Coding and Super-Resolution.

    Science.gov (United States)

    Hsieh, Sheng-Hsun; Li, Yung-Hui; Tien, Chung-Hao; Chang, Chin-Chen

    2016-12-01

    Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

  17. A Russian Keyword Spotting System Based on Large Vocabulary Continuous Speech Recognition and Linguistic Knowledge

    Directory of Open Access Journals (Sweden)

    Valentin Smirnov

    2016-01-01

    Full Text Available The paper describes the key concepts of a word spotting system for Russian based on large vocabulary continuous speech recognition. Key algorithms and system settings are described, including the pronunciation variation algorithm, and the experimental results on the real-life telecom data are provided. The description of system architecture and the user interface is provided. The system is based on CMU Sphinx open-source speech recognition platform and on the linguistic models and algorithms developed by Speech Drive LLC. The effective combination of baseline statistic methods, real-world training data, and the intensive use of linguistic knowledge led to a quality result applicable to industrial use.

  18. Spoof Detection for Finger-Vein Recognition System Using NIR Camera

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2017-10-01

    Full Text Available Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD, is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor based on the observations of the researchers about the difference between real (live and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR camera-based finger-vein recognition system using convolutional neural network (CNN to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA for dimensionality reduction of feature space and support vector machine (SVM for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared

  19. Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

    Science.gov (United States)

    Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

    2017-10-01

    Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN

  20. A Cooking Recipe Recommendation System with Visual Recognition of Food Ingredients

    Directory of Open Access Journals (Sweden)

    Keiji Yanai

    2014-04-01

    Full Text Available In this paper, we propose a cooking recipe recommendation system which runs on a consumer smartphone as an interactive mobile application. The proposed system employs real-time visual object recognition of food ingredients, and recommends cooking recipes related to the recognized food ingredients. Because of visual recognition, by only pointing a built-in camera on a smartphone to food ingredients, a user can get to know a related cooking recipes instantly. The objective of the proposed system is to assist people who cook to decide a cooking recipe at grocery stores or at a kitchen. In the current implementation, the system can recognize 30 kinds of food ingredient in 0.15 seconds, and it has achieved the 83.93% recognition rate within the top six candidates. By the user study, we confirmed the effectiveness of the proposed system.

  1. A Support System for the Electric Appliance Control Using Pose Recognition

    Science.gov (United States)

    Kawano, Takuya; Yamamoto, Kazuhiko; Kato, Kunihito; Hongo, Hitoshi

    In this paper, we propose an electric appliance control support system for aged and bedridden people using pose recognition. We proposed a pose recognition system that distinguishes between seven poses of the user on the bed. First, the face and arm regions of the user are detected by using the skin color. Our system focuses a recognition region surrounding the face region. Next, the higher order local autocorrelation features within the region are extracted. The linear discriminant analysis creates the coefficient matrix that can optimally distinguish among training data from the seven poses. Our algorithm can recognize the seven poses even if the subject wears different clothes and slightly shifts or slants on the bed. From the experimental results, our system achieved an accuracy rate of over 99 %. Then, we show that it possibles to construct one of a user-friendly system.

  2. Neuropeptide S interacts with the basolateral amygdala noradrenergic system in facilitating object recognition memory consolidation.

    Science.gov (United States)

    Han, Ren-Wen; Xu, Hong-Jiao; Zhang, Rui-San; Wang, Pei; Chang, Min; Peng, Ya-Li; Deng, Ke-Yu; Wang, Rui

    2014-01-01

    The noradrenergic activity in the basolateral amygdala (BLA) was reported to be involved in the regulation of object recognition memory. As the BLA expresses high density of receptors for Neuropeptide S (NPS), we investigated whether the BLA is involved in mediating NPS's effects on object recognition memory consolidation and whether such effects require noradrenergic activity. Intracerebroventricular infusion of NPS (1nmol) post training facilitated 24-h memory in a mouse novel object recognition task. The memory-enhancing effect of NPS could be blocked by the β-adrenoceptor antagonist propranolol. Furthermore, post-training intra-BLA infusions of NPS (0.5nmol/side) improved 24-h memory for objects, which was impaired by co-administration of propranolol (0.5μg/side). Taken together, these results indicate that NPS interacts with the BLA noradrenergic system in improving object recognition memory during consolidation. Copyright © 2013 Elsevier Inc. All rights reserved.

  3. Automated alignment system for optical wireless communication systems using image recognition.

    Science.gov (United States)

    Brandl, Paul; Weiss, Alexander; Zimmermann, Horst

    2014-07-01

    In this Letter, we describe the realization of a tracked line-of-sight optical wireless communication system for indoor data distribution. We built a laser-based transmitter with adaptive focus and ray steering by a microelectromechanical systems mirror. To execute the alignment procedure, we used a CMOS image sensor at the transmitter side and developed an algorithm for image recognition to localize the receiver's position. The receiver is based on a self-developed optoelectronic integrated chip with low requirements on the receiver optics to make the system economically attractive. With this system, we were able to set up the communication link automatically without any back channel and to perform error-free (bit error rate <10⁻⁹) data transmission over a distance of 3.5 m with a data rate of 3 Gbit/s.

  4. From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

    Science.gov (United States)

    Yildiz, Izzet B; von Kriegstein, Katharina; Kiebel, Stefan J

    2013-01-01

    Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

  5. From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

    Directory of Open Access Journals (Sweden)

    Izzet B Yildiz

    Full Text Available Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

  6. A multi-view face recognition system based on cascade face detector and improved Dlib

    Science.gov (United States)

    Zhou, Hongjun; Chen, Pei; Shen, Wei

    2018-03-01

    In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

  7. An Evolutionary Approach to Driving Tendency Recognition for Advanced Driver Assistance Systems

    Directory of Open Access Journals (Sweden)

    Lee Jong-Hyun

    2016-01-01

    Full Text Available Driving tendency recognition is important for constructing Advanced Driver Assistance Systems (ADAS. However, it had not been a lot of research using vehicle sensing data, due to the high difficulty to define it. In this paper, we attempt to improve the learning capability of a machine learning method using evolutionary computation. We propose a driving tendency recognition method, with consideration of data characteristics. Comparison of our classification system with conventional methods demonstrated the effectiveness and accuracy over 92% in our system. Our proposed evolutionary approach is confirmed that improve the classification accuracy of the learning method through evolution in the experiment.

  8. Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

    Science.gov (United States)

    Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

    We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

  9. Speech Recognition

    Directory of Open Access Journals (Sweden)

    Adrian Morariu

    2009-01-01

    Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

  10. A Robust and Device-Free System for the Recognition and Classification of Elderly Activities.

    Science.gov (United States)

    Li, Fangmin; Al-Qaness, Mohammed Abdulaziz Aide; Zhang, Yong; Zhao, Bihai; Luan, Xidao

    2016-12-01

    Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise.

  11. Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

    International Nuclear Information System (INIS)

    Gu Yu; Li Qiang

    2014-01-01

    We present a new pattern recognition system based on moving average and linear discriminant analysis (LDA), which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

  12. Introduction and Overview of the Vicens-Reddy Speech Recognition System.

    Science.gov (United States)

    Kameny, Iris; Ritea, H.

    The Vicens-Reddy System is unique in the sense that it approaches the problem of speech recognition as a whole, rather than treating particular aspects of the problems as in previous attempts. For example, where earlier systems treated only segmentation of speech into phoneme groups, or detected phonemes in a given context, the Vicens-Reddy System…

  13. A NEW STRATEGY FOR IMPROVING FEATURE SETS IN A DISCRETE HMM­BASED HANDWRITING RECOGNITION SYSTEM

    NARCIS (Netherlands)

    Grandidier, F.; Sabourin, R.; Suen, C.Y.; Gilloux, M.

    2004-01-01

    In this paper we introduce a new strategy for improving a discrete HMM­based handwriting recognition system, by integrating several information sources from specialized feature sets. For a given system, the basic idea is to keep the most discriminative features, and to replace the others with new

  14. Body posture recognition and turning recording system for the care of bed bound patients.

    Science.gov (United States)

    Hsiao, Rong-Shue; Mi, Zhenqiang; Yang, Bo-Ru; Kau, Lih-Jen; Bitew, Mekuanint Agegnehu; Li, Tzu-Yu

    2015-01-01

    This paper proposes body posture recognition and turning recording system for assisting the care of bed bound patients in nursing homes. The system continuously detects the patient's body posture and records the length of time for each body posture. If the patient remains in the same body posture long enough to develop pressure ulcers, the system notifies caregivers to change the patient's body posture. The objective of recording is to provide the log of body turning for querying of patients' family members. In order to accurately detect patient's body posture, we developed a novel pressure sensing pad which contains force sensing resistor sensors. Based on the proposed pressure sensing pad, we developed a bed posture recognition module which includes a bed posture recognition algorithm. The algorithm is based on fuzzy theory. The body posture recognition algorithm can detect the patient's bed posture whether it is right lateral decubitus, left lateral decubitus, or supine. The detected information of patient's body posture can be then transmitted to the server of healthcare center by the communication module to perform the functions of recording and notification. Experimental results showed that the average posture recognition accuracy for our proposed module is 92%.

  15. Impact of a voice recognition system on report cycle time and radiologist reading time

    Science.gov (United States)

    Melson, David L.; Brophy, Robert; Blaine, G. James; Jost, R. Gilbert; Brink, Gary S.

    1998-07-01

    Because of its exciting potential to improve clinical service, as well as reduce costs, a voice recognition system for radiological dictation was recently installed at our institution. This system will be clinically successful if it dramatically reduces radiology report turnaround time without substantially affecting radiologist dictation and editing time. This report summarizes an observer study currently under way in which radiologist reporting times using the traditional transcription system and the voice recognition system are compared. Four radiologists are observed interpreting portable intensive care unit (ICU) chest examinations at a workstation in the chest reading area. Data are recorded with the radiologists using the transcription system and using the voice recognition system. The measurements distinguish between time spent performing clerical tasks and time spent actually dictating the report. Editing time and the number of corrections made are recorded. Additionally, statistics are gathered to assess the voice recognition system's impact on the report cycle time -- the time from report dictation to availability of an edited and finalized report -- and the length of reports.

  16. A single-system model predicts recognition memory and repetition priming in amnesia.

    Science.gov (United States)

    Berry, Christopher J; Kessels, Roy P C; Wester, Arie J; Shanks, David R

    2014-08-13

    We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit. Copyright © 2014 the authors 0270-6474/14/3410963-12$15.00/0.

  17. Evaluation of iris recognition system for wavefront-guided laser in situ keratomileusis for myopic astigmatism.

    Science.gov (United States)

    Ghosh, Sudipta; Couper, Terry A; Lamoureux, Ecosse; Jhanji, Vishal; Taylor, Hugh R; Vajpayee, Rasik B

    2008-02-01

    To evaluate the visual and refractive outcomes of wavefront-guided laser in situ keratomileusis (LASIK) using an iris recognition system for the correction of myopic astigmatism. Centre for Eye Research Australia, Melbourne Excimer Laser Research Group, and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia. A comparative analysis of wavefront-guided LASIK was performed with an iris recognition system (iris recognition group) and without iris recognition (control group). The main parameters were uncorrected visual acuity (UCVA), best spectacle-corrected visual acuity, amount of residual cylinder, manifest spherical equivalent (SE), and the index of success using the Alpins method of astigmatism analysis 1 and 3 months postoperatively. A P value less than 0.05 was considered statistically significant. Preoperatively, the mean SE was -4.32 diopters (D) +/- 1.59 (SD) in the iris recognition group (100 eyes) and -4.55 +/- 1.87 D in the control group (98 eyes) (P = .84). At 3 months, the mean SE was -0.05 +/- 0.21 D and -0.20 +/- 0.40 D, respectively (P = .001), and an SE within +/-0.50 D of emmetropia was achieved in 92.0% and 85.7% of eyes, respectively (P = .07). At 3 months, the UCVA was 20/20 or better in 90.0% and 76.5% of eyes, respectively. A statistically significant difference in the amount of astigmatic correction was seen between the 2 groups (P = .00 and P = .01 at 1 and 3 months, respectively). The index of success was 98.0% in the iris recognition group and 81.6% in the control group (P = .03). Iris recognition software may achieve better visual and refractive outcomes in wavefront-guided LASIK for myopic astigmatism.

  18. Development of remote handling system based on 3-D shape recognition technique

    International Nuclear Information System (INIS)

    Tomizuka, Chiaki; Takeuchi, Yutaka

    2006-01-01

    In a nuclear facility, the maintenance and repair activities must be done remotely in a radioactive environment. Fuji Electric Systems Co., Ltd. has developed a remote handling system based on 3-D recognition technique. The system recognizes the pose and position of the target to manipulate, and visualizes the scene with the target in 3-D, enabling an operator to handle it easily. This paper introduces the concept and the key features of this system. (author)

  19. A Biometric Face Recognition System Using an Algorithm Based on the Principal Component Analysis Technique

    Directory of Open Access Journals (Sweden)

    Gheorghe Gîlcă

    2015-06-01

    Full Text Available This article deals with a recognition system using an algorithm based on the Principal Component Analysis (PCA technique. The recognition system consists only of a PC and an integrated video camera. The algorithm is developed in MATLAB language and calculates the eigenfaces considered as features of the face. The PCA technique is based on the matching between the facial test image and the training prototype vectors. The mathcing score between the facial test image and the training prototype vectors is calculated between their coefficient vectors. If the matching is high, we have the best recognition. The results of the algorithm based on the PCA technique are very good, even if the person looks from one side at the video camera.

  20. The nuclear fuel rod character recognition system based on neural network technique

    International Nuclear Information System (INIS)

    Kim, Woong-Ki; Park, Soon-Yong; Lee, Yong-Bum; Kim, Seung-Ho; Lee, Jong-Min; Chien, Sung-Il.

    1994-01-01

    The nuclear fuel rods should be discriminated and managed systematically by numeric characters which are printed at the end part of each rod in the process of producing fuel assembly. The characters are used to examine manufacturing process of the fuel rods in the inspection process of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies to establish automatic manufacturing process of fuel assembly. In the developed character recognition system, mesh feature set extracted from each character written in the fuel rod is employed to train a neural network based on back-propagation algorithm as a classifier for character recognition system. Performance evaluation has been achieved on a test set which is not included in a training character set. (author)

  1. Enrollment Time as a Requirement for Biometric Hand Recognition Systems

    OpenAIRE

    Carvalho, João; Sá, Vítor; Tenreiro de Magalhães, Sérgio; Santos, Henrique

    2015-01-01

    Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in...

  2. Automated Mulitple Object Optical Tracking and Recognition System, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — OPTRA proposes to develop an optical tracking system that is capable of recognizing and tracking up to 50 different objects within an approximately 2 degree x 3...

  3. an enhanced iris recognition and authentication system using ...

    African Journals Online (AJOL)

    Biu et al.

    1Department of Mathematical Sciences, Kaduna State University, Kaduna – Nigeria. (E-mail: ..... localization, the iris is in a circulation fashion then lastly, the image is saved into .... Conference on Computer Engineering Systems. Cleve, K.

  4. Dynamic combinatorial libraries: from exploring molecular recognition to systems chemistry.

    Science.gov (United States)

    Li, Jianwei; Nowak, Piotr; Otto, Sijbren

    2013-06-26

    Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.

  5. A heart disease recognition embedded system with fuzzy cluster algorithm.

    Science.gov (United States)

    de Carvalho, Helton Hugo; Moreno, Robson Luiz; Pimenta, Tales Cleber; Crepaldi, Paulo C; Cintra, Evaldo

    2013-06-01

    This article presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram - ECG signal processing by reducing the amount of data samples, without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson-Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicated common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database (EDB) as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan(®)-3A FPGA. The field programmable gate array (FPGA) implemented a Xilinx Microblaze(®) Soft-Core Processor running at a 50MHz clock rate. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  6. Motorcycle Start-stop System based on Intelligent Biometric Voice Recognition

    Science.gov (United States)

    Winda, A.; E Byan, W. R.; Sofyan; Armansyah; Zariantin, D. L.; Josep, B. G.

    2017-03-01

    Current mechanical key in the motorcycle is prone to bulgary, being stolen or misplaced. Intelligent biometric voice recognition as means to replace this mechanism is proposed as an alternative. The proposed system will decide whether the voice is belong to the user or not and the word utter by the user is ‘On’ or ‘Off’. The decision voice will be sent to Arduino in order to start or stop the engine. The recorded voice is processed in order to get some features which later be used as input to the proposed system. The Mel-Frequency Ceptral Coefficient (MFCC) is adopted as a feature extraction technique. The extracted feature is the used as input to the SVM-based identifier. Experimental results confirm the effectiveness of the proposed intelligent voice recognition and word recognition system. It show that the proposed method produces a good training and testing accuracy, 99.31% and 99.43%, respectively. Moreover, the proposed system shows the performance of false rejection rate (FRR) and false acceptance rate (FAR) accuracy of 0.18% and 17.58%, respectively. In the intelligent word recognition shows that the training and testing accuracy are 100% and 96.3%, respectively.

  7. An introduction to application-independent evaluation of speaker recognition systems

    NARCIS (Netherlands)

    Leeuwen, D.A. van; Brümmer, N.

    2007-01-01

    In the evaluation of speaker recognition systems - an important part of speaker classification [1], the trade-off between missed speakers and false alarms has always been an important diagnostic tool. NIST has defined the task of speaker detection with the associated Detection Cost Function (DCF) to

  8. Cherry Picking Robot Vision Recognition System Based on OpenCV

    Directory of Open Access Journals (Sweden)

    Zhang Qi Rong

    2016-01-01

    Full Text Available Through OpenCV function, the cherry in a natural environment image after image preprocessing, color recognition, threshold segmentation, morphological filtering, edge detection, circle Hough transform, you can draw the cherry’s center and circular contour, to carry out the purpose of the machine picking. The system is simple and effective.

  9. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  10. DEVELOPMENT OF AUTOMATED SPEECH RECOGNITION SYSTEM FOR EGYPTIAN ARABIC PHONE CONVERSATIONS

    Directory of Open Access Journals (Sweden)

    A. N. Romanenko

    2016-07-01

    Full Text Available The paper deals with description of several speech recognition systems for the Egyptian Colloquial Arabic. The research is based on the CALLHOME Egyptian corpus. The description of both systems, classic: based on Hidden Markov and Gaussian Mixture Models, and state-of-the-art: deep neural network acoustic models is given. We have demonstrated the contribution from the usage of speaker-dependent bottleneck features; for their extraction three extractors based on neural networks were trained. For their training three datasets in several languageswere used:Russian, English and differentArabic dialects.We have studied the possibility of application of a small Modern Standard Arabic (MSA corpus to derive phonetic transcriptions. The experiments have shown that application of the extractor obtained on the basis of the Russian dataset enables to increase significantly the quality of the Arabic speech recognition. We have also stated that the usage of phonetic transcriptions based on modern standard Arabic decreases recognition quality. Nevertheless, system operation results remain applicable in practice. In addition, we have carried out the study of obtained models application for the keywords searching problem solution. The systems obtained demonstrate good results as compared to those published before. Some ways to improve speech recognition are offered.

  11. Predicting Performance of a Face Recognition System Based on Image Quality

    NARCIS (Netherlands)

    Dutta, A.

    2015-01-01

    In this dissertation, we focus on several aspects of models that aim to predict performance of a face recognition system. Performance prediction models are commonly based on the following two types of performance predictor features: a) image quality features; and b) features derived solely from

  12. A Cross-Layer Biometric Recognition System for Mobile IoT Devices

    Directory of Open Access Journals (Sweden)

    Shayan Taheri

    2018-02-01

    Full Text Available A biometric recognition system is one of the leading candidates for the current and the next generation of smart visual systems. The visual system is the engine of the surveillance cameras that have great importance for intelligence and security purposes. These surveillance devices can be a target of adversaries for accomplishing various malicious scenarios such as disabling the camera in critical times or the lack of recognition of a criminal. In this work, we propose a cross-layer biometric recognition system that has small computational complexity and is suitable for mobile Internet of Things (IoT devices. Furthermore, due to the involvement of both hardware and software in realizing this system in a decussate and chaining structure, it is easier to locate and provide alternative paths for the system flow in the case of an attack. For security analysis of this system, one of the elements of this system named the advanced encryption standard (AES is infected by four different Hardware Trojansthat target different parts of this module. The purpose of these Trojans is to sabotage the biometric data that are under process by the biometric recognition system. All of the software and the hardware modules of this system are implemented using MATLAB and Verilog HDL, respectively. According to the performance evaluation results, the system shows an acceptable performance in recognizing healthy biometric data. It is able to detect the infected data, as well. With respect to its hardware results, the system may not contribute significantly to the hardware design parameters of a surveillance camera considering all the hardware elements within the device.

  13. USE OF FACIAL EMOTION RECOGNITION IN E-LEARNING SYSTEMS

    Directory of Open Access Journals (Sweden)

    Uğur Ayvaz

    2017-09-01

    Full Text Available Since the personal computer usage and internet bandwidth are increasing, e-learning systems are also widely spreading. Although e-learning has some advantages in terms of information accessibility, time and place flexibility compared to the formal learning, it does not provide enough face-to-face interactivity between an educator and learners. In this study, we are proposing a hybrid information system, which is combining computer vision and machine learning technologies for visual and interactive e-learning systems. The proposed information system detects emotional states of the learners and gives feedback to an educator about their instant and weighted emotional states based on facial expressions. In this way, the educator will be aware of the general emotional state of the virtual classroom and the system will create a formal learning-like interactive environment. Herein, several classification algorithms were applied to learn instant emotional state and the best accuracy rates were obtained using kNN and SVM algorithms.

  14. High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

    Science.gov (United States)

    Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

    2018-04-01

    An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

  15. System certification progress in concept recognition in IAEA regulation

    International Nuclear Information System (INIS)

    Luna, R.E.; Pollog, T.

    1995-01-01

    System Certification is a regulatory concept which is intended to expand the scope of radioactive material transport regulations by allowing alternative means for proving compliance with the requisite standards of safety set out in transport regulations. In practice it may allow more stringent requirements in one aspect of the regulations to be substituted for less stringent application in other areas so long as the safety standard provided by regulation is preserved. The concept is widely perceived as the imposition of operational controls in exchange for relaxation of packaging standards, but that is only one possibility in the spectrum of potential actions under a System Certification provision in IAEA or national regulations

  16. Hardware/Software Co-Design of a Traffic Sign Recognition System Using Zynq FPGAs

    Directory of Open Access Journals (Sweden)

    Yan Han

    2015-12-01

    Full Text Available Traffic sign recognition (TSR, taken as an important component of an intelligent vehicle system, has been an emerging research topic in recent years. In this paper, a traffic sign detection system based on color segmentation, speeded-up robust features (SURF detection and the k-nearest neighbor classifier is introduced. The proposed system benefits from the SURF detection algorithm, which achieves invariance to rotated, skewed and occluded signs. In addition to the accuracy and robustness issues, a TSR system should target a real-time implementation on an embedded system. Therefore, a hardware/software co-design architecture for a Zynq-7000 FPGA is presented as a major objective of this work. The sign detection operations are accelerated by programmable hardware logic that searches the potential candidates for sign classification. Sign recognition and classification uses a feature extraction and matching algorithm, which is implemented as a software component that runs on the embedded ARM CPU.

  17. A simple and efficient optical character recognition system for basic ...

    Indian Academy of Sciences (India)

    are on the way for the development of efficient OCR systems for Indian languages, .... Each vowel has a vowel sign (modifier) and each consonant has a basic form (prim- itive). ..... as a single class of character in the first stage of classification.

  18. BRAF inhibition improves tumor recognition by the immune system

    DEFF Research Database (Denmark)

    Donia, Marco; Fagone, Paolo; Nicoletti, Ferdinando

    2012-01-01

    to be poorly efficient. By characterizing the immunological interactions between T cells and cancer cells in clinical material as well as the influence of the FDA-approved BRAF inhibitor vemurafenib on the immune system, we aimed at unraveling new strategies to expand the efficacy of adoptive T-cell transfer...

  19. AN EFFICIENT SELF-UPDATING FACE RECOGNITION SYSTEM FOR PLASTIC SURGERY FACE

    Directory of Open Access Journals (Sweden)

    A. Devi

    2016-08-01

    Full Text Available Facial recognition system is fundamental a computer application for the automatic identification of a person through a digitized image or a video source. The major cause for the overall poor performance is related to the transformations in appearance of the user based on the aspects akin to ageing, beard growth, sun-tan etc. In order to overcome the above drawback, Self-update process has been developed in which, the system learns the biometric attributes of the user every time the user interacts with the system and the information gets updated automatically. The procedures of Plastic surgery yield a skilled and endurable means of enhancing the facial appearance by means of correcting the anomalies in the feature and then treating the facial skin with the aim of getting a youthful look. When plastic surgery is performed on an individual, the features of the face undergo reconstruction either locally or globally. But, the changes which are introduced new by plastic surgery remain hard to get modeled by the available face recognition systems and they deteriorate the performances of the face recognition algorithm. Hence the Facial plastic surgery produces changes in the facial features to larger extent and thereby creates a significant challenge to the face recognition system. This work introduces a fresh Multimodal Biometric approach making use of novel approaches to boost the rate of recognition and security. The proposed method consists of various processes like Face segmentation using Active Appearance Model (AAM, Face Normalization using Kernel Density Estimate/ Point Distribution Model (KDE-PDM, Feature extraction using Local Gabor XOR Patterns (LGXP and Classification using Independent Component Analysis (ICA. Efficient techniques have been used in each phase of the FRAS in order to obtain improved results.

  20. Vision-based obstacle recognition system for automated lawn mower robot development

    Science.gov (United States)

    Mohd Zin, Zalhan; Ibrahim, Ratnawati

    2011-06-01

    Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

  1. A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition

    Directory of Open Access Journals (Sweden)

    Shahram Najam

    2018-01-01

    Full Text Available A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy than single parameter identification method. The facial recognition system uses Viola-Jones algorithm along with rectangular Haar feature selection method for detection and extraction of features to develop a biometric template and for feature extraction during the voting process. Cascaded machine learning based classifiers are used for comparing the features for identity verification using GPCA (Generalized Principle Component Analysis and K-NN (K-Nearest Neighbor. It is accomplished through comparing the Eigen-vectors of the extracted features with the biometric template pre-stored in the election regulatory body database. The results of the proposed system show that the proposed cascaded design based system performs better than the systems using other classifiers or separate schemes i.e. facial or finger print based schemes. The proposed system will be highly useful for real time applications due to the reason that it has 91% accuracy under nominal light in terms of facial recognition.

  2. A novel hybrid biometric electronic voting system: integrating finger print face recognition

    International Nuclear Information System (INIS)

    Najam, S.S.; Shaikh, A.Z.; Naqvi, S.

    2018-01-01

    A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy than single parameter identification method. The facial recognition system uses Viola-Jones algorithm along with rectangular Haar feature selection method for detection and extraction of features to develop a biometric template and for feature extraction during the voting process. Cascaded machine learning based classifiers are used for comparing the features for identity verification using GPCA (Generalized Principle Component Analysis) and K-NN (K-Nearest Neighbor). It is accomplished through comparing the Eigen-vectors of the extracted features with the biometric template pre-stored in the election regulatory body database. The results of the proposed system show that the proposed cascaded design based system performs better than the systems using other classifiers or separate schemes i.e. facial or finger print based schemes. The proposed system will be highly useful for real time applications due to the reason that it has 91% accuracy under nominal light in terms of facial recognition. (author)

  3. Two-step calibration method for multi-algorithm score-based face recognition systems by minimizing discrimination loss

    NARCIS (Netherlands)

    Susyanto, N.; Veldhuis, R.N.J.; Spreeuwers, L.J.; Klaassen, C.A.J.; Fierrez, J.; Li, S.Z.; Ross, A.; Veldhuis, R.; Alonso-Fernandez, F.; Bigun, J.

    2016-01-01

    We propose a new method for combining multi-algorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its

  4. The MITLL NIST LRE 2015 Language Recognition System

    Science.gov (United States)

    2016-05-06

    Cluster Target Classes Arabic Egyptian , Iraqi, Levantine, Maghrebi, Modern Standard Chinese Cantonese, Mandarin, Min, Wu English...42.69 Egyptian (ara-arz) 440 97.27 British English (eng-gbr) 47 0.51 Indian English (eng-sas) 418 7.82 American English (eng-usg) 428 100.37...are obtained by training a Deep Neural Network (DNN) using a seven hidden layer architecture . On these systems, all hidden layers have 1024 nodes

  5. Developing a broadband automatic speech recognition system for Afrikaans

    CSIR Research Space (South Africa)

    De Wet, Febe

    2011-08-01

    Full Text Available baseline transcription for the news data. The match between a baseline transcription and its corre- sponding audio can be evaluated automatically using an ASR system in forced alignment mode. Only those bulletins for which a bad match is indicated... Component Index for data [3]. occurrence of Afrikaans words3. Other text corpora that are currently under construction in- clude daily downloads of the scripts of news bulletins that are read on an Afrikaans radio station as well as transcripts of par...

  6. Involvement of the intrinsic/default system in movement-related self recognition.

    Science.gov (United States)

    Salomon, Roy; Malach, Rafael; Lamy, Dominique

    2009-10-21

    The question of how people recognize themselves and separate themselves from the environment and others has long intrigued philosophers and scientists. Recent findings have linked regions of the 'default brain' or 'intrinsic system' to self-related processing. We used a paradigm in which subjects had to rely on subtle sensory-motor synchronization differences to determine whether a viewed movement belonged to them or to another person, while stimuli and task demands associated with the "responded self" and "responded other" conditions were precisely matched. Self recognition was associated with enhanced brain activity in several ROIs of the intrinsic system, whereas no differences emerged within the extrinsic system. This self-related effect was found even in cases where the sensory-motor aspects were precisely matched. Control conditions ruled out task difficulty as the source of the differential self-related effects. The findings shed light on the neural systems underlying bodily self recognition.

  7. Performance Evaluation of Speech Recognition Systems as a Next-Generation Pilot-Vehicle Interface Technology

    Science.gov (United States)

    Arthur, Jarvis J., III; Shelton, Kevin J.; Prinzel, Lawrence J., III; Bailey, Randall E.

    2016-01-01

    During the flight trials known as Gulfstream-V Synthetic Vision Systems Integrated Technology Evaluation (GV-SITE), a Speech Recognition System (SRS) was used by the evaluation pilots. The SRS system was intended to be an intuitive interface for display control (rather than knobs, buttons, etc.). This paper describes the performance of the current "state of the art" Speech Recognition System (SRS). The commercially available technology was evaluated as an application for possible inclusion in commercial aircraft flight decks as a crew-to-vehicle interface. Specifically, the technology is to be used as an interface from aircrew to the onboard displays, controls, and flight management tasks. A flight test of a SRS as well as a laboratory test was conducted.

  8. NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

    Science.gov (United States)

    Mezgec, Simon; Koroušić Seljak, Barbara

    2017-06-27

    Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

  9. An analog VLSI real time optical character recognition system based on a neural architecture

    International Nuclear Information System (INIS)

    Bo, G.; Caviglia, D.; Valle, M.

    1999-01-01

    In this paper a real time Optical Character Recognition system is presented: it is based on a feature extraction module and a neural network classifier which have been designed and fabricated in analog VLSI technology. Experimental results validate the circuit functionality. The results obtained from a validation based on a mixed approach (i.e., an approach based on both experimental and simulation results) confirm the soundness and reliability of the system

  10. An analog VLSI real time optical character recognition system based on a neural architecture

    Energy Technology Data Exchange (ETDEWEB)

    Bo, G.; Caviglia, D.; Valle, M. [Genoa Univ. (Italy). Dip. of Biophysical and Electronic Engineering

    1999-03-01

    In this paper a real time Optical Character Recognition system is presented: it is based on a feature extraction module and a neural network classifier which have been designed and fabricated in analog VLSI technology. Experimental results validate the circuit functionality. The results obtained from a validation based on a mixed approach (i.e., an approach based on both experimental and simulation results) confirm the soundness and reliability of the system.

  11. Foundations for a syntatic pattern recognition system for genomic DNA sequences

    Energy Technology Data Exchange (ETDEWEB)

    Searles, D.B.

    1993-03-01

    The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

  12. Face Prediction Model for an Automatic Age-invariant Face Recognition System

    OpenAIRE

    Yadav, Poonam

    2015-01-01

    07.11.14 KB. Emailed author re copyright. Author says that copyright is retained by author. Ok to add to spiral Automated face recognition and identi cation softwares are becoming part of our daily life; it nds its abode not only with Facebooks auto photo tagging, Apples iPhoto, Googles Picasa, Microsofts Kinect, but also in Homeland Security Departments dedicated biometric face detection systems. Most of these automatic face identification systems fail where the e ects of aging come into...

  13. Security and matching of partial fingerprint recognition systems

    Science.gov (United States)

    Jea, Tsai-Yang; Chavan, Viraj S.; Govindaraju, Venu; Schneider, John K.

    2004-08-01

    Despite advances in fingerprint identification techniques, matching incomplete or partial fingerprints still poses a difficult challenge. While the introduction of compact silicon chip-based sensors that capture only a part of the fingerprint area have made this problem important from a commercial perspective, there is also considerable interest on the topic for processing partial and latent fingerprints obtained at crime scenes. Attempts to match partial fingerprints using singular ridge structures-based alignment techniques fail when the partial print does not include such structures (e.g., core or delta). We present a multi-path fingerprint matching approach that utilizes localized secondary features derived using only the relative information of minutiae. Since the minutia-based fingerprint representation, is an ANSI-NIST standard, our approach has the advantage of being directly applicable to already existing databases. We also analyze the vulnerability of partial fingerprint identification systems to brute force attacks. The described matching approach has been tested on one of FVC2002"s DB1 database11. The experimental results show that our approach achieves an equal error rate of 1.25% and a total error rate of 1.8% (with FAR at 0.2% and FRR at 1.6%).

  14. Facial Emotion Recognition System – A Machine Learning Approach

    Science.gov (United States)

    Ramalingam, V. V.; Pandian, A.; Jayakumar, Lavanya

    2018-04-01

    Frown is a medium for people correlation and it could be exercised in multiple real systems. Single crucial stage for frown realizing is to exactly select hysterical aspects. This journal proposed a frown realization scheme applying transformative Particle Swarm Optimization (PSO) based aspect accumulation. This entity initially employs changed LVP, handles crisscross adjacent picture element contrast, for achieving the selective first frown portrayal. Then the PSO entity inserted with a concept of micro Genetic Algorithm (mGA) called mGA-embedded PSO designed for achieving aspect accumulation. This study, the technique subsumes no disposable memory, a little-populace insignificant flock, a latest acceleration that amends with the approach and a sub dimension-based in-depth local frown aspect examines. Assistance of provincial utilization and comprehensive inspection examine structure of alleviating of an immature concurrence complication of conventional PSO. Numerous identifiers are used to diagnose different frown expositions. Stationed on extensive study within and other-sphere pictures from the continued Cohn Kanade and MMI benchmark directory appropriately. Determination of the application exceeds most advanced level PSO variants, conventional PSO, classical GA and alternate relevant frown realization structures is described with powerful limit. Extending our accession to a motion based FER application for connecting patch-based Gabor aspects with continuous data in multi-frames.

  15. Pattern-recognition system application to EBR-II plant-life extension

    International Nuclear Information System (INIS)

    King, R.W.; Radtke, W.H.; Mott, J.E.

    1988-01-01

    A computer-based pattern-recognition system, the System State Analyzer (SSA), is being used as part of the EBR-II plant-life extension program for detection of degradation and other abnormalities in plant systems. The SSA is used for surveillance of the EBR-II primary system instrumentation, primary sodium pumps, and plant heat balances. Early results of this surveillance indicate that the SSA can detect instrumentation degradation and system performance degradation over varying time intervals, and can provide derived signal values to replace signals from failed critical sensors. These results are being used in planning for extended-life operation of EBR-II

  16. Pattern-recognition software detecting the onset of failures in complex systems

    International Nuclear Information System (INIS)

    Mott, J.; King, R.

    1987-01-01

    A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself

  17. An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

    International Nuclear Information System (INIS)

    Avci, E.

    2007-01-01

    In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

  18. Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

    Science.gov (United States)

    Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

    2011-01-01

    One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

  19. A field study of the accuracy and reliability of a biometric iris recognition system.

    Science.gov (United States)

    Latman, Neal S; Herb, Emily

    2013-06-01

    The iris of the eye appears to satisfy the criteria for a good anatomical characteristic for use in a biometric system. The purpose of this study was to evaluate a biometric iris recognition system: Mobile-Eyes™. The enrollment, verification, and identification applications were evaluated in a field study for accuracy and reliability using both irises of 277 subjects. Independent variables included a wide range of subject demographics, ambient light, and ambient temperature. A sub-set of 35 subjects had alcohol-induced nystagmus. There were 2710 identification and verification attempts, which resulted in 1,501,340 and 5540 iris comparisons respectively. In this study, the system successfully enrolled all subjects on the first attempt. All 277 subjects were successfully verified and identified on the first day of enrollment. None of the current or prior eye conditions prevented enrollment, verification, or identification. All 35 subjects with alcohol-induced nystagmus were successfully verified and identified. There were no false verifications or false identifications. Two conditions were identified that potentially could circumvent the use of iris recognitions systems in general. The Mobile-Eyes™ iris recognition system exhibited accurate and reliable enrollment, verification, and identification applications in this study. It may have special applications in subjects with nystagmus. Copyright © 2012 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

  20. A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

    Directory of Open Access Journals (Sweden)

    Yuanhong Zhong

    2018-05-01

    Full Text Available Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO object detection, the classification method and fine counting based on Support Vector Machines (SVM using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.

  1. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

    Science.gov (United States)

    Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

    2016-01-01

    The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

  2. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

    Directory of Open Access Journals (Sweden)

    Enea Cippitelli

    2016-01-01

    Full Text Available The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

  3. Architecture of top down, parallel pattern recognition system TOPS and its application to the MR head images

    International Nuclear Information System (INIS)

    Matsunoshita, Jun-ichi; Akamatsu, Shigeo; Yamamoto, Shinji.

    1993-01-01

    This paper describes about the system architecture of a new image recognition system TOPS (top-down parallel pattern recognition system), and its application to the automatic extraction of brain organs (cerebrum, cerebellum, brain stem) from 3D-MRI images. Main concepts of TOPS are as follows: (1) TOPS is the top-down type recognition system, which allows parallel models in each level of hierarchy structure. (2) TOPS allows parallel image processing algorithms for one purpose (for example, for extraction of one special organ). This results in multiple candidates for one purpose, and judgment to get unique solution for it will be made at upper level of hierarchy structure. (author)

  4. Development of Portable Automatic Number Plate Recognition System on Android Mobile Phone

    Science.gov (United States)

    Mutholib, Abdul; Gunawan, Teddy S.; Chebil, Jalel; Kartiwi, Mira

    2013-12-01

    The Automatic Number Plate Recognition (ANPR) System has performed as the main role in various access control and security, such as: tracking of stolen vehicles, traffic violations (speed trap) and parking management system. In this paper, the portable ANPR implemented on android mobile phone is presented. The main challenges in mobile application are including higher coding efficiency, reduced computational complexity, and improved flexibility. Significance efforts are being explored to find suitable and adaptive algorithm for implementation of ANPR on mobile phone. ANPR system for mobile phone need to be optimize due to its limited CPU and memory resources, its ability for geo-tagging image captured using GPS coordinates and its ability to access online database to store the vehicle's information. In this paper, the design of portable ANPR on android mobile phone will be described as follows. First, the graphical user interface (GUI) for capturing image using built-in camera was developed to acquire vehicle plate number in Malaysia. Second, the preprocessing of raw image was done using contrast enhancement. Next, character segmentation using fixed pitch and an optical character recognition (OCR) using neural network were utilized to extract texts and numbers. Both character segmentation and OCR were using Tesseract library from Google Inc. The proposed portable ANPR algorithm was implemented and simulated using Android SDK on a computer. Based on the experimental results, the proposed system can effectively recognize the license plate number at 90.86%. The required processing time to recognize a license plate is only 2 seconds on average. The result is consider good in comparison with the results obtained from previous system that was processed in a desktop PC with the range of result from 91.59% to 98% recognition rate and 0.284 second to 1.5 seconds recognition time.

  5. Object Recognition System in Remote Controlled Weapon Station using SIFT and SURF Methods

    Directory of Open Access Journals (Sweden)

    Midriem Mirdanies

    2013-12-01

    Full Text Available Object recognition system using computer vision that is implemented on Remote Controlled Weapon Station (RCWS is discussed. This system will make it easier to identify and shoot targeted object automatically. Algorithm was created to recognize real time multiple objects using two methods i.e. Scale Invariant Feature Transform (SIFT and Speeded Up Robust Features (SURF combined with K-Nearest Neighbors (KNN and Random Sample Consensus (RANSAC for verification. The algorithm is designed to improve object detection to be more robust and to minimize the processing time required. Objects are registered on the system consisting of the armored personnel carrier, tanks, bus, sedan, big foot, and police jeep. In addition, object selection can use mouse to shoot another object that has not been registered on the system. Kinect™ is used to capture RGB images and to find the coordinates x, y, and z of the object. The programming language used is C with visual studio IDE 2010 and opencv libraries. Object recognition program is divided into three parts: 1 reading image from kinect™ and simulation results, 2 object recognition process, and 3 transfer of the object data to the ballistic computer. Communication between programs is performed using shared memory. The detected object data is sent to the ballistic computer via Local Area Network (LAN using winsock for ballistic calculation, and then the motor control system moves the direction of the weapon model to the desired object. The experimental results show that the SIFT method is more suitable because more accurate and faster than SURF with the average processing time to detect one object is 430.2 ms, two object is 618.4 ms, three objects is 682.4 ms, and four objects is 756.2 ms. Object recognition program is able to recognize multi-objects and the data of the identified object can be processed by the ballistic computer in realtime.

  6. Camera-laser fusion sensor system and environmental recognition for humanoids in disaster scenarios

    International Nuclear Information System (INIS)

    Lee, Inho; Oh, Jaesung; Oh, Jun-Ho; Kim, Inhyeok

    2017-01-01

    This research aims to develop a vision sensor system and a recognition algorithm to enable a humanoid to operate autonomously in a disaster environment. In disaster response scenarios, humanoid robots that perform manipulation and locomotion tasks must identify the objects in the environment from those challenged by the call by the United States’ Defense Advanced Research Projects Agency, e.g., doors, valves, drills, debris, uneven terrains, and stairs, among others. In order for a humanoid to undertake a number of tasks, we con- struct a camera–laser fusion system and develop an environmental recognition algorithm. Laser distance sensor and motor are used to obtain 3D cloud data. We project the 3D cloud data onto a 2D image according to the intrinsic parameters of the camera and the distortion model of the lens. In this manner, our fusion sensor system performs functions such as those performed by the RGB-D sensor gener- ally used in segmentation research. Our recognition algorithm is based on super-pixel segmentation and random sampling. The proposed approach clusters the unorganized cloud data according to geometric characteristics, namely, proximity and co-planarity. To assess the feasibility of our system and algorithm, we utilize the humanoid robot, DRC-HUBO, and the results are demonstrated in the accompanying video.

  7. Camera-laser fusion sensor system and environmental recognition for humanoids in disaster scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Inho [Institute for Human and Machine Cognition (IHMC), Florida (United States); Oh, Jaesung; Oh, Jun-Ho [Korea Advanced Institute of Science and Technology (KAIST), Daejeon (Korea, Republic of); Kim, Inhyeok [NAVER Green Factory, Seongnam (Korea, Republic of)

    2017-06-15

    This research aims to develop a vision sensor system and a recognition algorithm to enable a humanoid to operate autonomously in a disaster environment. In disaster response scenarios, humanoid robots that perform manipulation and locomotion tasks must identify the objects in the environment from those challenged by the call by the United States’ Defense Advanced Research Projects Agency, e.g., doors, valves, drills, debris, uneven terrains, and stairs, among others. In order for a humanoid to undertake a number of tasks, we con- struct a camera–laser fusion system and develop an environmental recognition algorithm. Laser distance sensor and motor are used to obtain 3D cloud data. We project the 3D cloud data onto a 2D image according to the intrinsic parameters of the camera and the distortion model of the lens. In this manner, our fusion sensor system performs functions such as those performed by the RGB-D sensor gener- ally used in segmentation research. Our recognition algorithm is based on super-pixel segmentation and random sampling. The proposed approach clusters the unorganized cloud data according to geometric characteristics, namely, proximity and co-planarity. To assess the feasibility of our system and algorithm, we utilize the humanoid robot, DRC-HUBO, and the results are demonstrated in the accompanying video.

  8. A Kinect based sign language recognition system using spatio-temporal features

    Science.gov (United States)

    Memiş, Abbas; Albayrak, Songül

    2013-12-01

    This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.

  9. Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-01-01

    Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

  10. A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data

    Directory of Open Access Journals (Sweden)

    Alessandro Manzi

    2017-05-01

    Full Text Available Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM, trained with Sequential Minimal Optimization (SMO. The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60 and the Telecommunication Systems Team (TST Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

  11. A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

    Science.gov (United States)

    Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

    2017-05-11

    Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

  12. Compact holographic optical neural network system for real-time pattern recognition

    Science.gov (United States)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  13. Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model

    Science.gov (United States)

    Jin, Jing; Wang, Yuanqing; Xu, Liujing; Cao, Liqun; Han, Lei; Zhou, Biye; Li, Minggao

    2014-11-01

    A non-intrusive gesture recognition human-machine interaction system is proposed in this paper. In order to solve the hand positioning problem which is a difficulty in current algorithms, face detection is used for the pre-processing to narrow the search area and find user's hand quickly and accurately. Hidden Markov Model (HMM) is used for gesture recognition. A certain number of basic gesture units are trained as HMM models. At the same time, an improved 8-direction feature vector is proposed and used to quantify characteristics in order to improve the detection accuracy. The proposed system can be applied in interaction equipments without special training for users, such as household interactive television

  14. Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    C. S. Chin

    2017-01-01

    Full Text Available The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if “grooming” is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google’s Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.

  15. Implementing an excellence in teaching recognition system: needs analysis and recommendations.

    Science.gov (United States)

    Schindler, Nancy; Corcoran, Julia C; Miller, Megan; Wang, Chih-Hsiung; Roggin, Kevin; Posner, Mitchell; Fryer, Jonathan; DaRosa, Debra A

    2013-01-01

    Teaching awards have been suggested to serve a variety of purposes. The specific characteristics of teaching awards and the associated effectiveness at achieving planned purposes are poorly understood. A needs analysis was performed to inform recommendations for an Excellence in Teaching Recognition System to meet the needs of surgical education leadership. We performed a 2-part needs analysis beginning with a review of the literature. We then, developed, piloted, and administered a survey instrument to General Surgery program leaders. The survey examined the features and perceived effectiveness of existing teaching awards systems. A multi-institution committee of program directors, clerkship directors, and Vice-Chairs of education then met to identify goals and develop recommendations for implementation of an "Excellence in Teaching Recognition System." There is limited evidence demonstrating effectiveness of existing teaching awards in medical education. Evidence supports the ability of such awards to demonstrate value placed on teaching, to inspire faculty to teach, and to contribute to promotion. Survey findings indicate that existing awards strive to achieve these purposes and that educational leaders believe awards have the potential to do this and more. Leaders are moderately satisfied with existing awards for providing recognition and demonstrating value placed on teaching, but they are less satisfied with awards for motivating faculty to participate in teaching or for contributing to promotion. Most departments and institutions honor only a few recipients annually. There is a paucity of literature addressing teaching recognition systems in medical education and little evidence to support the success of such systems in achieving their intended purposes. The ability of awards to affect outcomes such as participation in teaching and promotion may be limited by the small number of recipients for most existing awards. We propose goals for a Teaching Recognition

  16. All-organic microelectromechanical systems integrating specific molecular recognition--a new generation of chemical sensors.

    Science.gov (United States)

    Ayela, Cédric; Dubourg, Georges; Pellet, Claude; Haupt, Karsten

    2014-09-03

    Cantilever-type all-organic microelectromechanical systems based on molecularly imprinted polymers for specific analyte recognition are used as chemical sensors. They are produced by a simple spray-coating-shadow-masking process. Analyte binding to the cantilever generates a measurable change in its resonance frequency. This allows label-free detection by direct mass sensing of low-molecular-weight analytes at nanomolar concentrations. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Practising verbal maritime communication with computer dialogue systems using automatic speech recognition (My Practice session)

    OpenAIRE

    John, Peter; Wellmann, J.; Appell, J.E.

    2016-01-01

    This My Practice session presents a novel online tool for practising verbal communication in a maritime setting. It is based on low-fi ChatBot simulation exercises which employ computer-based dialogue systems. The ChatBot exercises are equipped with an automatic speech recognition engine specifically designed for maritime communication. The speech input and output functionality enables learners to communicate with the computer freely and spontaneously. The exercises replicate real communicati...

  18. Multimodal Biometric System Based on the Recognition of Face and Both Irises

    Directory of Open Access Journals (Sweden)

    Yeong Gon Kim

    2012-09-01

    Full Text Available The performance of unimodal biometric systems (based on a single modality such as face or fingerprint has to contend with various problems, such as illumination variation, skin condition and environmental conditions, and device variations. Therefore, multimodal biometric systems have been used to overcome the limitations of unimodal biometrics and provide high accuracy recognition. In this paper, we propose a new multimodal biometric system based on score level fusion of face and both irises' recognition. Our study has the following novel features. First, the device proposed acquires images of the face and both irises simultaneously. The proposed device consists of a face camera, two iris cameras, near-infrared illuminators and cold mirrors. Second, fast and accurate iris detection is based on two circular edge detections, which are accomplished in the iris image on the basis of the size of the iris detected in the face image. Third, the combined accuracy is enhanced by combining each score for the face and both irises using a support vector machine. The experimental results show that the equal error rate for the proposed method is 0.131%, which is lower than that of face or iris recognition and other fusion methods.

  19. Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

    Science.gov (United States)

    Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

    2018-04-01

    To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

  20. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  1. Face Detection and Recognition

    National Research Council Canada - National Science Library

    Jain, Anil K

    2004-01-01

    This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

  2. Evaluating a voice recognition system: finding the right product for your department.

    Science.gov (United States)

    Freeh, M; Dewey, M; Brigham, L

    2001-06-01

    The Department of Radiology at the University of Utah Health Sciences Center has been in the process of transitioning from the traditional film-based department to a digital imaging department for the past 2 years. The department is now transitioning from the traditional method of dictating reports (dictation by radiologist to transcription to review and signing by radiologist) to a voice recognition system. The transition to digital operations will not be complete until we have the ability to directly interface the dictation process with the image review process. Voice recognition technology has advanced to the level where it can and should be an integral part of the new way of working in radiology and is an integral part of an efficient digital imaging department. The transition to voice recognition requires the task of identifying the product and the company that will best meet a department's needs. This report introduces the methods we used to evaluate the vendors and the products available as we made our purchasing decision. We discuss our evaluation method and provide a checklist that can be used by other departments to assist with their evaluation process. The criteria used in the evaluation process fall into the following major categories: user operations, technical infrastructure, medical dictionary, system interfaces, service support, cost, and company strength. Conclusions drawn from our evaluation process will be detailed, with the intention being to shorten the process for others as they embark on a similar venture. As more and more organizations investigate the many products and services that are now being offered to enhance the operations of a radiology department, it becomes increasingly important that solid methods are used to most effectively evaluate the new products. This report should help others complete the task of evaluating a voice recognition system and may be adaptable to other products as well.

  3. Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

    Science.gov (United States)

    Winda, A.; Sofyan; Sthevany; Vincent, R. S.

    2017-12-01

    Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

  4. IoT-Based Image Recognition System for Smart Home-Delivered Meal Services

    Directory of Open Access Journals (Sweden)

    Hsiao-Ting Tseng

    2017-07-01

    Full Text Available Population ageing is an important global issue. The Taiwanese government has used various Internet of Things (IoT applications in the “10-year long-term care program 2.0”. It is expected that the efficiency and effectiveness of long-term care services will be improved through IoT support. Home-delivered meal services for the elderly are important for home-based long-term care services. To ensure that the right meals are delivered to the right recipient at the right time, the runners need to take a picture of the meal recipient when the meal is delivered. This study uses the IoT-based image recognition system to design an integrated service to improve the management of image recognition. The core technology of this IoT-based image recognition system is statistical histogram-based k-means clustering for image segmentation. However, this method is time-consuming. Therefore, we proposed using the statistical histogram to obtain a probability density function of pixels of a figure and segmenting these with weighting for the same intensity. This aims to increase the computational performance and achieve the same results as k-means clustering. We combined histogram and k-means clustering in order to overcome the high computational cost for k-means clustering. The results indicate that the proposed method is significantly faster than k-means clustering by more than 10 times.

  5. Fast and Low-Cost Mechatronic Recognition System for Persian Banknotes

    Directory of Open Access Journals (Sweden)

    Majid Behjat

    2014-03-01

    Full Text Available In this paper, we designed a fast and low-cost mechatronic system for recognition of eight current Persian banknotes in circulation. Firstly, we proposed a mechanical solution for avoiding extra processing time caused by detecting the place of banknote and paper angle correction in an input image. We also defined new parameters for feature extraction, including colour features (RGBR values, size features (LWR and texture features (CRLVR value. Then, we used a Multi-Layer Perceptron (MLP neural network in the recognition phase to reduce the necessary processing time. In this research, we collected a perfect database of Persian banknote images (about 4000 double-sided prevalent images. We reached about 99.06% accuracy (average for each side in final banknote recognition by testing 800 different worn, torn and new banknotes which were not part of the initial learning phase. This accuracy could increase to 99.62% in double-sided decision mode. Finally, we designed an ATmega32 microcontroller-based hardware with 16MHz clock frequency for implementation of our proposed system which can recognize sample banknotes at about 480ms and 560ms for single-sided detection and double-sided detection respectively, after image scanning.

  6. Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

    Directory of Open Access Journals (Sweden)

    Miguel Gavilán

    2012-01-01

    Full Text Available This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM. A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

  7. Complete vision-based traffic sign recognition supported by an I2V communication system.

    Science.gov (United States)

    García-Garrido, Miguel A; Ocaña, Manuel; Llorca, David F; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

    2012-01-01

    This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

  8. Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

    Science.gov (United States)

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

  9. New pattern recognition system in the e-nose for Chinese spirit identification

    International Nuclear Information System (INIS)

    Zeng Hui; Li Qiang; Gu Yu

    2016-01-01

    This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance (QCM) principle, and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an 8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value (A), root-mean-square value (RMS), shape factor value (S f ), crest factor value (C f ), impulse factor value (I f ), clearance factor value (CL f ), kurtosis factor value (K v ) are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis (PCA) method. Finally the back propagation (BP) neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. (paper)

  10. Automatic Speech Recognition Systems for the Evaluation of Voice and Speech Disorders in Head and Neck Cancer

    Directory of Open Access Journals (Sweden)

    Andreas Maier

    2010-01-01

    Full Text Available In patients suffering from head and neck cancer, speech intelligibility is often restricted. For assessment and outcome measurements, automatic speech recognition systems have previously been shown to be appropriate for objective and quick evaluation of intelligibility. In this study we investigate the applicability of the method to speech disorders caused by head and neck cancer. Intelligibility was quantified by speech recognition on recordings of a standard text read by 41 German laryngectomized patients with cancer of the larynx or hypopharynx and 49 German patients who had suffered from oral cancer. The speech recognition provides the percentage of correctly recognized words of a sequence, that is, the word recognition rate. Automatic evaluation was compared to perceptual ratings by a panel of experts and to an age-matched control group. Both patient groups showed significantly lower word recognition rates than the control group. Automatic speech recognition yielded word recognition rates which complied with experts' evaluation of intelligibility on a significant level. Automatic speech recognition serves as a good means with low effort to objectify and quantify the most important aspect of pathologic speech—the intelligibility. The system was successfully applied to voice and speech disorders.

  11. Single-Walled Carbon Nano tubes as Fluorescence Biosensors for Pathogen Recognition in Water Systems

    International Nuclear Information System (INIS)

    Upadhyayula, V.K.K

    2008-01-01

    The possibility of using single-walled carbon nanotubes (SWCNTs) aggregates as fluorescence sensors for pathogen recognition in drinking water treatment applications has been studied. Batch adsorption study is conducted to adsorb large concentrations of Staphylococcus aureus aureus SH 1000 and Escherichia coli pKV-11 on single-walled carbon nanotubes. Subsequently the immobilized bacteria are detected with confocal microscopy by coating the nanotubes with fluorescence emitting antibodies. The Freundlich adsorption equilibrium constant (k) for S.aureus and E.coli determined from batch adsorption study was found to be 9 x108 and 2 x108 ml/g, respectively. The visualization of bacterial cells adsorbed on fluorescently modified carbon nanotubes is also clearly seen. The results indicate that hydrophobic single-walled carbon nanotubes have excellent bacterial adsorption capacity and fluorescent detection capability. This is an important advancement in designing fluorescence biosensors for pathogen recognition in water systems.

  12. High-emulation mask recognition with high-resolution hyperspectral video capture system

    Science.gov (United States)

    Feng, Jiao; Fang, Xiaojing; Li, Shoufeng; Wang, Yongjin

    2014-11-01

    We present a method for distinguishing human face from high-emulation mask, which is increasingly used by criminals for activities such as stealing card numbers and passwords on ATM. Traditional facial recognition technique is difficult to detect such camouflaged criminals. In this paper, we use the high-resolution hyperspectral video capture system to detect high-emulation mask. A RGB camera is used for traditional facial recognition. A prism and a gray scale camera are used to capture spectral information of the observed face. Experiments show that mask made of silica gel has different spectral reflectance compared with the human skin. As multispectral image offers additional spectral information about physical characteristics, high-emulation mask can be easily recognized.

  13. Social Hackers: Integration in the Host Chemical Recognition System by a Paper Wasp Social Parasite

    Science.gov (United States)

    Turillazzi, S.; Sledge, M. F.; Dani, F. R.; Cervo, R.; Massolo, A.; Fondelli, L.

    Obligate social parasites in the social insects have lost the worker caste and the ability to establish nests. As a result, parasites must usurp a host nest, overcome the host recognition system, and depend on the host workers to rear their offspring. We analysed cuticular hydrocarbon profiles of live parasite females of the paper wasp social parasite Polistes sulcifer before and after usurpation of host nests, using the non-destructive technique of solid-phase micro-extraction. Our results reveal that hydrocarbon profiles of parasites change after usurpation of host nests to match the cuticular profile of the host species. Chemical evidence further shows that the parasite queen changes the odour of the nest by the addition of a parasite-specific hydrocarbon. We discuss the possible role of this in the recognition and acceptance of the parasite and its offspring in the host colony.

  14. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  15. Using speech recognition to enhance the Tongue Drive System functionality in computer access.

    Science.gov (United States)

    Huo, Xueliang; Ghovanloo, Maysam

    2011-01-01

    Tongue Drive System (TDS) is a wireless tongue operated assistive technology (AT), which can enable people with severe physical disabilities to access computers and drive powered wheelchairs using their volitional tongue movements. TDS offers six discrete commands, simultaneously available to the users, for pointing and typing as a substitute for mouse and keyboard in computer access, respectively. To enhance the TDS performance in typing, we have added a microphone, an audio codec, and a wireless audio link to its readily available 3-axial magnetic sensor array, and combined it with a commercially available speech recognition software, the Dragon Naturally Speaking, which is regarded as one of the most efficient ways for text entry. Our preliminary evaluations indicate that the combined TDS and speech recognition technologies can provide end users with significantly higher performance than using each technology alone, particularly in completing tasks that require both pointing and text entry, such as web surfing.

  16. Individual recognition of social rank and social memory performance depends on a functional circadian system.

    Science.gov (United States)

    Müller, L; Weinert, D

    2016-11-01

    In a natural environment, social abilities of an animal are important for its survival. Particularly, it must recognize its own social rank and the social rank of a conspecific and have a good social memory. While the role of the circadian system for object and spatial recognition and memory is well known, the impact of the social rank and circadian disruptions on social recognition and memory were not investigated so far. In the present study, individual recognition of social rank and social memory performance of Djungarian hamsters revealing different circadian phenotypes were investigated. Wild type (WT) animals show a clear and well-synchronized daily activity rhythm, whereas in arrhythmic (AR) hamsters, the suprachiasmatic nuclei (SCN) do not generate a circadian signal. The aim of the study was to investigate putative consequences of these deteriorations in the circadian system for animalś cognitive abilities. Hamsters were bred and kept under standardized housing conditions with food and water ad libitum and a 14l/10 D lighting regimen. Experimental animals were assigned to different groups (WT and AR) according to their activity pattern obtained by means of infrared motion sensors. Before the experiments, the animals were given to develop a dominant-subordinate relationship in a dyadic encounter. Experiment 1 dealt with individual recognition of social rank. Subordinate and dominant hamsters were tested in an open arena for their behavioral responses towards a familiar (known from the agonistic encounters) or an unfamiliar hamster (from another agonistic encounter) which had the same or an opposite social rank. The investigation time depended on the social rank of the WT subject hamster and its familiarity with the stimulus animal. Both subordinate and dominant WT hamsters preferred an unfamiliar subordinate stimulus animal. In contrast, neither subordinate nor dominant AR hamsters preferred any of the stimulus animals. Thus, disruptions in circadian

  17. Single-Walled Carbon Nanotubes as Fluorescence Biosensors for Pathogen Recognition in Water Systems

    Directory of Open Access Journals (Sweden)

    Venkata K. K. Upadhyayula

    2008-01-01

    Full Text Available The possibility of using single-walled carbon nanotubes (SWCNTs aggregates as fluorescence sensors for pathogen recognition in drinking water treatment applications has been studied. Batch adsorption study is conducted to adsorb large concentrations of Staphylococcus aureus aureus SH 1000 and Escherichia coli pKV-11 on single-walled carbon nanotubes. Subsequently the immobilized bacteria are detected with confocal microscopy by coating the nanotubes with fluorescence emitting antibodies. The Freundlich adsorption equilibrium constant (k for S.aureus and E.coli determined from batch adsorption study was found to be 9×108 and 2×108 ml/g, respectively. The visualization of bacterial cells adsorbed on fluorescently modified carbon nanotubes is also clearly seen. The results indicate that hydrophobic single-walled carbon nanotubes have excellent bacterial adsorption capacity and fluorescent detection capability. This is an important advancement in designing fluorescence biosensors for pathogen recognition in water systems.

  18. Object Recognition System-on-Chip Using the Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Houzet Dominique

    2005-01-01

    Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

  19. Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

    Science.gov (United States)

    Lhamon, Michael Earl

    A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase

  20. An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

    Science.gov (United States)

    Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

    2018-04-01

    Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic

  1. An automatic speech recognition system with speaker-independent identification support

    Science.gov (United States)

    Caranica, Alexandru; Burileanu, Corneliu

    2015-02-01

    The novelty of this work relies on the application of an open source research software toolkit (CMU Sphinx) to train, build and evaluate a speech recognition system, with speaker-independent support, for voice-controlled hardware applications. Moreover, we propose to use the trained acoustic model to successfully decode offline voice commands on embedded hardware, such as an ARMv6 low-cost SoC, Raspberry PI. This type of single-board computer, mainly used for educational and research activities, can serve as a proof-of-concept software and hardware stack for low cost voice automation systems.

  2. Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

    Science.gov (United States)

    Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

    2017-01-01

    In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

  3. A low-cost machine vision system for the recognition and sorting of small parts

    Science.gov (United States)

    Barea, Gustavo; Surgenor, Brian W.; Chauhan, Vedang; Joshi, Keyur D.

    2018-04-01

    An automated machine vision-based system for the recognition and sorting of small parts was designed, assembled and tested. The system was developed to address a need to expose engineering students to the issues of machine vision and assembly automation technology, with readily available and relatively low-cost hardware and software. This paper outlines the design of the system and presents experimental performance results. Three different styles of plastic gears, together with three different styles of defective gears, were used to test the system. A pattern matching tool was used for part classification. Nine experiments were conducted to demonstrate the effects of changing various hardware and software parameters, including: conveyor speed, gear feed rate, classification, and identification score thresholds. It was found that the system could achieve a maximum system accuracy of 95% at a feed rate of 60 parts/min, for a given set of parameter settings. Future work will be looking at the effect of lighting.

  4. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

    Directory of Open Access Journals (Sweden)

    Jiangyi Qin

    Full Text Available A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

  5. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

    Science.gov (United States)

    Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

    2015-01-01

    A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

  6. A neural network based artificial vision system for licence plate recognition.

    Science.gov (United States)

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

  7. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

    Directory of Open Access Journals (Sweden)

    Hongqiang Li

    2016-10-01

    Full Text Available Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

  8. Computer versus paper system for recognition and management of sepsis in surgical intensive care.

    Science.gov (United States)

    Croft, Chasen A; Moore, Frederick A; Efron, Philip A; Marker, Peggy S; Gabrielli, Andrea; Westhoff, Lynn S; Lottenberg, Lawrence; Jordan, Janeen; Klink, Victoria; Sailors, R Matthew; McKinley, Bruce A

    2014-02-01

    A system to provide surveillance, diagnosis, and protocolized management of surgical intensive care unit (SICU) sepsis was undertaken as a performance improvement project. A system for sepsis management was implemented for SICU patients using paper followed by a computerized system. The hypothesis was that the computerized system would be associated with improved process and outcomes. A system was designed to provide early recognition and guide patient-specific management of sepsis including (1) modified early warning signs-sepsis recognition score (MEWS-SRS; summative point score of ranges of vital signs, mental status, white blood cell count; after every 4 hours) by bedside nurse; (2) suspected site assessment (vascular access, lung, abdomen, urinary tract, soft tissue, other) at bedside by physician or extender; (3) sepsis management protocol (replicable, point-of-care decisions) at bedside by nurse, physician, and extender. The system was implemented first using paper and then a computerized system. Sepsis severity was defined using standard criteria. In January to May 2012, a paper system was used to manage 77 consecutive sepsis encounters (3.9 ± 0.5 cases per week) in 65 patients (77% male; age, 53 ± 2 years). In June to December 2012, a computerized system was used to manage 132 consecutive sepsis encounters (4.4 ± 0.4 cases per week) in 119 patients (63% male; age, 58 ± 2 years). MEWS-SRS elicited 683 site assessments, and 201 had sepsis diagnosis and protocol management. The predominant site of infection was abdomen (paper, 58%; computer, 53%). Recognition of early sepsis tended to occur more using the computerized system (paper, 23%; computer, 35%). Hospital mortality rate for surgical ICU sepsis (paper, 20%; computer, 14%) was less with the computerized system. A computerized sepsis management system improves care process and outcome. Early sepsis is recognized and managed with greater frequency compared with severe sepsis or septic shock. The system

  9. Computerized literature reference system: use of an optical scanner and optical character recognition software.

    Science.gov (United States)

    Lossef, S V; Schwartz, L H

    1990-09-01

    A computerized reference system for radiology journal articles was developed by using an IBM-compatible personal computer with a hand-held optical scanner and optical character recognition software. This allows direct entry of scanned text from printed material into word processing or data-base files. Additionally, line diagrams and photographs of radiographs can be incorporated into these files. A text search and retrieval software program enables rapid searching for keywords in scanned documents. The hand scanner and software programs are commercially available, relatively inexpensive, and easily used. This permits construction of a personalized radiology literature file of readily accessible text and images requiring minimal typing or keystroke entry.

  10. Sistema audiovisual para reconocimiento de comandos Audiovisual system for recognition of commands

    Directory of Open Access Journals (Sweden)

    Alexander Ceballos

    2011-08-01

    Full Text Available Se presenta el desarrollo de un sistema automático de reconocimiento audiovisual del habla enfocado en el reconocimiento de comandos. La representación del audio se realizó mediante los coeficientes cepstrales de Mel y las primeras dos derivadas temporales. Para la caracterización del vídeo se hizo seguimiento automático de características visuales de alto nivel a través de toda la secuencia. Para la inicialización automática del algoritmo se emplearon transformaciones de color y contornos activos con información de flujo del vector gradiente ("GVF snakes" sobre la región labial, mientras que para el seguimiento se usaron medidas de similitud entre vecindarios y restricciones morfológicas definidas en el estándar MPEG-4. Inicialmente, se presenta el diseño del sistema de reconocimiento automático del habla, empleando únicamente información de audio (ASR, mediante Modelos Ocultos de Markov (HMMs y un enfoque de palabra aislada; posteriormente, se muestra el diseño de los sistemas empleando únicamente características de vídeo (VSR, y empleando características de audio y vídeo combinadas (AVSR. Al final se comparan los resultados de los tres sistemas para una base de datos propia en español y francés, y se muestra la influencia del ruido acústico, mostrando que el sistema de AVSR es más robusto que ASR y VSR.We present the development of an automatic audiovisual speech recognition system focused on the recognition of commands. Signal audio representation was done using Mel cepstral coefficients and their first and second order time derivatives. In order to characterize the video signal, a set of high-level visual features was tracked throughout the sequences. Automatic initialization of the algorithm was performed using color transformations and active contour models based on Gradient Vector Flow (GVF Snakes on the lip region, whereas visual tracking used similarity measures across neighborhoods and morphological

  11. Challenges and Specifications for Robust Face and Gait Recognition Systems for Surveillance Application

    Directory of Open Access Journals (Sweden)

    BUCIU Ioan

    2014-05-01

    Full Text Available Automated person recognition (APR based on biometric signals addresses the process of automatically recognize a person according to his physiological traits (face, voice, iris, fingerprint, ear shape, body odor, electroencephalogram – EEG, electrocardiogram, or hand geometry, or behavioural patterns (gait, signature, hand-grip, lip movement. The paper aims at briefly presenting the current challenges for two specific non-cooperative biometric approaches, namely face and gait biometrics as well as approaches that consider combination of the two in the attempt of a more robust system for accurate APR, in the context of surveillance application. Open problems from both sides are also pointed out.

  12. Fluid pipeline system leak detection based on neural network and pattern recognition

    International Nuclear Information System (INIS)

    Tang Xiujia

    1998-01-01

    The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

  13. Applied learning-based color tone mapping for face recognition in video surveillance system

    Science.gov (United States)

    Yew, Chuu Tian; Suandi, Shahrel Azmin

    2012-04-01

    In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

  14. A Presence-Based Context-Aware Chronic Stress Recognition System

    Directory of Open Access Journals (Sweden)

    Andrej Kos

    2012-11-01

    Full Text Available Stressors encountered in daily life may play an important role in personal well-being. Chronic stress can have a serious long-term impact on our physical as well as our psychological health, due to ongoing increased levels of the chemicals released in the ‘fight or flight’ response. The currently available stress assessment methods are usually not suitable for daily chronic stress measurement. The paper presents a context-aware chronic stress recognition system that addresses this problem. The proposed system obtains contextual data from various mobile sensors and other external sources in order to calculate the impact of ongoing stress. By identifying and visualizing ongoing stress situations of an individual user, he/she is able to modify his/her behavior in order to successfully avoid them. Clinical evaluation of the proposed methodology has been made in parallel by using electrodermal activity sensor. To the best of our knowledge, the system presented herein is the first one that enables recognition of chronic stress situations on the basis of user context.

  15. A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

    Directory of Open Access Journals (Sweden)

    Minglin Wu

    2016-10-01

    Full Text Available In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

  16. A food recognition system for diabetic patients based on an optimized bag-of-features model.

    Science.gov (United States)

    Anthimopoulos, Marios M; Gianola, Lauro; Scarnato, Luca; Diem, Peter; Mougiakakou, Stavroula G

    2014-07-01

    Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

  17. Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

    Science.gov (United States)

    Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

    2017-03-01

    Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

  18. Automatic Speech Recognition Systems for the Evaluation of Voice and Speech Disorders in Head and Neck Cancer

    OpenAIRE

    Andreas Maier; Tino Haderlein; Florian Stelzle; Elmar Nöth; Emeka Nkenke; Frank Rosanowski; Anne Schützenberger; Maria Schuster

    2010-01-01

    In patients suffering from head and neck cancer, speech intelligibility is often restricted. For assessment and outcome measurements, automatic speech recognition systems have previously been shown to be appropriate for objective and quick evaluation of intelligibility. In this study we investigate the applicability of the method to speech disorders caused by head and neck cancer. Intelligibility was quantified by speech recognition on recordings of a standard text read by 41 German laryngect...

  19. An Intelligent Systems Approach to Automated Object Recognition: A Preliminary Study

    Science.gov (United States)

    Maddox, Brian G.; Swadley, Casey L.

    2002-01-01

    Attempts at fully automated object recognition systems have met with varying levels of success over the years. However, none of the systems have achieved high enough accuracy rates to be run unattended. One of the reasons for this may be that they are designed from the computer's point of view and rely mainly on image-processing methods. A better solution to this problem may be to make use of modern advances in computational intelligence and distributed processing to try to mimic how the human brain is thought to recognize objects. As humans combine cognitive processes with detection techniques, such a system would combine traditional image-processing techniques with computer-based intelligence to determine the identity of various objects in a scene.

  20. A freely-available authoring system for browser-based CALL with speech recognition

    Directory of Open Access Journals (Sweden)

    Myles O'Brien

    2017-06-01

    Full Text Available A system for authoring browser-based CALL material incorporating Google speech recognition has been developed and made freely available for download. The system provides a teacher with a simple way to set up CALL material, including an optional image, sound or video, which will elicit spoken (and/or typed answers from the user and check them against a list of specified permitted answers, giving feedback with hints when necessary. The teacher needs no HTML or Javascript expertise, just the facilities and ability to edit text files and upload to the Internet. The structure and functioning of the system are explained in detail, and some suggestions are given for practical use. Finally, some of its limitations are described.

  1. An open and configurable embedded system for EMG pattern recognition implementation for artificial arms.

    Science.gov (United States)

    Jun Liu; Fan Zhang; Huang, He Helen

    2014-01-01

    Pattern recognition (PR) based on electromyographic (EMG) signals has been developed for multifunctional artificial arms for decades. However, assessment of EMG PR control for daily prosthesis use is still limited. One of the major barriers is the lack of a portable and configurable embedded system to implement the EMG PR control. This paper aimed to design an open and configurable embedded system for EMG PR implementation so that researchers can easily modify and optimize the control algorithms upon our designed platform and test the EMG PR control outside of the lab environments. The open platform was built on an open source embedded Linux Operating System running a high-performance Gumstix board. Both the hardware and software system framework were openly designed. The system was highly flexible in terms of number of inputs/outputs and calibration interfaces used. Such flexibility enabled easy integration of our embedded system with different types of commercialized or prototypic artificial arms. Thus far, our system was portable for take-home use. Additionally, compared with previously reported embedded systems for EMG PR implementation, our system demonstrated improved processing efficiency and high system precision. Our long-term goals are (1) to develop a wearable and practical EMG PR-based control for multifunctional artificial arms, and (2) to quantify the benefits of EMG PR-based control over conventional myoelectric prosthesis control in a home setting.

  2. Pattern Recognition via the Toll-Like Receptor System in the Human Female Genital Tract

    Directory of Open Access Journals (Sweden)

    Kaei Nasu

    2010-01-01

    Full Text Available The mucosal surface of the female genital tract is a complex biosystem, which provides a barrier against the outside world and participates in both innate and acquired immune defense systems. This mucosal compartment has adapted to a dynamic, non-sterile environment challenged by a variety of antigenic/inflammatory stimuli associated with sexual intercourse and endogenous vaginal microbiota. Rapid innate immune defenses against microbial infection usually involve the recognition of invading pathogens by specific pattern-recognition receptors recently attributed to the family of Toll-like receptors (TLRs. TLRs recognize conserved pathogen-associated molecular patterns (PAMPs synthesized by microorganisms including bacteria, fungi, parasites, and viruses as well as endogenous ligands associated with cell damage. Members of the TLR family, which includes 10 human TLRs identified to date, recognize distinct PAMPs produced by various bacterial, fungal, and viral pathogens. The available literature regarding the innate immune system of the female genital tract during human reproductive processes was reviewed in order to identify studies specifically related to the expression and function of TLRs under normal as well as pathological conditions. Increased understanding of these molecules may provide insight into site-specific immunoregulatory mechanisms in the female reproductive tract.

  3. Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model.

    Science.gov (United States)

    Chherawala, Youssouf; Roy, Partha Pratim; Cheriet, Mohamed

    2016-12-01

    The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.

  4. Recognition and management of idiopathic systemic capillary leak syndrome: an evidence-based review.

    Science.gov (United States)

    Baloch, Noor Ul-Ain; Bikak, Marvi; Rehman, Abdul; Rahman, Omar

    2018-05-01

    Idiopathic systemic capillary leak syndrome (SCLS) is a unique disorder characterized by episodes of massive systemic leak of intravascular fluid leading to volume depletion and shock. A typical attack of SCLS consists of prodromal, leak and post-leak phases. Complications, such as compartment syndrome and pulmonary edema, usually develop during the leak and post-leak phases respectively. Judicious intravenous hydration and early use of vasopressors is the cornerstone of management in such cases. Areas covered: The purpose of the present review is to provide an up-to-date, evidence-based review of our understanding of SCLS and its management in the light of currently available evidence. Idiopathic SCLS was first described in 1960 and, since then, more than 250 cases have been reported. A large number of cases have been reported over the past one decade, most likely due to improved recognition. In the acute care setting, most patients with SCLS are managed as per the Surviving Sepsis guidelines and receive aggressive volume resuscitation - which is not the optimal management strategy for such patients. There is a need to raise awareness amongst physicians and clinicians in order to improve recognition of this disorder and ensure its appropriate management.

  5. A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors

    Directory of Open Access Journals (Sweden)

    Xinhua Liu

    2017-03-01

    Full Text Available Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle’s angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers.

  6. Material recognition with the Medipix photon counting colour X-ray system

    Energy Technology Data Exchange (ETDEWEB)

    Norlin, B. E-mail: borje.norlin@mh.se; Manuilskiy, A.; Nilsson, H.-E.; Froejdh, C

    2004-09-21

    An energy sensitive imaging system like Medipix1 has proved to be promising in distinguishing different materials in an X-ray image of an object. We propose a general method utilising X-ray energy information for material recognition. For objects where the thickness of the materials is unknown, a convenient material parameter to identify is K={alpha}{sub 1}/{alpha}{sub 2}, which is the ratio of the logarithms of the measured transmissions ln(t{sub 1})/ln(t{sub 2}). If a database of the parameter K for different materials and energies is created, this method can be used for material recognition independent of the thickness of the materials. Series of images of an object consisting of aluminium and silicon were taken with different energy thresholds. The X-ray absorption for silicon and aluminium is very similar for the range 40-60 keV and only differs for lower energies. The results show that it is possible to distinguish between aluminium and silicon on images achieved by Medipix1 using a standard dental source. By decreasing the spatial resolution a better contrast between the materials was achieved. The resolution of contrasts shown by the histograms was close to the limit of the system due to the statistical noise of the signal.

  7. Fluorescent sensor systems based on nanostructured polymeric membranes for selective recognition of Aflatoxin B1.

    Science.gov (United States)

    Sergeyeva, Tetyana; Yarynka, Daria; Piletska, Elena; Lynnik, Rostyslav; Zaporozhets, Olga; Brovko, Oleksandr; Piletsky, Sergey; El'skaya, Anna

    2017-12-01

    Nanostructured polymeric membranes for selective recognition of aflatoxin B1 were synthesized in situ and used as highly sensitive recognition elements in the developed fluorescent sensor. Artificial binding sites capable of selective recognition of aflatoxin B1 were formed in the structure of the polymeric membranes using the method of molecular imprinting. A composition of molecularly imprinted polymer (MIP) membranes was optimized using the method of computational modeling. The MIP membranes were synthesized using the non-toxic close structural analogue of aflatoxin B1, ethyl-2-oxocyclopentanecarboxylate as a dummy template. The MIP membranes with the optimized composition demonstrated extremely high selectivity towards aflatoxin B1 (AFB1). Negligible binding of close structural analogues of AFB1 - aflatoxins B2 (AFB2), aflatoxin G2 (AFG2), and ochratoxin A (OTA) was demonstrated. Binding of AFB1 by the MIP membranes was investigated as a function of both type and concentration of the functional monomer in the initial monomer composition used for the membranes' synthesis, as well as sample composition. The conditions of the solid-phase extraction of the mycotoxin using the MIP membrane as a stationary phase (pH, ionic strength, buffer concentration, volume of the solution, ratio between water and organic solvent, filtration rate) were optimized. The fluorescent sensor system based on the optimized MIP membranes provided a possibility of AFB1 detection within the range 14-500ngmL -1 demonstrating detection limit (3Ϭ) of 14ngmL -1 . The developed technique was successfully applied for the analysis of model solutions and waste waters from bread-making plants. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Contralateral comparison of wavefront-guided LASIK surgery with iris recognition versus without iris recognition using the MEL80 Excimer laser system.

    Science.gov (United States)

    Wu, Fang; Yang, Yabo; Dougherty, Paul J

    2009-05-01

    To compare outcomes in wavefront-guided LASIK performed with iris recognition software versus without iris recognition software in different eyes of the same patient. A randomised, prospective study of 104 myopic eyes of 52 patients undergoing LASIK surgery with the MEL80 excimer laser system was performed. Iris recognition software was used in one eye of each patient (study group) and not used in the other eye (control group). Higher order aberrations (HOAs), contrast sensitivity, uncorrected vision (UCV), visual acuity (VA) and corneal topography were measured and recorded pre-operatively and at one month and three months post-operatively for each eye. The mean post-operative sphere and cylinder between groups was similar, however the post-operative angles of error (AE) by refraction were significantly smaller in the study group compared to the control group both in arithmetic and absolute means (p = 0.03, p = 0.01). The mean logMAR UCV was significantly better in the study group than in the control group at one month (p = 0.01). The mean logMAR VA was significantly better in the study group than in control group at both one and three months (p = 0.01, p = 0.03). In addition, mean trefoil, total third-order aberration, total fourth-order aberration and the total scotopic root-mean-square (RMS) HOAs were significantly less in the study group than those in the control group at the third (p = 0.01, p = 0.05, p = 0.04, p = 0.02). By three months, the contrast sensitivity had recovered in both groups but the study group performed better at 2.6, 4.2 and 6.6 cpd (cycles per degree) than the control group (p = 0.01, p iris recognition results in better VA, lower mean higher-order aberrations, lower refractive post-operative angles of error and better contrast sensitivity at three months post-operatively than LASIK performed without iris recognition.

  9. Template protection and its implementation in 3D face recognition systems

    Science.gov (United States)

    Zhou, Xuebing

    2007-04-01

    As biometric recognition systems are widely applied in various application areas, security and privacy risks have recently attracted the attention of the biometric community. Template protection techniques prevent stored reference data from revealing private biometric information and enhance the security of biometrics systems against attacks such as identity theft and cross matching. This paper concentrates on a template protection algorithm that merges methods from cryptography, error correction coding and biometrics. The key component of the algorithm is to convert biometric templates into binary vectors. It is shown that the binary vectors should be robust, uniformly distributed, statistically independent and collision-free so that authentication performance can be optimized and information leakage can be avoided. Depending on statistical character of the biometric template, different approaches for transforming biometric templates into compact binary vectors are presented. The proposed methods are integrated into a 3D face recognition system and tested on the 3D facial images of the FRGC database. It is shown that the resulting binary vectors provide an authentication performance that is similar to the original 3D face templates. A high security level is achieved with reasonable false acceptance and false rejection rates of the system, based on an efficient statistical analysis. The algorithm estimates the statistical character of biometric templates from a number of biometric samples in the enrollment database. For the FRGC 3D face database, the small distinction of robustness and discriminative power between the classification results under the assumption of uniquely distributed templates and the ones under the assumption of Gaussian distributed templates is shown in our tests.

  10. Customized Computer Vision and Sensor System for Colony Recognition and Live Bacteria Counting in Agriculture

    Directory of Open Access Journals (Sweden)

    Gabriel M. ALVES

    2016-06-01

    Full Text Available This paper presents an arrangement based on a dedicated computer and charge-coupled device (CCD sensor system to intelligently allow the counting and recognition of colony formation. Microbes in agricultural environments are important catalysts of global carbon and nitrogen cycles, including the production and consumption of greenhouse gases in soil. Some microbes produce greenhouse gases such as carbon dioxide and nitrous oxide while decomposing organic matter in soil. Others consume methane from the atmosphere, helping to mitigate climate change. The magnitude of each of these processes is influenced by human activities and impacts the warming potential of Earth’s atmosphere. In this context, bacterial colony counting is important and requires sophisticated analysis methods. The method implemented in this study uses digital image processing techniques, including the Hough Transform for circular objects. The visual environment Borland Builder C++ was used for development, and a model for decision making was incorporated to aggregate intelligence. For calibration of the method a prepared illuminated chamber was used to enable analyses of the bacteria Escherichia coli, and Acidithiobacillus ferrooxidans. For validation, a set of comparisons were established between this smart method and the expert analyses. The results show the potential of this method for laboratory applications that involve the quantification and pattern recognition of bacterial colonies in solid culture environments.

  11. An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Abdu Gumaei

    2018-01-01

    Full Text Available Multispectral palmprint recognition system (MPRS is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE and regularized extreme learning machine (RELM is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI from palmprint images is extracted by David Zhang’s method. Second, an efficient normalized Gist (NGist descriptor is used for palmprint feature extraction. Then, the dimensionality of extracted features is reduced using optimized AE. Finally, the reduced features are fed to the RELM for classification. A comprehensive set of experiments are conducted on the benchmark MS-PolyU dataset. The results were significantly high compared to the state-of-the-art approaches, and the robustness and efficiency of the proposed approach are revealed.

  12. Application of Business Process Management to drive the deployment of a speech recognition system in a healthcare organization.

    Science.gov (United States)

    González Sánchez, María José; Framiñán Torres, José Manuel; Parra Calderón, Carlos Luis; Del Río Ortega, Juan Antonio; Vigil Martín, Eduardo; Nieto Cervera, Jaime

    2008-01-01

    We present a methodology based on Business Process Management to guide the development of a speech recognition system in a hospital in Spain. The methodology eases the deployment of the system by 1) involving the clinical staff in the process, 2) providing the IT professionals with a description of the process and its requirements, 3) assessing advantages and disadvantages of the speech recognition system, as well as its impact in the organisation, and 4) help reorganising the healthcare process before implementing the new technology in order to identify how it can better contribute to the overall objective of the organisation.

  13. The neuro-immunological interface in an evolutionary perspective: the dynamic relationship between effector and recognition systems.

    Science.gov (United States)

    Ottaviani, E; Valensin, S; Franceschi, C

    1998-04-16

    The evolutionary perspective indicates that an immune-neuroendocrine effector system integrating innate immunity, stress and inflammation is present in invertebrates. This defense network, centered on the macrophage and exerting primitive and highly promiscuous recognition units, is very effective, ancestral and appears to have been conserved throughout evolution from invertebrates to higher vertebrates. It would seem that there was a "big bang" in the recognition system of lower vertebrates, and T and B cell repertoires, MHC and antibodies suddenly appeared. We argue that this phenomenon is the counterpart of the increasing complexity of the internal circuitry and recognition units in the effector system. The immediate consequences were a progressive enlargement of the pathogen repertoire and new problems regarding self/not-self discrimination. Probably not by chance, a new organ appeared, capable of purging cells able of excessive self recognition. This organ, the thymus, appears to be the result of a well known evolutionary strategy of re-using pre-existing material (neuroendocrine cells and mediators constituting the thymic microenvironment). This bricolage at an organ level is similar to the effect we have already described at the level of molecules and functions of the defense network, and has a general counterpart at genetic level. Thus, in vertebrates, the conserved immune-neuroendocrine effector system remains of fundamental importance in defense against pathogens, while its efficiency has increased through synergy with the new, clonotipical recognition repertoire.

  14. Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

    Science.gov (United States)

    Huang, Yiteng; Chen, Jingdong; Chen, Shaoyan

    2010-01-01

    A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

  15. Implementation of a Tour Guide Robot System Using RFID Technology and Viterbi Algorithm-Based HMM for Speech Recognition

    Directory of Open Access Journals (Sweden)

    Neng-Sheng Pai

    2014-01-01

    Full Text Available This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. For speech recognition, the speech signals were captured by short-time processing. The speaker first recorded the isolated words for the robot to create speech database of specific speakers. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC. Then, the Hidden Markov Model (HMM was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot.

  16. Toward a multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition

    International Nuclear Information System (INIS)

    King, D; Lyons, W B; Flanagan, C; Lewis, E

    2005-01-01

    An optical fibre sensor capable of detecting various concentrations of ethanol in water supplies is reported. The sensor is based on a U-bend sensor configuration and is incorporated into a 170-metre length of silica cladding silica core optical fibre. The sensor is interrogated using Optical Time Domain Reflectometry (OTDR) and it is proposed to apply artificial neural network (ANN) pattern recognition techniques to the resulting OTDR signals to accurately classify the sensor test conditions. It is also proposed that additional U-bend configuration sensors will be added to the fibre measurement length, in order to implement a multipoint optical fibre sensor system

  17. Perceived Task-Difficulty Recognition from Log-File Information for the Use in Adaptive Intelligent Tutoring Systems

    Science.gov (United States)

    Janning, Ruth; Schatten, Carlotta; Schmidt-Thieme, Lars

    2016-01-01

    Recognising students' emotion, affect or cognition is a relatively young field and still a challenging task in the area of intelligent tutoring systems. There are several ways to use the output of these recognition tasks within the system. The approach most often mentioned in the literature is using it for giving feedback to the students. The…

  18. Towards evidence-based, quality-controlled health promotion: the Dutch recognition system for health promotion interventions.

    NARCIS (Netherlands)

    Brug, J.; Dale, D. van; Lanting, L.; Kremers, S.; Veenhof, C.; Leurs, M.; Yperen, T. van; Kok, G.

    2010-01-01

    Registration or recognition systems for best-practice health promotion interventions may contribute to better quality assurance and control in health promotion practice. In the Netherlands, such a system has been developed and is being implemented aiming to provide policy makers and professionals

  19. Computer system for structure recognition of polyatomic molecules by i. r. , n. m. r. , u. v. and m. s. methods

    Energy Technology Data Exchange (ETDEWEB)

    Gribov, L A; Elyashberg, M E; Serov, V V [USSR Academy of Sciences, Moscow (USSR). V.I. Vernadsky Institute of Geochemistry and Analytical Chemistry

    1977-12-15

    A system of algorithms and programs for the recognition of the structures of polyatomic molecules by means of i.r., n.m.r., u.v. and mass spectra is described. Examples of structures identified are cited. The results are promising and suggest that the system could be used for the identification of complex organic compounds.

  20. CL-L1 and CL-K1 and other complement associated pattern recognition molecules in systemic lupus erythematosus

    DEFF Research Database (Denmark)

    Troldborg, Anne; Thiel, Steffen; Jensen, Lisbeth

    2015-01-01

    The objective of this study was to explore the involvement of collectin liver 1 (CL-L1) and collectin kidney 1 (CL-K1) and other pattern recognition molecules (PRMs) of the lectin pathway of the complement system in a cross-sectional cohort of systemic lupus erythematosus (SLE) patients...

  1. High-accuracy and robust face recognition system based on optical parallel correlator using a temporal image sequence

    Science.gov (United States)

    Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Kodate, Kashiko

    2005-09-01

    Face recognition is used in a wide range of security systems, such as monitoring credit card use, searching for individuals with street cameras via Internet and maintaining immigration control. There are still many technical subjects under study. For instance, the number of images that can be stored is limited under the current system, and the rate of recognition must be improved to account for photo shots taken at different angles under various conditions. We implemented a fully automatic Fast Face Recognition Optical Correlator (FARCO) system by using a 1000 frame/s optical parallel correlator designed and assembled by us. Operational speed for the 1: N (i.e. matching a pair of images among N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 seconds, including the pre/post processing. From trial 1: N identification experiments using FARCO, we acquired low error rates of 2.6% False Reject Rate and 1.3% False Accept Rate. By making the most of the high-speed data-processing capability of this system, much more robustness can be achieved for various recognition conditions when large-category data are registered for a single person. We propose a face recognition algorithm for the FARCO while employing a temporal image sequence of moving images. Applying this algorithm to a natural posture, a two times higher recognition rate scored compared with our conventional system. The system has high potential for future use in a variety of purposes such as search for criminal suspects by use of street and airport video cameras, registration of babies at hospitals or handling of an immeasurable number of images in a database.

  2. A study of fuzzy logic ensemble system performance on face recognition problem

    Science.gov (United States)

    Polyakova, A.; Lipinskiy, L.

    2017-02-01

    Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

  3. Extraction Of Audio Features For Emotion Recognition System Based On Music

    Directory of Open Access Journals (Sweden)

    Kee Moe Han

    2015-08-01

    Full Text Available Music is the combination of melody linguistic information and the vocalists emotion. Since music is a work of art analyzing emotion in music by computer is a difficult task. Many approaches have been developed to detect the emotions included in music but the results are not satisfactory because emotion is very complex. In this paper the evaluations of audio features from the music files are presented. The extracted features are used to classify the different emotion classes of the vocalists. Musical features extraction is done by using Music Information Retrieval MIR tool box in this paper. The database of 100 music clips are used to classify the emotions perceived in music clips. Music may contain many emotions according to the vocalists mood such as happy sad nervous bored peace etc. In this paper the audio features related to the emotions of the vocalists are extracted to use in emotion recognition system based on music.

  4. Combining users' activity survey and simulators to evaluate human activity recognition systems.

    Science.gov (United States)

    Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

    2015-04-08

    Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

  5. Combining Users’ Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

    Directory of Open Access Journals (Sweden)

    Gorka Azkune

    2015-04-01

    Full Text Available Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

  6. Semi-automatic parking slot marking recognition for intelligent parking assist systems

    Directory of Open Access Journals (Sweden)

    Ho Gi Jung

    2014-01-01

    Full Text Available This paper proposes a semi-automatic parking slot marking-based target position designation method for parking assist systems in cases where the parking slot markings are of a rectangular type, and its efficient implementation for real-time operation. After the driver observes a rearview image captured by a rearward camera installed at the rear of the vehicle through a touchscreen-based human machine interface, a target parking position is designated by touching the inside of a parking slot. To ensure the proposed method operates in real-time in an embedded environment, access of the bird's-eye view image is made efficient: image-wise batch transformation is replaced with pixel-wise instantaneous transformation. The proposed method showed a 95.5% recognition rate in 378 test cases with 63 test images. Additionally, experiments confirmed that the pixel-wise instantaneous transformation reduced execution time by 92%.

  7. Combining Users' Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

    Science.gov (United States)

    Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

    2015-01-01

    Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant. PMID:25856329

  8. A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

    Directory of Open Access Journals (Sweden)

    Ahmad Jalal

    2014-07-01

    Full Text Available Recent advancements in depth video sensors technologies have made human activity recognition (HAR realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

  9. A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments.

    Science.gov (United States)

    Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

    2014-07-02

    Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

  10. An interactive VR system based on full-body tracking and gesture recognition

    Science.gov (United States)

    Zeng, Xia; Sang, Xinzhu; Chen, Duo; Wang, Peng; Guo, Nan; Yan, Binbin; Wang, Kuiru

    2016-10-01

    Most current virtual reality (VR) interactions are realized with the hand-held input device which leads to a low degree of presence. There is other solutions using sensors like Leap Motion to recognize the gestures of users in order to interact in a more natural way, but the navigation in these systems is still a problem, because they fail to map the actual walking to virtual walking only with a partial body of the user represented in the synthetic environment. Therefore, we propose a system in which users can walk around in the virtual environment as a humanoid model, selecting menu items and manipulating with the virtual objects using natural hand gestures. With a Kinect depth camera, the system tracks the joints of the user, mapping them to a full virtual body which follows the move of the tracked user. The movements of the feet can be detected to determine whether the user is in walking state, so that the walking of model in the virtual world can be activated and stopped by means of animation control in Unity engine. This method frees the hands of users comparing to traditional navigation way using hand-held device. We use the point cloud data getting from Kinect depth camera to recognize the gestures of users, such as swiping, pressing and manipulating virtual objects. Combining the full body tracking and gestures recognition using Kinect, we achieve our interactive VR system in Unity engine with a high degree of presence.

  11. Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System

    Directory of Open Access Journals (Sweden)

    Jun-Ming Lu

    2011-07-01

    Full Text Available This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications.

  12. Shift-, rotation-, and scale-invariant shape recognition system using an optical Hough transform

    Science.gov (United States)

    Schmid, Volker R.; Bader, Gerhard; Lueder, Ernst H.

    1998-02-01

    We present a hybrid shape recognition system with an optical Hough transform processor. The features of the Hough space offer a separate cancellation of distortions caused by translations and rotations. Scale invariance is also provided by suitable normalization. The proposed system extends the capabilities of Hough transform based detection from only straight lines to areas bounded by edges. A very compact optical design is achieved by a microlens array processor accepting incoherent light as direct optical input and realizing the computationally expensive connections massively parallel. Our newly developed algorithm extracts rotation and translation invariant normalized patterns of bright spots on a 2D grid. A neural network classifier maps the 2D features via a nonlinear hidden layer onto the classification output vector. We propose initialization of the connection weights according to regions of activity specifically assigned to each neuron in the hidden layer using a competitive network. The presented system is designed for industry inspection applications. Presently we have demonstrated detection of six different machined parts in real-time. Our method yields very promising detection results of more than 96% correctly classified parts.

  13. A vision-based automated guided vehicle system with marker recognition for indoor use.

    Science.gov (United States)

    Lee, Jeisung; Hyun, Chang-Ho; Park, Mignon

    2013-08-07

    We propose an intelligent vision-based Automated Guided Vehicle (AGV) system using fiduciary markers. In this paper, we explore a low-cost, efficient vehicle guiding method using a consumer grade web camera and fiduciary markers. In the proposed method, the system uses fiduciary markers with a capital letter or triangle indicating direction in it. The markers are very easy to produce, manipulate, and maintain. The marker information is used to guide a vehicle. We use hue and saturation values in the image to extract marker candidates. When the known size fiduciary marker is detected by using a bird's eye view and Hough transform, the positional relation between the marker and the vehicle can be calculated. To recognize the character in the marker, a distance transform is used. The probability of feature matching was calculated by using a distance transform, and a feature having high probability is selected as a captured marker. Four directional signals and 10 alphabet features are defined and used as markers. A 98.87% recognition rate was achieved in the testing phase. The experimental results with the fiduciary marker show that the proposed method is a solution for an indoor AGV system.

  14. 24/7 security system: 60-FPS color EMCCD camera with integral human recognition

    Science.gov (United States)

    Vogelsong, T. L.; Boult, T. E.; Gardner, D. W.; Woodworth, R.; Johnson, R. C.; Heflin, B.

    2007-04-01

    An advanced surveillance/security system is being developed for unattended 24/7 image acquisition and automated detection, discrimination, and tracking of humans and vehicles. The low-light video camera incorporates an electron multiplying CCD sensor with a programmable on-chip gain of up to 1000:1, providing effective noise levels of less than 1 electron. The EMCCD camera operates in full color mode under sunlit and moonlit conditions, and monochrome under quarter-moonlight to overcast starlight illumination. Sixty frame per second operation and progressive scanning minimizes motion artifacts. The acquired image sequences are processed with FPGA-compatible real-time algorithms, to detect/localize/track targets and reject non-targets due to clutter under a broad range of illumination conditions and viewing angles. The object detectors that are used are trained from actual image data. Detectors have been developed and demonstrated for faces, upright humans, crawling humans, large animals, cars and trucks. Detection and tracking of targets too small for template-based detection is achieved. For face and vehicle targets the results of the detection are passed to secondary processing to extract recognition templates, which are then compared with a database for identification. When combined with pan-tilt-zoom (PTZ) optics, the resulting system provides a reliable wide-area 24/7 surveillance system that avoids the high life-cycle cost of infrared cameras and image intensifiers.

  15. Toward fast feature adaptation and localization for real-time face recognition systems

    NARCIS (Netherlands)

    Zuo, F.; With, de P.H.N.; Ebrahimi, T.; Sikora, T.

    2003-01-01

    In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization.

  16. Towards PLDA-RBM based speaker recognition in mobile environment: Designing stacked/deep PLDA-RBM systems

    DEFF Research Database (Denmark)

    Nautsch, Andreas; Hao, Hong; Stafylakis, Themos

    2016-01-01

    recognition: two deep architectures are presented and examined, which aim at suppressing channel effects and recovering speaker-discriminative information on back-ends trained on a small dataset. Experiments are carried out on the MOBIO SRE'13 database, which is a challenging and publicly available dataset...... for mobile speaker recognition with limited amounts of training data. The experiments show that the proposed system outperforms the baseline i-vector/PLDA approach by relative gains of 31% on female and 9% on male speakers in terms of half total error rate....

  17. The influence of age, hearing, and working memory on the speech comprehension benefit derived from an automatic speech recognition system

    NARCIS (Netherlands)

    Zekveld, A.A.; Kramer, S.E.; Kessens, J.M.; Vlaming, M.S.M.G.; Houtgast, T.

    2009-01-01

    Objective: The aim of the current study was to examine whether partly incorrect subtitles that are automatically generated by an Automatic Speech Recognition (ASR) system, improve speech comprehension by listeners with hearing impairment. In an earlier study (Zekveld et al. 2008), we showed that

  18. A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

    Science.gov (United States)

    Zhao, Weixiang; Davis, Cristina E.

    2011-01-01

    Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for

  19. Type I interferon production during herpes simplex virus infection is controlled by cell-type-specific viral recognition through Toll-like receptor 9, the mitochondrial antiviral signaling protein pathway, and novel recognition systems

    DEFF Research Database (Denmark)

    Rasmussen, Simon Brandtoft; Sørensen, Louise Nørgaard; Malmgaard, Lene

    2007-01-01

    Recognition of viruses by germ line-encoded pattern recognition receptors of the innate immune system is essential for rapid production of type I interferon (IFN) and early antiviral defense. We investigated the mechanisms of viral recognition governing production of type I IFN during herpes...... simplex virus (HSV) infection. We show that early production of IFN in vivo is mediated through Toll-like receptor 9 (TLR9) and plasmacytoid dendritic cells, whereas the subsequent alpha/beta IFN (IFN-alpha/beta) response is derived from several cell types and induced independently of TLR9...

  20. Neuroscience-inspired computational systems for speech recognition under noisy conditions

    Science.gov (United States)

    Schafer, Phillip B.

    Humans routinely recognize speech in challenging acoustic environments with background music, engine sounds, competing talkers, and other acoustic noise. However, today's automatic speech recognition (ASR) systems perform poorly in such environments. In this dissertation, I present novel methods for ASR designed to approach human-level performance by emulating the brain's processing of sounds. I exploit recent advances in auditory neuroscience to compute neuron-based representations of speech, and design novel methods for decoding these representations to produce word transcriptions. I begin by considering speech representations modeled on the spectrotemporal receptive fields of auditory neurons. These representations can be tuned to optimize a variety of objective functions, which characterize the response properties of a neural population. I propose an objective function that explicitly optimizes the noise invariance of the neural responses, and find that it gives improved performance on an ASR task in noise compared to other objectives. The method as a whole, however, fails to significantly close the performance gap with humans. I next consider speech representations that make use of spiking model neurons. The neurons in this method are feature detectors that selectively respond to spectrotemporal patterns within short time windows in speech. I consider a number of methods for training the response properties of the neurons. In particular, I present a method using linear support vector machines (SVMs) and show that this method produces spikes that are robust to additive noise. I compute the spectrotemporal receptive fields of the neurons for comparison with previous physiological results. To decode the spike-based speech representations, I propose two methods designed to work on isolated word recordings. The first method uses a classical ASR technique based on the hidden Markov model. The second method is a novel template-based recognition scheme that takes

  1. Development of Vision Based Multiview Gait Recognition System with MMUGait Database

    Directory of Open Access Journals (Sweden)

    Hu Ng

    2014-01-01

    Full Text Available This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

  2. Dissecting molecular interactions involved in recognition of target disulfides by the barley thioredoxin system

    DEFF Research Database (Denmark)

    Björnberg, Olof; Maeda, Kenji; Svensson, Birte

    2012-01-01

    Thioredoxin reduces disulfide bonds, thus regulating activities of target proteins in various biological systems, e.g., inactivation of inhibitors of starch hydrolases and proteases in germinating plant seeds. In the three-dimensional structure of a complex with barley α-amylase/subtilisin inhibi......Thioredoxin reduces disulfide bonds, thus regulating activities of target proteins in various biological systems, e.g., inactivation of inhibitors of starch hydrolases and proteases in germinating plant seeds. In the three-dimensional structure of a complex with barley α...... thioredoxin reductase. HvTrxh2 M88G and M88A adjacent to the invariant cis-proline lost efficiency in both BASI disulfide reduction and recycling by thioredoxin reductase. These effects were further pronounced in M88P lacking a backbone NH group. Remarkably, HvTrxh2 E86R in the same loop displayed overall...... retained catalytic properties, with the exception of a 3-fold increased activity toward BASI. From the 104VGA106 loop, a backbone hydrogen bond donated by A106 appears to be important for target disulfide recognition as A106P lost 90% activity toward BASI but was efficiently recycled by thioredoxin...

  3. High-Speed Video System for Micro-Expression Detection and Recognition

    Directory of Open Access Journals (Sweden)

    Diana Borza

    2017-12-01

    Full Text Available Micro-expressions play an essential part in understanding non-verbal communication and deceit detection. They are involuntary, brief facial movements that are shown when a person is trying to conceal something. Automatic analysis of micro-expression is challenging due to their low amplitude and to their short duration (they occur as fast as 1/15 to 1/25 of a second. We propose a fully micro-expression analysis system consisting of a high-speed image acquisition setup and a software framework which can detect the frames when the micro-expressions occurred as well as determine the type of the emerged expression. The detection and classification methods use fast and simple motion descriptors based on absolute image differences. The recognition module it only involves the computation of several 2D Gaussian probabilities. The software framework was tested on two publicly available high speed micro-expression databases and the whole system was used to acquire new data. The experiments we performed show that our solution outperforms state of the art works which use more complex and computationally intensive descriptors.

  4. A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles

    Directory of Open Access Journals (Sweden)

    Jae Kyu Suhr

    2018-04-01

    Full Text Available An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.

  5. Model-based vision system for automatic recognition of structures in dental radiographs

    Science.gov (United States)

    Acharya, Raj S.; Samarabandu, Jagath K.; Hausmann, E.; Allen, K. A.

    1991-07-01

    X-ray diagnosis of destructive periodontal disease requires assessing serial radiographs by an expert to determine the change in the distance between cemento-enamel junction (CEJ) and the bone crest. To achieve this without the subjectivity of a human expert, a knowledge based system is proposed to automatically locate the two landmarks which are the CEJ and the level of alveolar crest at its junction with the periodontal ligament space. This work is a part of an ongoing project to automatically measure the distance between CEJ and the bone crest along a line parallel to the axis of the tooth. The approach presented in this paper is based on identifying a prominent feature such as the tooth boundary using local edge detection and edge thresholding to establish a reference and then using model knowledge to process sub-regions in locating the landmarks. Segmentation techniques invoked around these regions consists of a neural-network like hierarchical refinement scheme together with local gradient extraction, multilevel thresholding and ridge tracking. Recognition accuracy is further improved by first locating the easily identifiable parts of the bone surface and the interface between the enamel and the dentine and then extending these boundaries towards the periodontal ligament space and the tooth boundary respectively. The system is realized as a collection of tools (or knowledge sources) for pre-processing, segmentation, primary and secondary feature detection and a control structure based on the blackboard model to coordinate the activities of these tools.

  6. A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery

    Directory of Open Access Journals (Sweden)

    Shu Tian

    2015-01-01

    Full Text Available The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a VidEo-Based Intelligent Recognitionand Decision (VEBIRD system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VEBIRD comprises a robust eye (iris detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VEBIRD’s effectiveness.

  7. Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors

    Directory of Open Access Journals (Sweden)

    Tuyen Danh Pham

    2015-07-01

    Full Text Available Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

  8. Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors.

    Science.gov (United States)

    Pham, Tuyen Danh; Park, Young Ho; Nguyen, Dat Tien; Kwon, Seung Yong; Park, Kang Ryoung

    2015-07-13

    Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.

  9. Large Vocabulary Recognition of Wall Street Journal Sentences at Dragon Systems

    National Research Council Canada - National Science Library

    Baker, James; Baker, Janet; Bamberg, Paul; Bishop, Kathleen; Gillick, Larry; Helman, Vera; Huang, Zezhen; Ito, Yoshiko; Lowe, Stephen; Peskin, Barbara; Roth, Robert; Scattone, Francesco

    1992-01-01

    In this paper we present some of the algorithm improvements that have been made to Dragon's continuous speech recognition and training programs, improvements that have more than halved our error rate...

  10. Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

    OpenAIRE

    CHIKH, Mohamed Amine; SAIDI, Meryem; SETTOUTI, Nesma

    2012-01-01

    The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disea...

  11. Developing a Natural User Interface and Facial Recognition System With OpenCV and the Microsoft Kinect

    Science.gov (United States)

    Gutensohn, Michael

    2018-01-01

    The task for this project was to design, develop, test, and deploy a facial recognition system for the Kennedy Space Center Augmented/Virtual Reality Lab. This system will serve as a means of user authentication as part of the NUI of the lab. The overarching goal is to create a seamless user interface that will allow the user to initiate and interact with AR and VR experiences without ever needing to use a mouse or keyboard at any step in the process.

  12. Foundations for a syntatic pattern recognition system for genomic DNA sequences. [Annual] report, 1 December 1991--31 March 1993

    Energy Technology Data Exchange (ETDEWEB)

    Searles, D.B.

    1993-03-01

    The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

  13. A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

    Directory of Open Access Journals (Sweden)

    Haryati Jaafar

    2015-01-01

    Full Text Available Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN, was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

  14. Financial and workflow analysis of radiology reporting processes in the planning phase of implementation of a speech recognition system

    Science.gov (United States)

    Whang, Tom; Ratib, Osman M.; Umamoto, Kathleen; Grant, Edward G.; McCoy, Michael J.

    2002-05-01

    The goal of this study is to determine the financial value and workflow improvements achievable by replacing traditional transcription services with a speech recognition system in a large, university hospital setting. Workflow metrics were measured at two hospitals, one of which exclusively uses a transcription service (UCLA Medical Center), and the other which exclusively uses speech recognition (West Los Angeles VA Hospital). Workflow metrics include time spent per report (the sum of time spent interpreting, dictating, reviewing, and editing), transcription turnaround, and total report turnaround. Compared to traditional transcription, speech recognition resulted in radiologists spending 13-32% more time per report, but it also resulted in reduction of report turnaround time by 22-62% and reduction of marginal cost per report by 94%. The model developed here helps justify the introduction of a speech recognition system by showing that the benefits of reduced operating costs and decreased turnaround time outweigh the cost of increased time spent per report. Whether the ultimate goal is to achieve a financial objective or to improve operational efficiency, it is important to conduct a thorough analysis of workflow before implementation.

  15. Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

    Science.gov (United States)

    Alkilani, Amjad; Shirkhodaie, Amir

    2013-05-01

    Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

  16. Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system.

    Science.gov (United States)

    Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari Hormozi, Shahram; Wah, Teh Ying; Aghabozorgi, Saeed; Pourhoseingholi, Mohamad Amin; Olariu, Teodora

    2015-04-01

    Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for

  17. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

    Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

  18. Development of an automated speech recognition interface for personal emergency response systems

    Directory of Open Access Journals (Sweden)

    Mihailidis Alex

    2009-07-01

    Full Text Available Abstract Background Demands on long-term-care facilities are predicted to increase at an unprecedented rate as the baby boomer generation reaches retirement age. Aging-in-place (i.e. aging at home is the desire of most seniors and is also a good option to reduce the burden on an over-stretched long-term-care system. Personal Emergency Response Systems (PERSs help enable older adults to age-in-place by providing them with immediate access to emergency assistance. Traditionally they operate with push-button activators that connect the occupant via speaker-phone to a live emergency call-centre operator. If occupants do not wear the push button or cannot access the button, then the system is useless in the event of a fall or emergency. Additionally, a false alarm or failure to check-in at a regular interval will trigger a connection to a live operator, which can be unwanted and intrusive to the occupant. This paper describes the development and testing of an automated, hands-free, dialogue-based PERS prototype. Methods The prototype system was built using a ceiling mounted microphone array, an open-source automatic speech recognition engine, and a 'yes' and 'no' response dialog modelled after an existing call-centre protocol. Testing compared a single microphone versus a microphone array with nine adults in both noisy and quiet conditions. Dialogue testing was completed with four adults. Results and discussion The microphone array demonstrated improvement over the single microphone. In all cases, dialog testing resulted in the system reaching the correct decision about the kind of assistance the user was requesting. Further testing is required with elderly voices and under different noise conditions to ensure the appropriateness of the technology. Future developments include integration of the system with an emergency detection method as well as communication enhancement using features such as barge-in capability. Conclusion The use of an automated

  19. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2005-03-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  20. Autonomic nervous system dynamics for mood and emotional-state recognition significant advances in data acquisition, signal processing and classification

    CERN Document Server

    Valenza, Gaetano

    2014-01-01

    This monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and ...

  1. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2008-11-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  2. Optical Pattern Recognition

    Science.gov (United States)

    Yu, Francis T. S.; Jutamulia, Suganda

    2008-10-01

    Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

  3. Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

    Science.gov (United States)

    Abu-Alqumsan, Mohammad; Ebert, Felix; Peer, Angelika

    2017-06-01

    Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the

  4. Target recognition and scene interpretation in image/video understanding systems based on network-symbolic models

    Science.gov (United States)

    Kuvich, Gary

    2004-08-01

    Vision is only a part of a system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. These mechanisms provide a reliable recognition if the object is occluded or cannot be recognized as a whole. It is hard to split the entire system apart, and reliable solutions to the target recognition problems are possible only within the solution of a more generic Image Understanding Problem. Brain reduces informational and computational complexities, using implicit symbolic coding of features, hierarchical compression, and selective processing of visual information. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, is the most feasible for such models. It converts visual information into relational Network-Symbolic structures, avoiding artificial precise computations of 3-dimensional models. Network-Symbolic Transformations derive abstract structures, which allows for invariant recognition of an object as exemplar of a class. Active vision helps creating consistent models. Attention, separation of figure from ground and perceptual grouping are special kinds of network-symbolic transformations. Such Image/Video Understanding Systems will be reliably recognizing targets.

  5. Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images.

    Science.gov (United States)

    Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

    2018-05-22

    Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

  6. Paradigms in object recognition

    International Nuclear Information System (INIS)

    Mutihac, R.; Mutihac, R.C.

    1999-09-01

    A broad range of approaches has been proposed and applied for the complex and rather difficult task of object recognition that involves the determination of object characteristics and object classification into one of many a priori object types. Our paper revises briefly the three main different paradigms in pattern recognition, namely Bayesian statistics, neural networks, and expert systems. (author)

  7. Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.

    Science.gov (United States)

    Lemaire, Edward D; Tundo, Marco D; Baddour, Natalie

    2015-12-11

    recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

  8. Description and Recognition of the Concept of Social Capital in Higher Education System

    Science.gov (United States)

    Tonkaboni, Forouzan; Yousefy, Alireza; Keshtiaray, Narges

    2013-01-01

    The current research is intended to describe and recognize the concept of social capital in higher education based on theoretical method in a descriptive-analytical approach. Description and Recognition of the data, gathered from theoretical and experimental studies, indicated that social capital is one of the most important indices for…

  9. Towards social touch intelligence: developing a robust system for automatic touch recognition

    NARCIS (Netherlands)

    Jung, Merel Madeleine

    2014-01-01

    Touch behavior is of great importance during social interaction. Automatic recognition of social touch is necessary to transfer the touch modality from interpersonal interaction to other areas such as Human-Robot Interaction (HRI). This paper describes a PhD research program on the automatic

  10. Automatic speech recognition used for evaluation of text-to-speech systems

    Czech Academy of Sciences Publication Activity Database

    Vích, Robert; Nouza, J.; Vondra, Martin

    -, č. 5042 (2008), s. 136-148 ISSN 0302-9743 R&D Projects: GA AV ČR 1ET301710509; GA AV ČR 1QS108040569 Institutional research plan: CEZ:AV0Z20670512 Keywords : speech recognition * speech processing Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  11. On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

    Directory of Open Access Journals (Sweden)

    Sid-Ahmed Selouani

    2003-07-01

    Full Text Available Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT in the mel-frequency domain with a genetic algorithm (GA to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs varying from 16 dB to −4 dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

  12. [Research on the application of grey system theory in the pattern recognition for chromatographic fingerprints of traditional Chinese medicine].

    Science.gov (United States)

    Wei, Hang; Lin, Li; Zhang, Yuan; Wang, Lianjing; Chen, Qinqun

    2013-02-01

    A model based on grey system theory was proposed for pattern recognition in chromatographic fingerprints (CF) of traditional Chinese medicine (TCM). The grey relational grade among the data series of each testing CF and the ideal CF was obtained by entropy and norm respectively, then the principle of "maximal matching degree" was introduced to make judgments, so as to achieve the purpose of variety identification and quality evaluation. A satisfactory result in the high performance liquid chromatographic (HPLC) analysis of 56 batches of different varieties of Exocarpium Citrus Grandis was achieved with this model. The errors in the chromatographic fingerprint analysis caused by traditional similarity method or grey correlation method were overcome, as the samples of Citrus grandis 'Tomentosa' and Citrus grandis (L.) Osbeck were correctly distinguished in the experiment. Furthermore in the study on the variety identification of Citrus grandis 'Tomentosa', the recognition rates were up to 92.85%, although the types and the contents of the chemical compositions of the samples were very close. At the same time, the model had the merits of low computation complexity and easy operation by computer programming. The research indicated that the grey system theory has good applicability to pattern recognition in the chromatographic fingerprints of TCM.

  13. A modern optical character recognition system in a real world clinical setting: some accuracy and feasibility observations.

    Science.gov (United States)

    Biondich, Paul G; Overhage, J Marc; Dexter, Paul R; Downs, Stephen M; Lemmon, Larry; McDonald, Clement J

    2002-01-01

    Advances in optical character recognition (OCR) software and computer hardware have stimulated a reevaluation of the technology and its ability to capture structured clinical data from preexisting paper forms. In our pilot evaluation, we measured the accuracy and feasibility of capturing vitals data from a pediatric encounter form that has been in use for over twenty years. We found that the software had a digit recognition rate of 92.4% (95% confidence interval: 91.6 to 93.2) overall. More importantly, this system was approximately three times as fast as our existing method of data entry. These preliminary results suggest that with further refinements in the approach and additional development, we may be able to incorporate OCR as another method for capturing structured clinical data.

  14. Application of pattern recognition technique on randon signals for automatic monitoring of dynamic systems with emphasis on nuclear reactors

    International Nuclear Information System (INIS)

    Nascimento, J.A. do.

    1981-01-01

    The time varying or noise component of dynamic system parameters contains information on the system state. Pattern recognition analysis of noise signals for such systems is a powerful technique for assessing 'system normality' or 'correct operation'. Data analysis with modern small computers enables the otherwise unmanageable volumes of data to be processed on line and the results presented in a meaningful form. These informations provide necessary data for maintaining the system at optimum operating conditions. An automatic pattern recognition program, PSDREC, developmed for the surveillance of nuclear reactor and rotating machinery is described, and the relevant theory is outlined. This program, which applies 8 statistical tests to calculated power spectral density (PSD) distributions, was earlier installed in a PDP-11/45 computer at IPEN. In this work it has been used to separately analyse recorded signals from three systems, namely an operational BWR power reactor (neutron signals), a water pump and a diesel motor (vibration signals). The latter two were, respectively, operated over a wide-range of flow and load conditions. The statistical tests were applied to frequency bands of (0,1-40) Hz, (0-1000) Hz and (0,20000) Hz. for the BWR, pump and diesel signal data, respectively. Operation and analysis conditions are given together with representative graphs of the analysed PSD distributions. Results of the tests - discussed in some detail - are considered to be satisfactory. (Author) [pt

  15. Impact of a PACS/RIS-integrated speech recognition system on radiology reporting time and report availability

    International Nuclear Information System (INIS)

    Trumm, C.G.; Glaser, C.; Paasche, V.; Kuettner, B.; Francke, M.; Nissen-Meyer, S.; Reiser, M.; Crispin, A.; Popp, P.

    2006-01-01

    Purpose: Quantification of the impact of a PACS/RIS-integrated speech recognition system (SRS) on the time expenditure for radiology reporting and on hospital-wide report availability (RA) in a university institution. Material and Methods: In a prospective pilot study, the following parameters were assessed for 669 radiographic examinations (CR): 1. time requirement per report dictation (TED: dictation time (s)/number of images [examination] x number of words [report]) with either a combination of PACS/tape-based dictation (TD: analog dictation device/minicassette/transcription) or PACS/RIS/speech recognition system (RR: remote recognition/transcription and OR: online recognition/self-correction by radiologist), respectively, and 2. the Report Turnaround Time (RTT) as the time interval from the entry of the first image into the PACS to the available RIS/HIS report. Two equal time periods were chosen retrospectively from the RIS database: 11/2002-2/2003 (only TD) and 11/2003-2/2004 (only RR or OR with speech recognition system [SRS]). The midterm (≥24 h, 24 h intervals) and short-term (< 24 h, 1 h intervals), RA after examination completion were calculated for all modalities and for Cr, CT, MR and XA/DS separately. The relative increase in the mid-term RA (RIMRA: related to total number of examinations in each time period) and increase in the short-term RA (ISRA: ratio of available reports during the 1st to 24th hour) were calculated. Results: Prospectively, there was a significant difference between TD/RR/OR (n=151/257/261) regarding mean TED (0.44/0.54/0.62 s [per word and image]) and mean RTT (10.47/6.65/1.27 h), respectively. Retrospectively, 37 898/39 680 reports were computed from the RIS database for the time periods of 11/2002-2/2003 and 11/2003-2/2004. For CR/CT there was a shift of the short-term RA to the first 6 hours after examination completion (mean cumulative RA 20% higher) with a more than three-fold increase in the total number of available

  16. A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor.

    Science.gov (United States)

    Park, Young Ho; Kwon, Seung Yong; Pham, Tuyen Danh; Park, Kang Ryoung; Jeong, Dae Sik; Yoon, Sungsoo

    2015-06-15

    An algorithm for recognizing banknotes is required in many fields, such as banknote-counting machines and automatic teller machines (ATM). Due to the size and cost limitations of banknote-counting machines and ATMs, the banknote image is usually captured by a one-dimensional (line) sensor instead of a conventional two-dimensional (area) sensor. Because the banknote image is captured by the line sensor while it is moved at fast speed through the rollers inside the banknote-counting machine or ATM, misalignment, geometric distortion, and non-uniform illumination of the captured images frequently occur, which degrades the banknote recognition accuracy. To overcome these problems, we propose a new method for recognizing banknotes. The experimental results using two-fold cross-validation for 61,240 United States dollar (USD) images show that the pre-classification error rate is 0%, and the average error rate for the final recognition of the USD banknotes is 0.114%.

  17. EBR-II [Experimental Breeder Reactor-II] system surveillance using pattern recognition software

    International Nuclear Information System (INIS)

    Mott, J.E.; Radtke, W.H.; King, R.W.

    1986-02-01

    The problem of most accurately determining the Experimental Breeder Reactor-II (EBR-II) reactor outlet temperature from currently available plant signals is investigated. Historically, the reactor outlet pipe was originally instrumented with 8 temperature sensors but, during 22 years of operation, all these instruments have failed except for one remaining thermocouple, and its output had recently become suspect. Using pattern recognition methods to compare values of 129 plant signals for similarities over a 7 month period spanning reconfiguration of the core and recalibration of many plant signals, it was determined that the remaining reactor outlet pipe thermocouple is still useful as an indicator of true mixed mean reactor outlet temperature. Application of this methodology to investigate one specific signal has automatically validated the vast majority of the 129 signals used for pattern recognition and also highlighted a few inconsistent signals for further investigation

  18. The Effects of Certain Background Noises on the Performance of a Voice Recognition System.

    Science.gov (United States)

    1980-09-01

    Principles in Experimental Design. New York: McGraw-Hill, 1962. Woodworth, R.S. and H. Schlosberg, Experimental Psychology, (Revised edition), New...collection iheet APPENDIX II EXPERIMENTAL PROTOCOL AND SUBJECTS’ INSTRICTJONS THIS IS AN EXPERIMENT DESIGNED TO EVALUJATE SOME ," lE RECOGNITION EQUIPMENT. I...37. CDR Paul Chatelier OUSD R&E Room 3D129 Pentagon Washington, D.C. 20301 38. Ralph Cleveland NFMSO Code 9333 Mechanicsburg, PA 17055 39. Clay Coler

  19. CERN's Merit Appraisal and Recognition System from the point of view of the supervisor

    CERN Multimedia

    CERN. Geneva

    2013-01-01

    The required training consists of 2 parts : This presentation explaining “CERN’s merit recognition system”, followed by a session of questions/answers – duration : 2 hours A training session on “How to get, as a supervisor, the most out of the annual interview” – duration : 1 day. This hands-on training focuses on how to set smart work and development objectives, how to give feedback and how to run the annual interview in a constructive way.

  20. FEATURE RECOGNITION BERBASIS CORNER DETECTION DENGAN METODE FAST, SURF DAN FLANN TREE UNTUK IDENTIFIKASI LOGO PADA AUGMENTED REALITY MOBILE SYSTEM

    Directory of Open Access Journals (Sweden)

    Rastri Prathivi

    2014-01-01

    Full Text Available Logo is a graphical symbol that is the identity of an organization, institution, or company. Logo is generally used to introduce to the public the existence of an organization, institution, or company. Through the existence of an agency logo can be seen by the public. Feature recognition is one of the processes that exist within an augmented reality system. One of uses augmented reality is able to recognize the identity of the logo through a camera.The first step to make a process of feature recognition is through the corner detection. Incorporation of several method such as FAST, SURF, and FLANN TREE for the feature detection process based corner detection feature matching up process, will have the better ability to detect the presence of a logo. Additionally when running the feature extraction process there are several issues that arise as scale invariant feature and rotation invariant feature. In this study the research object in the form of logo to the priority to make the process of feature recognition. FAST, SURF, and FLANN TREE method will detection logo with scale invariant feature and rotation invariant feature conditions. Obtained from this study will demonstration the accuracy from FAST, SURF, and FLANN TREE methods to solve the scale invariant and rotation invariant feature problems.

  1. Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

    Science.gov (United States)

    Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

    2018-03-01

    This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Within-individual variation in bullfrog vocalizations: implications for a vocally mediated social recognition system.

    Science.gov (United States)

    Bee, Mark A

    2004-12-01

    Acoustic signals provide a basis for social recognition in a wide range of animals. Few studies, however, have attempted to relate the patterns of individual variation in signals to behavioral discrimination thresholds used by receivers to discriminate among individuals. North American bullfrogs (Rana catesbeiana) discriminate among familiar and unfamiliar individuals based on individual variation in advertisement calls. The sources, patterns, and magnitudes of variation in eight acoustic properties of multiple-note advertisement calls were examined to understand how patterns of within-individual variation might either constrain, or provide additional cues for, vocal recognition. Six of eight acoustic properties exhibited significant note-to-note variation within multiple-note calls. Despite this source of within-individual variation, all call properties varied significantly among individuals, and multivariate analyses indicated that call notes were individually distinct. Fine-temporal and spectral call properties exhibited less within-individual variation compared to gross-temporal properties and contributed most toward statistically distinguishing among individuals. Among-individual differences in the patterns of within-individual variation in some properties suggest that within-individual variation could also function as a recognition cue. The distributions of among-individual and within-individual differences were used to generate hypotheses about the expected behavioral discrimination thresholds of receivers.

  3. The impact of limbic system morphology on facial emotion recognition in bipolar I disorder and healthy controls

    Directory of Open Access Journals (Sweden)

    Bio DS

    2013-05-01

    Full Text Available Danielle Soares Bio,1 Márcio Gerhardt Soeiro-de-Souza,1 Maria Concepción Garcia Otaduy,2 Rodrigo Machado-Vieira,3 Ricardo Alberto Moreno11Mood Disorders Unit, 2Institute of Radiology, Department and Institute of Psychiatry, School of Medicine, University of São Paulo, São Paulo, Brazil; 3Experimental Therapeutics and Pathophysiology Branch (ETPB, National Institute of Mental Health, NIMH NIH, Bethesda, MD, USAIntroduction: Impairments in facial emotion recognition (FER have been reported in bipolar disorder (BD subjects during all mood states. This study aims to investigate the impact of limbic system morphology on FER scores in BD subjects and healthy controls.Material and methods: Thirty-nine euthymic BD I (type I subjects and 40 healthy controls were subjected to a battery of FER tests and examined with 3D structural imaging of the amygdala and hippocampus.Results: The volume of these structures demonstrated a differential pattern of influence on FER scores in BD subjects and controls. In our control sample, larger left and right amygdala demonstrated to be associated to less recognition of sadness faces. In BD group, there was no impact of amygdala volume on FER but we observed a negative impact of the left hippocampus volume in the recognition of happiness while the right hippocampus volume positively impacted on the scores of happiness.Conclusion: Our results indicate that amygdala and hippocampus volumes have distinct effects on FER in BD subjects compared to controls. Knowledge of the neurobiological basis of the illness may help to provide further insights on the role of treatments and psychosocial interventions for BD. Further studies should explore how these effects of amygdala and hippocampus volumes on FER are associated with social networks and social network functioning.Keywords: bipolar disorder, social cognition, facial emotion recognition

  4. A REVIEW: OPTICAL CHARACTER RECOGNITION

    OpenAIRE

    Swati Tomar*1 & Amit Kishore2

    2018-01-01

    This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

  5. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

    Directory of Open Access Journals (Sweden)

    Fernando Castaño

    2017-09-01

    Full Text Available Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.. The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

  6. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.

    Science.gov (United States)

    Castaño, Fernando; Beruvides, Gerardo; Haber, Rodolfo E; Artuñedo, Antonio

    2017-09-14

    Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

  7. Virtualized Network Function Orchestration System and Experimental Network Based QR Recognition for a 5G Mobile Access Network

    Directory of Open Access Journals (Sweden)

    Misun Ahn

    2017-12-01

    Full Text Available This paper proposes a virtualized network function orchestration system based on Network Function Virtualization (NFV, one of the main technologies in 5G mobile networks. This system should provide connectivity between network devices and be able to create flexible network function and distribution. This system focuses more on access networks. By experimenting with various scenarios of user service established and activated in a network, we examine whether rapid adoption of new service is possible and whether network resources can be managed efficiently. The proposed method is based on Bluetooth transfer technology and mesh networking to provide automatic connections between network machines and on a Docker flat form, which is a container virtualization technology for setting and managing key functions. Additionally, the system includes a clustering and recovery measure regarding network function based on the Docker platform. We will briefly introduce the QR code perceived service as a user service to examine the proposal and based on this given service, we evaluate the function of the proposal and present analysis. Through the proposed approach, container relocation has been implemented according to a network device’s CPU usage and we confirm successful service through function evaluation on a real test bed. We estimate QR code recognition speed as the amount of network equipment is gradually increased, improving user service and confirm that the speed of recognition is increased as the assigned number of network devices is increased by the user service.

  8. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2018-02-01

    Full Text Available Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples. Therefore, a presentation attack detection (PAD method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP, local ternary pattern (LTP, and histogram of oriented gradients (HOG. As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN method to extract deep image features and the multi-level local binary pattern (MLBP method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

  9. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

    Science.gov (United States)

    Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

    2018-02-26

    Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

  10. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

    Science.gov (United States)

    Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

    2018-01-01

    Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

  11. Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2016-07-01

    Full Text Available With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG, which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

  12. Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

    Science.gov (United States)

    Nguyen, Dat Tien; Park, Kang Ryoung

    2016-07-21

    With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

  13. Study of Robust Position Recognition System of a Mobile Robot Using Multiple Cameras and Absolute Space Coordinates

    International Nuclear Information System (INIS)

    Mo, Se Hyun; Jeon, Young Pil; Park, Jong Ho; Chong, Kil To

    2017-01-01

    With the development of ICT technology, the indoor utilization of robots is increasing. Research on transportation, cleaning, guidance robots, etc., that can be used now or increase the scope of future use will be advanced. To facilitate the use of mobile robots in indoor spaces, the problem of self-location recognition is an important research area to be addressed. If an unexpected collision occurs during the motion of a mobile robot, the position of the mobile robot deviates from the initially planned navigation path. In this case, the mobile robot needs a robust controller that enables the mobile robot to accurately navigate toward the goal. This research tries to address the issues related to self-location of the mobile robot. A robust position recognition system was implemented; the system estimates the position of the mobile robot using a combination of encoder information of the mobile robot and the absolute space coordinate transformation information obtained from external video sources such as a large number of CCTVs installed in the room. Furthermore, vector field histogram method of the pass traveling algorithm of the mobile robot system was applied, and the results of the research were confirmed after conducting experiments.

  14. A Tumor-specific MicroRNA Recognition System Facilitates the Accurate Targeting to Tumor Cells by Magnetic Nanoparticles

    Directory of Open Access Journals (Sweden)

    Yingting Yu

    2016-01-01

    Full Text Available Targeted therapy for cancer is a research area of great interest, and magnetic nanoparticles (MNPs show great potential as targeted carriers for therapeutics. One important class of cancer biomarkers is microRNAs (miRNAs, which play a significant role in tumor initiation and progression. In this study, a cascade recognition system containing multiple plasmids, including a Tet activator, a lacI repressor gene driven by the TetOn promoter, and a reporter gene repressed by the lacI repressor and influenced by multiple endogenous miRNAs, was used to recognize cells that display miRNA signals that are characteristic of cancer. For this purpose, three types of signal miRNAs with high proliferation and metastasis abilities were chosen (miR-21, miR-145, and miR-9. The response of this system to the human breast cancer MCF-7 cell line was 3.2-fold higher than that to the human breast epithelial HBL100 cell line and almost 7.5-fold higher than that to human embryonic kidney HEK293T cells. In combination with polyethyleneimine-modified MNPs, this recognition system targeted the tumor location in situ in an animal model, and an ≃42% repression of tumor growth was achieved. Our study provides a new combination of magnetic nanocarrier and gene therapy based on miRNAs that are active in vivo, which has potential for use in future cancer therapies.

  15. Study of Robust Position Recognition System of a Mobile Robot Using Multiple Cameras and Absolute Space Coordinates

    Energy Technology Data Exchange (ETDEWEB)

    Mo, Se Hyun [Amotech, Seoul (Korea, Republic of); Jeon, Young Pil [Samsung Electronics Co., Ltd. Suwon (Korea, Republic of); Park, Jong Ho [Seonam Univ., Namwon (Korea, Republic of); Chong, Kil To [Chon-buk Nat' 1 Univ., Junju (Korea, Republic of)

    2017-07-15

    With the development of ICT technology, the indoor utilization of robots is increasing. Research on transportation, cleaning, guidance robots, etc., that can be used now or increase the scope of future use will be advanced. To facilitate the use of mobile robots in indoor spaces, the problem of self-location recognition is an important research area to be addressed. If an unexpected collision occurs during the motion of a mobile robot, the position of the mobile robot deviates from the initially planned navigation path. In this case, the mobile robot needs a robust controller that enables the mobile robot to accurately navigate toward the goal. This research tries to address the issues related to self-location of the mobile robot. A robust position recognition system was implemented; the system estimates the position of the mobile robot using a combination of encoder information of the mobile robot and the absolute space coordinate transformation information obtained from external video sources such as a large number of CCTVs installed in the room. Furthermore, vector field histogram method of the pass traveling algorithm of the mobile robot system was applied, and the results of the research were confirmed after conducting experiments.

  16. Construction of in vitro transcription system for Corynebacterium glutamicum and its use in the recognition of promoters of different classes.

    Science.gov (United States)

    Holátko, Jiří; Silar, Radoslav; Rabatinová, Alžbeta; Sanderová, Hana; Halada, Petr; Nešvera, Jan; Krásný, Libor; Pátek, Miroslav

    2012-10-01

    To facilitate transcription studies in Corynebacterium glutamicum, we have developed an in vitro transcription system for this bacterium used as an industrial producer of amino acids and a model organism for actinobacteria. This system consists of C. glutamicum RNA polymerase (RNAP) core (α2, β, β'), a sigma factor and a promoter-carrying DNA template, that is specifically recognized by the RNAP holoenzyme formed. The RNAP core was purified from the C. glutamicum strain with the modified rpoC gene, which produced His-tagged β' subunit. The C. glutamicum sigA and sigH genes were cloned and overexpressed using the Escherichia coli plasmid vector, and the sigma subunits σ(A) and σ(H) were purified by affinity chromatography. Using the reconstituted C. glutamicum holo-RNAPs, recognition of the σ(A)- and σ(H)-dependent promoters and synthesis of the specific transcripts was demonstrated. The developed in vitro transcription system is a novel tool that can be used to directly prove the specific recognition of a promoter by the particular σ factor(s) and to analyze transcriptional control by various regulatory proteins in C. glutamicum.

  17. A self-teaching image processing and voice-recognition-based, intelligent and interactive system to educate visually impaired children

    Science.gov (United States)

    Iqbal, Asim; Farooq, Umar; Mahmood, Hassan; Asad, Muhammad Usman; Khan, Akrama; Atiq, Hafiz Muhammad

    2010-02-01

    A self teaching image processing and voice recognition based system is developed to educate visually impaired children, chiefly in their primary education. System comprises of a computer, a vision camera, an ear speaker and a microphone. Camera, attached with the computer system is mounted on the ceiling opposite (on the required angle) to the desk on which the book is placed. Sample images and voices in the form of instructions and commands of English, Urdu alphabets, Numeric Digits, Operators and Shapes are already stored in the database. A blind child first reads the embossed character (object) with the help of fingers than he speaks the answer, name of the character, shape etc into the microphone. With the voice command of a blind child received by the microphone, image is taken by the camera which is processed by MATLAB® program developed with the help of Image Acquisition and Image processing toolbox and generates a response or required set of instructions to child via ear speaker, resulting in self education of a visually impaired child. Speech recognition program is also developed in MATLAB® with the help of Data Acquisition and Signal Processing toolbox which records and process the command of the blind child.

  18. Smart dosimetry by pattern recognition using a single photon counting detector system in time over threshold mode

    International Nuclear Information System (INIS)

    Reza, S; Wong, W S; Fröjdh, E; Norlin, B; Fröjdh, C; Thungström, G; Thim, J

    2012-01-01

    The function of a dosimeter is to determine the absorbed dose of radiation, for those cases in which, generally, the particular type of radiation is already known. Lately, a number of applications have emerged in which all kinds of radiation are absorbed and are sorted by pattern recognition, such as the Medipix2 application in [1]. This form of smart dosimetry enables measurements where not only the total dosage is measured, but also the contributions of different types of radiation impacting upon the detector surface. Furthermore, the use of a photon counting system, where the energy deposition can be measured in each individual pixel, ensures measurements with a high degree of accuracy in relation to the pattern recognition. In this article a Timepix [2] detector system has been used in the creation of a smart dosimeter for Alpha, Beta and Gamma radiation. When a radioactive particle hits the detector surface it generates charge clusters and those impacting upon the detector surface are read out and image processing algorithms are then used to classify each charge cluster. The individual clusters are calculated and as a result, the dosage for each type of radiation is given. In some cases, several particles can impact in roughly the same place, forming overlapping clusters. In order to handle this problem, a cluster separation method has been added to the pattern recognition algorithm. When the clusters have been separated, they are classified by shape and sorted into the correct type of radiation. The algorithms and methods used in this dosimeter have been developed so as to be simple and computationally effective, in order to enable implementation on a portable device.

  19. RGBD Video Based Human Hand Trajectory Tracking and Gesture Recognition System

    Directory of Open Access Journals (Sweden)

    Weihua Liu

    2015-01-01

    Full Text Available The task of human hand trajectory tracking and gesture trajectory recognition based on synchronized color and depth video is considered. Toward this end, in the facet of hand tracking, a joint observation model with the hand cues of skin saliency, motion and depth is integrated into particle filter in order to move particles to local peak in the likelihood. The proposed hand tracking method, namely, salient skin, motion, and depth based particle filter (SSMD-PF, is capable of improving the tracking accuracy considerably, in the context of the signer performing the gesture toward the camera device and in front of moving, cluttered backgrounds. In the facet of gesture recognition, a shape-order context descriptor on the basis of shape context is introduced, which can describe the gesture in spatiotemporal domain. The efficient shape-order context descriptor can reveal the shape relationship and embed gesture sequence order information into descriptor. Moreover, the shape-order context leads to a robust score for gesture invariant. Our approach is complemented with experimental results on the settings of the challenging hand-signed digits datasets and American sign language dataset, which corroborate the performance of the novel techniques.

  20. A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor

    Directory of Open Access Journals (Sweden)

    Young Ho Park

    2015-06-01

    Full Text Available An algorithm for recognizing banknotes is required in many fields, such as banknote-counting machines and automatic teller machines (ATM. Due to the size and cost limitations of banknote-counting machines and ATMs, the banknote image is usually captured by a one-dimensional (line sensor instead of a conventional two-dimensional (area sensor. Because the banknote image is captured by the line sensor while it is moved at fast speed through the rollers inside the banknote-counting machine or ATM, misalignment, geometric distortion, and non-uniform illumination of the captured images frequently occur, which degrades the banknote recognition accuracy. To overcome these problems, we propose a new method for recognizing banknotes. The experimental results using two-fold cross-validation for 61,240 United States dollar (USD images show that the pre-classification error rate is 0%, and the average error rate for the final recognition of the USD banknotes is 0.114%.

  1. Kernel learning algorithms for face recognition

    CERN Document Server

    Li, Jun-Bao; Pan, Jeng-Shyang

    2013-01-01

    Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

  2. Optical Character Recognition.

    Science.gov (United States)

    Converso, L.; Hocek, S.

    1990-01-01

    This paper describes computer-based optical character recognition (OCR) systems, focusing on their components (the computer, the scanner, the OCR, and the output device); how the systems work; and features to consider in selecting a system. A list of 26 questions to ask to evaluate systems for potential purchase is included. (JDD)

  3. Research on operation and maintenance support system adaptive to human recognition and understanding in human-centered plant

    International Nuclear Information System (INIS)

    Numano, Masayoshi; Matsuoka, Takeshi; Mitomo, N.

    2004-01-01

    As a human-centered plant, advanced nuclear power plant needs appropriate role sharing between human and mobile intelligent agents. Human-machine cooperation for plant operation and maintenance activities is also required with an advanced interface. Plant's maintenance is programmed using mobile robots working under the radiation environments instead of human beings. Operation and maintenance support system adaptive to human recognition and understanding should be developed to establish adequate human and machine interface so as to induce human capabilities to the full and enable human to take responsibility for plan's operation. Plant's operation and maintenance can be cooperative activities between human and intelligent automonous agents having surveillance and control functions. Infrastructure of multi-agent simulation system for the support system has been investigated and developed based on work plans derived from the scheduler. (T. Tanaka)

  4. The Performance Assessment of the Detector for the Portable Environmental Radiation Distribution Monitoring System with Rapid Nuclide Recognition

    International Nuclear Information System (INIS)

    Lee, Uk Jae; Kim, Hee Reyoung

    2015-01-01

    The environment radiation distribution monitoring system measures the radiation using a portable detector and display the overall radiation distribution. Bluetooth and RS-232 communications are used for constructing monitoring system. However RS-232 serial communication is known to be more stable than Bluetooth and also it can use the detector's raw data which will be used for getting the activity of each artificial nuclide. In the present study, the detection and communication performance of the developed detector with RS-232 method is assessed by using standard sources for the real application to the urban or rural environment. Assessment of the detector for the portable environmental radiation distribution monitoring system with rapid nuclide recognition was carried out. It was understood that the raw data of detector could be effectively treated by using RS-232 method and the measurement showed a good agreement with the calculation within the relative error of 0.4 % in maximum

  5. The Performance Assessment of the Detector for the Portable Environmental Radiation Distribution Monitoring System with Rapid Nuclide Recognition

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Uk Jae; Kim, Hee Reyoung [Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

    2015-05-15

    The environment radiation distribution monitoring system measures the radiation using a portable detector and display the overall radiation distribution. Bluetooth and RS-232 communications are used for constructing monitoring system. However RS-232 serial communication is known to be more stable than Bluetooth and also it can use the detector's raw data which will be used for getting the activity of each artificial nuclide. In the present study, the detection and communication performance of the developed detector with RS-232 method is assessed by using standard sources for the real application to the urban or rural environment. Assessment of the detector for the portable environmental radiation distribution monitoring system with rapid nuclide recognition was carried out. It was understood that the raw data of detector could be effectively treated by using RS-232 method and the measurement showed a good agreement with the calculation within the relative error of 0.4 % in maximum.

  6. Involvement of the oxytocin system in the bed nucleus of the stria terminalis in the sex-specific regulation of social recognition

    Science.gov (United States)

    Dumais, Kelly M.; Alonso, Andrea G.; Immormino, Marisa A.; Bredewold, Remco; Veenema, Alexa H.

    2015-01-01

    Sex differences in the oxytocin (OT) system in the brain may explain why OT often regulates social behaviors in sex-specific ways. However, a link between sex differences in the OT system and sex-specific regulation of social behavior has not been tested. Here, we determined whether sex differences in the OT receptor (OTR) or in OT release in the posterior bed nucleus of the stria terminalis (pBNST) mediates sex-specific regulation of social recognition in rats. We recently showed that, compared to female rats, male rats have a three-fold higher OTR binding density in the pBNST, a sexually dimorphic area implicated in the regulation of social behaviors. We now demonstrate that OTR antagonist (5 ng/0.5 μl/side) administration into the pBNST impairs social recognition in both sexes, while OT (100 pg/0.5 μl/side) administration into the pBNST prolongs the duration of social recognition in males only. These effects seem specific to social recognition, as neither treatment altered total social investigation time in either sex. Moreover, baseline OT release in the pBNST, as measured with in vivo microdialysis, did not differ between the sexes. However, males showed higher OT release in the pBNST during social recognition compared to females. These findings suggest a sex-specific role of the OT system in the pBNST in the regulation of social recognition. PMID:26630388

  7. Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

    Science.gov (United States)

    Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

    2017-01-01

    People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

  8. Influence of different envelope maskers on signal recognition and neuronal representation in the auditory system of a grasshopper.

    Directory of Open Access Journals (Sweden)

    Daniela Neuhofer

    Full Text Available BACKGROUND: Animals that communicate by sound face the problem that the signals arriving at the receiver often are degraded and masked by noise. Frequency filters in the receiver's auditory system may improve the signal-to-noise ratio (SNR by excluding parts of the spectrum which are not occupied by the species-specific signals. This solution, however, is hardly amenable to species that produce broad band signals or have ears with broad frequency tuning. In mammals auditory filters exist that work in the temporal domain of amplitude modulations (AM. Do insects also use this type of filtering? PRINCIPAL FINDINGS: Combining behavioural and neurophysiological experiments we investigated whether AM filters may improve the recognition of masked communication signals in grasshoppers. The AM pattern of the sound, its envelope, is crucial for signal recognition in these animals. We degraded the species-specific song by adding random fluctuations to its envelope. Six noise bands were used that differed in their overlap with the spectral content of the song envelope. If AM filters contribute to reduced masking, signal recognition should depend on the degree of overlap between the song envelope spectrum and the noise spectra. Contrary to this prediction, the resistance against signal degradation was the same for five of six masker bands. Most remarkably, the band with the strongest frequency overlap to the natural song envelope (0-100 Hz impaired acceptance of degraded signals the least. To assess the noise filter capacities of single auditory neurons, the changes of spike trains as a function of the masking level were assessed. Increasing levels of signal degradation in different frequency bands led to similar changes in the spike trains in most neurones. CONCLUSIONS: There is no indication that auditory neurones of grasshoppers are specialized to improve the SNR with respect to the pattern of amplitude modulations.

  9. Selecting Informative Features of the Helicopter and Aircraft Acoustic Signals in the Adaptive Autonomous Information Systems for Recognition

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2017-01-01

    Full Text Available The article forms the rationale for selecting the informative features of the helicopter and aircraft acoustic signals to solve a problem of their recognition and shows that the most informative ones are the counts of extrema in the energy spectra of the input signals, which represent non-centered random variables. An apparatus of the multiple initial regression coefficients was selected as a mathematical tool of research. The application of digital re-circulators with positive and negative feedbacks, which have the comb-like frequency characteristics, solves the problem of selecting informative features. A distinguishing feature of such an approach is easy agility of the comb frequency characteristics just through the agility of a delay value of digital signal in the feedback circuit. Adding an adaptation block to the selection block of the informative features enables us to ensure the invariance of used informative feature and counts of local extrema of the spectral power density to the airspeed of a helicopter. The paper gives reasons for the principle of adaptation and the structure of the adaptation block. To form the discriminator characteristics are used the cross-correlation statistical characteristics of the quadrature components of acoustic signal realizations, obtained by Hilbert transform. The paper proposes to provide signal recognition using a regression algorithm that allows handling the non-centered informative features and using a priori information about coefficients of initial regression of signal and noise.The simulation in Matlab Simulink has shown that selected informative features of signals in regressive processing of signal realizations of 0.5 s duration have good separability, and based on a set of 100 acoustic signal realizations in each class in full-scale conditions, has proved ensuring a correct recognition probability of 0.975, at least. The considered principles of informative features selection and adaptation can

  10. A non-linear discrete transform for pattern recognition of discrete chaotic systems

    International Nuclear Information System (INIS)

    Karanikas, C.; Proios, G.

    2003-01-01

    It is shown, by an invertible non-linear discrete transform that any finite sequence or any collection of strings of any length can be presented as a random walk on trees. These transforms create the mathematical background for coding any information, for exploring its local variability and diversity. With the underlying computational algorithms, with several examples and applications we propose that these transforms can be used for pattern recognition of immune type. In other words we propose a mathematical platform for detecting self and non-self strings of any alphabet, based on a negative selection algorithms, for scouting data's periodicity and self-similarity and for measuring the diversity of chaotic strings with fractal dimension methods. In particular we estimate successfully the entropy and the ratio of chaotic data with self similarity. Moreover we give some applications of a non-linear denoising filter

  11. A non-linear discrete transform for pattern recognition of discrete chaotic systems

    CERN Document Server

    Karanikas, C

    2003-01-01

    It is shown, by an invertible non-linear discrete transform that any finite sequence or any collection of strings of any length can be presented as a random walk on trees. These transforms create the mathematical background for coding any information, for exploring its local variability and diversity. With the underlying computational algorithms, with several examples and applications we propose that these transforms can be used for pattern recognition of immune type. In other words we propose a mathematical platform for detecting self and non-self strings of any alphabet, based on a negative selection algorithms, for scouting data's periodicity and self-similarity and for measuring the diversity of chaotic strings with fractal dimension methods. In particular we estimate successfully the entropy and the ratio of chaotic data with self similarity. Moreover we give some applications of a non-linear denoising filter.

  12. Design of environmental monitoring system of nuclear facility based on a method of pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

    Katoh, N; Kiyose, R; Yamamoto, Y [Tokyo Univ. (Japan). Faculty of Engineering

    1977-10-01

    The problem to optimize the number and locations of environmental radiation monitoring detectors is formulated by taking the specifically defined distance measures as a performance index and solved numerically using heuristic programming such as branch and bound method. An ideal numerical example neglecting noises due to background radiation, shows that the desirable number and locations of detectors are determined mainly by the atmospheric conditions and are not significantly influenced by the variation of the rate and pattern of activity release from the nuclear facility. It is shown also that the appropriate and sufficient number of monitoring detectors to be located around the facility will be from three to six at most, if considered from the viewpoint of pattern recognition.

  13. Trade off between variable and fixed size normalization in orthogonal polynomials based iris recognition system.

    Science.gov (United States)

    Krishnamoorthi, R; Anna Poorani, G

    2016-01-01

    Iris normalization is an important stage in any iris biometric, as it has a propensity to trim down the consequences of iris distortion. To indemnify the variation in size of the iris owing to the action of stretching or enlarging the pupil in iris acquisition process and camera to eyeball distance, two normalization schemes has been proposed in this work. In the first method, the iris region of interest is normalized by converting the iris into the variable size rectangular model in order to avoid the under samples near the limbus border. In the second method, the iris region of interest is normalized by converting the iris region into a fixed size rectangular model in order to avoid the dimensional discrepancies between the eye images. The performance of the proposed normalization methods is evaluated with orthogonal polynomials based iris recognition in terms of FAR, FRR, GAR, CRR and EER.

  14. Evaluating music emotion recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2013-01-01

    A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do...... not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER system, and conclude with recommendations....

  15. Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Aleš Procházka

    2018-05-01

    Full Text Available Multimodal signal analysis based on sophisticated sensors, efficient communicationsystems and fast parallel processing methods has a rapidly increasing range of multidisciplinaryapplications. The present paper is devoted to pattern recognition, machine learning, and the analysisof sleep stages in the detection of sleep disorders using polysomnography (PSG data, includingelectroencephalography (EEG, breathing (Flow, and electro-oculogram (EOG signals. The proposedmethod is based on the classification of selected features by a neural network system with sigmoidaland softmax transfer functions using Bayesian methods for the evaluation of the probabilities of theseparate classes. The application is devoted to the analysis of the sleep stages of 184 individualswith different diagnoses, using EEG and further PSG signals. Data analysis points to an averageincrease of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of theRapid Eye Movement (REM stages by 0.8% per 10 years. The mean classification accuracy for givensets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1 and89.6% (STD:1.9, respectively. The proposed methods enable the use of adaptive learning processesfor the detection and classification of health disorders based on prior specialist experience andman–machine interaction.

  16. Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

    Science.gov (United States)

    Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

    2012-10-01

    The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

  17. Two-dimensional laser servoing for precision motion control of an ODV robotic license plate recognition system

    Science.gov (United States)

    Song, Zhen; Moore, Kevin L.; Chen, YangQuan; Bahl, Vikas

    2003-09-01

    As an outgrowth of series of projects focused on mobility of unmanned ground vehicles (UGV), an omni-directional (ODV), multi-robot, autonomous mobile parking security system has been developed. The system has two types of robots: the low-profile Omni-Directional Inspection System (ODIS), which can be used for under-vehicle inspections, and the mid-sized T4 robot, which serves as a ``marsupial mothership'' for the ODIS vehicles and performs coarse resolution inspection. A key task for the T4 robot is license plate recognition (LPR). For a successful LPR task without compromising the recognition rate, the robot must be able to identify the bumper locations of vehicles in the parking area and then precisely position the LPR camera relative to the bumper. This paper describes a 2D-laser scanner based approach to bumper identification and laser servoing for the T4 robot. The system uses a gimbal-mounted scanning laser. As the T4 robot travels down a row of parking stalls, data is collected from the laser every 100ms. For each parking stall in the range of the laser during the scan, the data is matched to a ``bumper box'' corresponding to where a car bumper is expected, resulting in a point cloud of data corresponding to a vehicle bumper for each stall. Next, recursive line-fitting algorithms are used to determine a line for the data in each stall's ``bumper box.'' The fitting technique uses Hough based transforms, which are robust against segmentation problems and fast enough for real-time line fitting. Once a bumper line is fitted with an acceptable confidence, the bumper location is passed to the T4 motion controller, which moves to position the LPR camera properly relative to the bumper. The paper includes examples and results that show the effectiveness of the technique, including its ability to work in real-time.

  18. Perceived Parenting Mediates Serotonin Transporter Gene (5-HTTLPR) and Neural System Function during Facial Recognition: A Pilot Study

    Science.gov (United States)

    Nishikawa, Saori

    2015-01-01

    This study examined changes in prefrontal oxy-Hb levels measured by NIRS (Near-Infrared Spectroscopy) during a facial-emotion recognition task in healthy adults, testing a mediational/moderational model of these variables. Fifty-three healthy adults (male = 35, female = 18) aged between 22 to 37 years old (mean age = 24.05 years old) provided saliva samples, completed a EMBU questionnaire (Swedish acronym for Egna Minnen Beträffande Uppfostran [My memories of upbringing]), and participated in a facial-emotion recognition task during NIRS recording. There was a main effect of maternal rejection on RoxH (right frontal activation during an ambiguous task), and a gene × environment (G×E) interaction on RoxH, suggesting that individuals who carry the SL or LL genotype and who endorse greater perceived maternal rejection show less right frontal activation than SL/LL carriers with lower perceived maternal rejection. Finally, perceived parenting style played a mediating role in right frontal activation via the 5-HTTLPR genotype. Early-perceived parenting might influence neural activity in an uncertain situation i.e. rating ambiguous faces among individuals with certain genotypes. This preliminary study makes a small contribution to the mapping of an influence of gene and behaviour on the neural system. More such attempts should be made in order to clarify the links. PMID:26418317

  19. Perceived Parenting Mediates Serotonin Transporter Gene (5-HTTLPR and Neural System Function during Facial Recognition: A Pilot Study.

    Directory of Open Access Journals (Sweden)

    Saori Nishikawa

    Full Text Available This study examined changes in prefrontal oxy-Hb levels measured by NIRS (Near-Infrared Spectroscopy during a facial-emotion recognition task in healthy adults, testing a mediational/moderational model of these variables. Fifty-three healthy adults (male = 35, female = 18 aged between 22 to 37 years old (mean age = 24.05 years old provided saliva samples, completed a EMBU questionnaire (Swedish acronym for Egna Minnen Beträffande Uppfostran [My memories of upbringing], and participated in a facial-emotion recognition task during NIRS recording. There was a main effect of maternal rejection on RoxH (right frontal activation during an ambiguous task, and a gene × environment (G × E interaction on RoxH, suggesting that individuals who carry the SL or LL genotype and who endorse greater perceived maternal rejection show less right frontal activation than SL/LL carriers with lower perceived maternal rejection. Finally, perceived parenting style played a mediating role in right frontal activation via the 5-HTTLPR genotype. Early-perceived parenting might influence neural activity in an uncertain situation i.e. rating ambiguous faces among individuals with certain genotypes. This preliminary study makes a small contribution to the mapping of an influence of gene and behaviour on the neural system. More such attempts should be made in order to clarify the links.

  20. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    Science.gov (United States)

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

  1. Helicase Dependent Isothermal Amplification of DNA and RNA using Self-Avoiding Molecular Recognition Systems

    Science.gov (United States)

    Yang, Zunyi; McLendon, Chris; Hutter, Daniel; Bradley, Kevin M.; Hoshika, Shuichi; Frye, Carole; Benner, Steven A.

    2015-01-01

    Assays that target DNA or RNA (xNA) are highly sensitive, as small amounts of xNA can be amplified by PCR. Unfortunately, PCR is inconvenient in low resource environments, requiring equipment and power that may not be available in these environments. However, isothermal procedures that avoid thermal cycling are often confounded by primer dimers, off-target priming, and other artifacts. Here, we show how a “self avoiding molecular recognition system” (SAMRS) eliminates these artifacts to give clean amplicons in a helicase-dependent isothermal amplification (SAMRS-HDA). We also show that incorporating SAMRS into the 3′-ends of primers facilitates the design and screening of primers for HDA assays. Finally, we show that SAMRS-HDA can be twofold multiplexed, something difficult to achieve with HDA using standard primers. This shows that SAMRS-HDA is a more versatile approach than standard HDA with a broader applicability for xNA-targeted diagnostics and research. PMID:25953623

  2. Modulation of the storage of social recognition memory by neurotransmitter systems in the insular cortex.

    Science.gov (United States)

    Cavalcante, Lorena E S; Zinn, Carolina G; Schmidt, Scheila D; Saenger, Bruna F; Ferreira, Flávia F; Furini, Cristiane R G; Myskiw, Jociane C; Izquierdo, Ivan

    2017-09-15

    The insular cortex (IC) receives projections from prefrontal, entorhinal and cingulate cortex, olfactory bulb and basal nuclei and has reciprocal connections with the amygdala and entorhinal cortex. These connections suggest a possible involvement in memory processes; this has been borne out by data on several behaviors. Social recognition memory (SRM) is essential to form social groups and to establish hierarchies and social and affective ties. Despite its importance, knowledge about the brain structures and the neurotransmitter mechanisms involved in its processing is still scarce. Here we study the participation of NMDA-glutamatergic, D1/D5-dopaminergic, H2-histaminergic, β-adrenergic and 5-HT 1A -serotoninergic receptors of the IC in the consolidation of SRM. Male Wistar rats received intra-IC infusions of substances acting on these receptors immediately after the sample phase of a social discrimination task and 24h later were exposed to a 5-min retention test. The intra-IC infusion of antagonists of D1/D5, β-adrenergic or 5-HT 1A receptors immediately after the sample phase impaired the consolidation of SRM. These effects were blocked by the concomitant intra-IC infusion of agonists of these receptors. Antagonists and agonists of NMDA and H2 receptors had no effect on SRM. The results suggest that the dopaminergic D1/D5, β-adrenergic and serotonergic 5-HT 1A receptors in the IC, but not glutamatergic NMDA and the histaminergic H2 receptors, participate in the consolidation of SRM in the IC. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

    Science.gov (United States)

    Kaplan, Bernhard A.; Lansner, Anders

    2014-01-01

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID

  4. Performance Comparison of Several Pre-Processing Methods in a Hand Gesture Recognition System based on Nearest Neighbor for Different Background Conditions

    Directory of Open Access Journals (Sweden)

    Iwan Setyawan

    2012-12-01

    Full Text Available This paper presents a performance analysis and comparison of several pre-processing methods used in a hand gesture recognition system. The pre-processing methods are based on the combinations of several image processing operations, namely edge detection, low pass filtering, histogram equalization, thresholding and desaturation. The hand gesture recognition system is designed to classify an input image into one of six possible classes. The input images are taken with various background conditions. Our experiments showed that the best result is achieved when the pre-processing method consists of only a desaturation operation, achieving a classification accuracy of up to 83.15%.

  5. Sudden Event Recognition: A Survey

    Directory of Open Access Journals (Sweden)

    Mohd Asyraf Zulkifley

    2013-08-01

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

  6. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets.

    Science.gov (United States)

    Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W

    2016-09-01

    Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.

  7. [Development of image quality assurance support system using image recognition technology in radiography in lacked images of chest and abdomen].

    Science.gov (United States)

    Shibuya, Toru; Kato, Kyouichi; Eshima, Hidekazu; Sumi, Shinichirou; Kubo, Tadashi; Ishida, Hideki; Nakazawa, Yasuo

    2012-01-01

    In order to provide a precise radiography for diagnosis, it is required that we avoid radiography with defects by having enough evaluation. Conventionally, evaluation was performed only by observation of a radiological technologist (RT). The evaluation support system was developed for providing a high quality assurance without depending on RT observation only. The evaluation support system, called as the Image Quality Assurance Support System (IQASS), is characterized in that "image recognition technology" for the purpose of diagnostic radiography of chest and abdomen areas. The technique of the system used in this study. Of the 259 samples of posterior-anterior (AP) chest, lateral chest, and upright abdominal x-rays, the sensitivity and specificity was 93.1% and 91.8% in the chest AP, 93.3% and 93.6% in the chest lateral, and 95.0% and 93.8% in the upright abdominal x-rays. In the light of these results, it is suggested that AIQAS could be applied to practical usage for the RT.

  8. An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

    Science.gov (United States)

    Rundo, Francesco; Conoci, Sabrina; Ortis, Alessandro; Battiato, Sebastiano

    2018-01-30

    Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

  9. Scheimpflug with computational imaging to extend the depth of field of iris recognition systems

    Science.gov (United States)

    Sinharoy, Indranil

    Despite the enormous success of iris recognition in close-range and well-regulated spaces for biometric authentication, it has hitherto failed to gain wide-scale adoption in less controlled, public environments. The problem arises from a limitation in imaging called the depth of field (DOF): the limited range of distances beyond which subjects appear blurry in the image. The loss of spatial details in the iris image outside the small DOF limits the iris image capture to a small volume-the capture volume. Existing techniques to extend the capture volume are usually expensive, computationally intensive, or afflicted by noise. Is there a way to combine the classical Scheimpflug principle with the modern computational imaging techniques to extend the capture volume? The solution we found is, surprisingly, simple; yet, it provides several key advantages over existing approaches. Our method, called Angular Focus Stacking (AFS), consists of capturing a set of images while rotating the lens, followed by registration, and blending of the in-focus regions from the images in the stack. The theoretical underpinnings of AFS arose from a pair of new and general imaging models we developed for Scheimpflug imaging that directly incorporates the pupil parameters. The model revealed that we could register the images in the stack analytically if we pivot the lens at the center of its entrance pupil, rendering the registration process exact. Additionally, we found that a specific lens design further reduces the complexity of image registration making AFS suitable for real-time performance. We have demonstrated up to an order of magnitude improvement in the axial capture volume over conventional image capture without sacrificing optical resolution and signal-to-noise ratio. The total time required for capturing the set of images for AFS is less than the time needed for a single-exposure, conventional image for the same DOF and brightness level. The net reduction in capture time can

  10. Speaker Recognition

    DEFF Research Database (Denmark)

    Mølgaard, Lasse Lohilahti; Jørgensen, Kasper Winther

    2005-01-01

    Speaker recognition is basically divided into speaker identification and speaker verification. Verification is the task of automatically determining if a person really is the person he or she claims to be. This technology can be used as a biometric feature for verifying the identity of a person...

  11. Invention and validation of an automated camera system that uses optical character recognition to identify patient name mislabeled samples.

    Science.gov (United States)

    Hawker, Charles D; McCarthy, William; Cleveland, David; Messinger, Bonnie L

    2014-03-01

    Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.

  12. Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

    Science.gov (United States)

    Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

    2018-04-01

    This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

  13. Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics

    Science.gov (United States)

    Tibaduiza-Burgos, Diego Alexander; Torres-Arredondo, Miguel Angel

    2015-08-01

    Aeronautical structures are subjected to damage during their service raising the necessity for periodic inspection and maintenance of their components so that structural integrity and safe operation can be guaranteed. Cost reduction related to minimizing the out-of-service time of the aircraft, together with the advantages offered by real-time and safe-life service monitoring, have led to a boom in the design of inexpensive and structurally integrated transducer networks comprising actuators, sensors, signal processing units and controllers. These kinds of automated systems are normally referred to as smart structures and offer a multitude of new solutions to engineering problems and multi-functional capabilities. It is thus expected that structural health monitoring (SHM) systems will become one of the leading technologies for assessing and assuring the structural integrity of future aircraft. This study is devoted to the development and experimental investigation of an SHM methodology for the detection of damage in real scale complex aeronautical structures. The work focuses on each aspect of the SHM system and highlights the potentialities of the health monitoring technique based on acousto-ultrasonics and data-driven modelling within the concepts of sensor data fusion, feature extraction and pattern recognition. The methodology is experimentally demonstrated on an aircraft skin panel and fuselage panel for which several damage scenarios are analysed. The detection performance in both structures is quantified and presented.

  14. Involvement of the oxytocin system in the bed nucleus of the stria terminalis in the sex-specific regulation of social recognition.

    Science.gov (United States)

    Dumais, Kelly M; Alonso, Andrea G; Immormino, Marisa A; Bredewold, Remco; Veenema, Alexa H

    2016-02-01

    Sex differences in the oxytocin (OT) system in the brain may explain why OT often regulates social behaviors in sex-specific ways. However, a link between sex differences in the OT system and sex-specific regulation of social behavior has not been tested. Here, we determined whether sex differences in the OT receptor (OTR) or in OT release in the posterior bed nucleus of the stria terminalis (pBNST) mediates sex-specific regulation of social recognition in rats. We recently showed that, compared to female rats, male rats have a three-fold higher OTR binding density in the pBNST, a sexually dimorphic area implicated in the regulation of social behaviors. We now demonstrate that OTR antagonist (5 ng/0.5 μl/side) administration into the pBNST impairs social recognition in both sexes, while OT (100 pg/0.5 μl/side) administration into the pBNST prolongs the duration of social recognition in males only. These effects seem specific to social recognition, as neither treatment altered total social investigation time in either sex. Moreover, baseline OT release in the pBNST, as measured with in vivo microdialysis, did not differ between the sexes. However, males showed higher OT release in the pBNST during social recognition compared to females. These findings suggest a sex-specific role of the OT system in the pBNST in the regulation of social recognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Real-Time Speech Recognition System for Robotic Control Applications Using an Ear-Microphone

    National Research Council Canada - National Science Library

    Koliousis, Dimitrios S

    2007-01-01

    ...) system designed in an earlier study. The recognizer uses a short-term energy and zero-crossing based detection scheme, and a discrete Hidden Markov model recognizer designed to recognize seven isolated words...

  16. Multiscale aspects of the visual system and their use for scale invariant object recognition

    NARCIS (Netherlands)

    Petkov, N; vanDeemter, J; Karsch, F; Monien, B; Satz, H

    1997-01-01

    Psychophysical, neuroanatomical and neurophysiological evidence for multiscale aspects of the visual system is considered. The stack model and its relation to the image pyramid are discussed. The results of a straightforward implementation on a parallel supercomputer are presented. The high

  17. The immune self: a selectionist theory of recognition, learning, and remembering within the immune system.

    Science.gov (United States)

    Kradin, R L

    1995-01-01

    In this paper, I have briefly explored metaphors shared by the immune and nervous systems and shown that this exercise can lead to the elucidation of common principles of organization, as well as to predictions concerning how the immune system functions. Metaphor itself undoubtedly reflects the way in which we categorize and retrieve information 44], so it is not surprising that the deep processes of language tend to sample information from related data categories. Although the nervous and immune systems are obviously not the same and metaphors are indeed just that, my primary goal has been to suggest that by virtue of their having evolved in parallel over millions of years, the nervous and immune systems currently use the same archetypal principles and strategies to address related challenges in information processing and retrieval. Ultimately, nature is conservative. One need only look at a tree, a river, the airways, or the vascular bed in order to see how a fractal pattern of repetitive dichotomous branching has been used by each, in order to optimize the transport of fluids over large distances [45]. While each system has had to adopt different materials in order to solve the problem, the shape of their solutions is remarkably alike. In the immune and nervous systems, the elements used to produce optimal functional responses are also quite different, but again the solutions have been achieved by comparable strategies. I am certain that these two great systems of information processing, each responding with vastly different kinetics, will prove to be far more integrally interdependent than has been previously recognized. For example, should a swift response by the immune system be required in an overwhelming invasion by microbial pathogens, the immune system may be able to cooperate with the rapidly reacting nervous system to rid the host of the invaders. In this regard, we have shown that the beta-adrenergic hormone epinephrine rapidly increases the traffic of

  18. A preliminary analysis of human factors affecting the recognition accuracy of a discrete word recognizer for C3 systems

    Science.gov (United States)

    Yellen, H. W.

    1983-03-01

    Literature pertaining to Voice Recognition abounds with information relevant to the assessment of transitory speech recognition devices. In the past, engineering requirements have dictated the path this technology followed. But, other factors do exist that influence recognition accuracy. This thesis explores the impact of Human Factors on the successful recognition of speech, principally addressing the differences or variability among users. A Threshold Technology T-600 was used for a 100 utterance vocubalary to test 44 subjects. A statistical analysis was conducted on 5 generic categories of Human Factors: Occupational, Operational, Psychological, Physiological and Personal. How the equipment is trained and the experience level of the speaker were found to be key characteristics influencing recognition accuracy. To a lesser extent computer experience, time or week, accent, vital capacity and rate of air flow, speaker cooperativeness and anxiety were found to affect overall error rates.

  19. Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows

    DEFF Research Database (Denmark)

    Zapata, Julián; Kirkedal, Andreas Søeborg

    2015-01-01

    In this paper, we report on a two-part experiment aiming to assess and compare the performance of two types of automatic speech recognition (ASR) systems on two different computational platforms when used to augment dictation workflows. The experiment was performed with a sample of speakers...

  20. Nestmate recognition system in a lower attini ant: Mycocepurus smithii | Sistema de reconocimiento de compañeras de nido en la hormiga attini inferior: Mycocepurus smithii

    Directory of Open Access Journals (Sweden)

    Cristina Sainz-Borgo

    2017-11-01

    Full Text Available The aim of this study was to determine how the nestmate recognition system operates in Mycocepurus smithii. Behavioural bio-assays were performed in order to identify the nestmate recognition signal. In the field, bio-assays consisted of placing an alien and a nestmate in the nest entrance. In the laboratory, the bio-assays were made using three nestmates and three aliens placed in a Petri dish. The treatments evaluated were: determination of the effect of geographical distance, the volatility of the signal, the source of nestmate recognition signal and the effect of the Post-Pharyngeal Gland (PPG and Mandibular Gland (MG. The PPG hydrocarbons and MG volatiles were analyzed using the Solid Phase Microextraction technique (SPME. The relative intercolonial variations of both glands were studied. In the behavioral bio-assays, agonistic interactions were not detected either in the field or the laboratory. In the field, statistical differences were found in the distance aliens were carried away from the nest entrance. Intercolonial differences were found for MG volatile compounds, and for such compunds between ants immediately collected or isolated for 48 hours. In PPG hydrocarbons no intercolonial differences were found. In conclusion, workers of M. smithii have the skill to discriminate between aliens and nestmates. This is the first study on the behavior of recognition in this species, however, more detailed studies are necessary to a better description of the signals that allow such recognition.

  1. Dielectric and ferroelectric sensing based on molecular recognition in Cu(1,10-phenlothroline)2SeO4.(diol) systems

    Science.gov (United States)

    Ye, Heng-Yun; Liao, Wei-Qiang; Zhou, Qionghua; Zhang, Yi; Wang, Jinlan; You, Yu-Meng; Wang, Jin-Yun; Chen, Zhong-Ning; Li, Peng-Fei; Fu, Da-Wei; Huang, Songping D.; Xiong, Ren-Gen

    2017-02-01

    The process of molecular recognition is the assembly of two or more molecules through weak interactions. Information in the process of molecular recognition can be transmitted to us via physical signals, which may find applications in sensing and switching. The conventional signals are mainly limited to light signal. Here, we describe the recognition of diols with Cu(1,10-phenlothroline)2SeO4 and the transduction of discrete recognition events into dielectric and/or ferroelectric signals. We observe that systems of Cu(1,10-phenlothroline)2SeO4.(diol) exhibit significant dielectric and/or ferroelectric dependence on different diol molecules. The compounds including ethane-1,2-diol or propane-1,2-diol just show small temperature-dependent dielectric anomalies and no reversible polarization, while the compound including ethane-1,3-diol shows giant temperature-dependent dielectric anomalies as well as ferroelectric reversible spontaneous polarization. This finding shows that dielectricity and/or ferroelectricity has the potential to be used for signalling molecular recognition.

  2. Markov Models for Handwriting Recognition

    CERN Document Server

    Plotz, Thomas

    2011-01-01

    Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden

  3. Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

    Science.gov (United States)

    Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

    2013-11-01

    Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory. Copyright © 2013 Elsevier Inc. All rights

  4. Suspicious Behavior Detection System for an Open Space Parking Based on Recognition of Human Elemental Actions

    Science.gov (United States)

    Inomata, Teppei; Kimura, Kouji; Hagiwara, Masafumi

    Studies for video surveillance applications for preventing various crimes such as stealing and violence have become a hot topic. This paper proposes a new video surveillance system that can detect suspicious behaviors such as a car break-in and vandalization in an open space parking, and that is based on image processing. The proposed system has the following features: it 1)deals time series data flow, 2)recognizes “human elemental actions” using statistic features, and 3)detects suspicious behavior using Subspace method and AdaBoost. We conducted the experiments to test the performance of the proposed system using open space parking scenes. As a result, we obtained about 10.0% for false positive rate, and about 4.6% for false negative rate.

  5. A Real-Time Angle- and Illumination-Aware Face Recognition System Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hisateru Kato

    2012-01-01

    Full Text Available Automatic authentication systems, using biometric technology, are becoming increasingly important with the increased need for person verification in our daily life. A few years back, fingerprint verification was done only in criminal investigations. Now fingerprints and face images are widely used in bank tellers, airports, and building entrances. Face images are easy to obtain, but successful recognition depends on proper orientation and illumination of the image, compared to the one taken at registration time. Facial features heavily change with illumination and orientation angle, leading to increased false rejection as well as false acceptance. Registering face images for all possible angles and illumination is impossible. In this work, we proposed a memory efficient way to register (store multiple angle and changing illumination face image data, and a computationally efficient authentication technique, using multilayer perceptron (MLP. Though MLP is trained using a few registered images with different orientation, due to generalization property of MLP, interpolation of features for intermediate orientation angles was possible. The algorithm is further extended to include illumination robust authentication system. Results of extensive experiments verify the effectiveness of the proposed algorithm.

  6. Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images.

    Science.gov (United States)

    Kwon, Seung Yong; Pham, Tuyen Danh; Park, Kang Ryoung; Jeong, Dae Sik; Yoon, Sungsoo

    2016-06-11

    Fitness classification is a technique to assess the quality of banknotes in order to determine whether they are usable. Banknote classification techniques are useful in preventing problems that arise from the circulation of substandard banknotes (such as recognition failures, or bill jams in automated teller machines (ATMs) or bank counting machines). By and large, fitness classification continues to be carried out by humans, and this can cause the problem of varying fitness classifications for the same bill by different evaluators, and requires a lot of time. To address these problems, this study proposes a fuzzy system-based method that can reduce the processing time needed for fitness classification, and can determine the fitness of banknotes through an objective, systematic method rather than subjective judgment. Our algorithm was an implementation to actual banknote counting machine. Based on the results of tests on 3856 banknotes in United States currency (USD), 3956 in Korean currency (KRW), and 2300 banknotes in Indian currency (INR) using visible light reflection (VR) and near-infrared light transmission (NIRT) imaging, the proposed method was found to yield higher accuracy than prevalent banknote fitness classification methods. Moreover, it was confirmed that the proposed algorithm can operate in real time, not only in a normal PC environment, but also in an embedded system environment of a banknote counting machine.

  7. Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images

    Directory of Open Access Journals (Sweden)

    Seung Yong Kwon

    2016-06-01

    Full Text Available Fitness classification is a technique to assess the quality of banknotes in order to determine whether they are usable. Banknote classification techniques are useful in preventing problems that arise from the circulation of substandard banknotes (such as recognition failures, or bill jams in automated teller machines (ATMs or bank counting machines. By and large, fitness classification continues to be carried out by humans, and this can cause the problem of varying fitness classifications for the same bill by different evaluators, and requires a lot of time. To address these problems, this study proposes a fuzzy system-based method that can reduce the processing time needed for fitness classification, and can determine the fitness of banknotes through an objective, systematic method rather than subjective judgment. Our algorithm was an implementation to actual banknote counting machine. Based on the results of tests on 3856 banknotes in United States currency (USD, 3956 in Korean currency (KRW, and 2300 banknotes in Indian currency (INR using visible light reflection (VR and near-infrared light transmission (NIRT imaging, the proposed method was found to yield higher accuracy than prevalent banknote fitness classification methods. Moreover, it was confirmed that the proposed algorithm can operate in real time, not only in a normal PC environment, but also in an embedded system environment of a banknote counting machine.

  8. Employing Textual and Facial Emotion Recognition to Design an Affective Tutoring System

    Science.gov (United States)

    Lin, Hao-Chiang Koong; Wang, Cheng-Hung; Chao, Ching-Ju; Chien, Ming-Kuan

    2012-01-01

    Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners'…

  9. Photoresponsive Molecular Recognition and Adhesion of Vesicles in a Competitive Ternary Supramolecular System

    NARCIS (Netherlands)

    Nalluri, Siva Krishna Mohan; Bultema, Jelle B.; Boekema, Egbert J.; Ravoo, Bart Jan

    A competitive photoresponsive supramolecular system is formed in a dilute aqueous solution of three components: vesicles of amphiphilic alpha-cyclodextrin host 1a, divalent p-methylphenyl guest 2 or divalent p-methylbenzamide guest 3, and photoresponsive azobenzene monovalent guest 5. Guests 2 and 3

  10. Pattern recognition and expert image analysis systems in biomedical image processing (Invited Paper)

    Science.gov (United States)

    Oosterlinck, A.; Suetens, P.; Wu, Q.; Baird, M.; F. M., C.

    1987-09-01

    This paper gives an overview of pattern recoanition techniques (P.R.) used in biomedical image processing and problems related to the different P.R. solutions. Also the use of knowledge based systems to overcome P.R. difficulties, is described. This is illustrated by a common example ofabiomedical image processing application.

  11. Design of real-time communication system for image recognition based colony picking instrument

    Science.gov (United States)

    Wang, Qun; Zhang, Rongfu; Yan, Hua; Wu, Huamin

    2017-11-01

    In order to aachieve autommated observatiion and pickinng of monocloonal colonies, an overall dessign and realizzation of real-time commmunication system based on High-throoughput monooclonal auto-piicking instrumment is propossed. The real-time commmunication system is commposed of PCC-PLC commuunication systtem and Centrral Control CComputer (CCC)-PLC communicatioon system. Bassed on RS232 synchronous serial communnication methood to develop a set of dedicated shoort-range commmunication prootocol betweenn the PC and PPLC. Furthermmore, the systemm uses SQL SSERVER database to rrealize the dataa interaction between PC andd CCC. Moreoover, the commmunication of CCC and PC, adopted Socket Ethernnet communicaation based on TCP/IP protoccol. TCP full-dduplex data cannnel to ensure real-time data eexchange as well as immprove system reliability andd security. We tested the commmunication syystem using sppecially develooped test software, thee test results show that the sysstem can realizze the communnication in an eefficient, safe aand stable way between PLC, PC andd CCC, keep thhe real-time conntrol to PLC annd colony inforrmation collecttion.

  12. An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

    Science.gov (United States)

    Khatun, Nazma; Miwa, Jouji

    2016-01-01

    This research project was aimed to develop an intelligent Bengali handwriting education system to improve the literacy level in Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. Here, we developed a prototype of web-based (iPhone/smartphone or computer browser) intelligent…

  13. Towards future interactive intelligent systems for animals : Study and recognition of embodied interactions

    NARCIS (Netherlands)

    Pons, Patricia; Jaen, Javier; Catala, Alejandro

    2017-01-01

    User-centered design applied to non-human animals is showing to be a promising research line known as Animal Computer Interaction (ACI), aimed at improving animals' wellbeing using technology. Within this research line, intelligent systems for animal entertainment could have remarkable benefits for

  14. Towards Robust Visual Speech Recognition : Automatic Systems for Lip Reading of Dutch

    NARCIS (Netherlands)

    Chitu, A.G.

    2010-01-01

    In the last two decades we witnessed a rapid increase of the computational power governed by Moore's Law. As a side effect, the affordability of cheaper and faster CPUs increased as well. Therefore, many new “smart” devices flooded the market and made informational systems widely spread. The number

  15. Efficient CEPSTRAL Normalization for Robust Speech Recognition

    National Research Council Canada - National Science Library

    Liu, Fu-Hua; Stern, Richard M; Huang, Xuedong; Acero, Alejandro

    1993-01-01

    In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy...

  16. Online handwritten mathematical expression recognition

    Science.gov (United States)

    Büyükbayrak, Hakan; Yanikoglu, Berrin; Erçil, Aytül

    2007-01-01

    We describe a system for recognizing online, handwritten mathematical expressions. The system is designed with a user-interface for writing scientific articles, supporting the recognition of basic mathematical expressions as well as integrals, summations, matrices etc. A feed-forward neural network recognizes symbols which are assumed to be single-stroke and a recursive algorithm parses the expression by combining neural network output and the structure of the expression. Preliminary results show that writer-dependent recognition rates are very high (99.8%) while writer-independent symbol recognition rates are lower (75%). The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC API for recognizing textual input and supports conversion of hand-drawn figures into PNG format. This enables the user to enter text, mathematics and draw figures in a single interface. After recognition, all output is combined into one LATEX code and compiled into a PDF file.

  17. The development of an automatic recognition system for earmark and earprint comparisons.

    Science.gov (United States)

    Junod, Stéphane; Pasquier, Julien; Champod, Christophe

    2012-10-10

    The value of earmarks as an efficient means of personal identification is still subject to debate. It has been argued that the field is lacking a firm systematic and structured data basis to help practitioners to form their conclusions. Typically, there is a paucity of research guiding as to the selectivity of the features used in the comparison process between an earmark and reference earprints taken from an individual. This study proposes a system for the automatic comparison of earprints and earmarks, operating without any manual extraction of key-points or manual annotations. For each donor, a model is created using multiple reference prints, hence capturing the donor within source variability. For each comparison between a mark and a model, images are automatically aligned and a proximity score, based on a normalized 2D correlation coefficient, is calculated. Appropriate use of this score allows deriving a likelihood ratio that can be explored under known state of affairs (both in cases where it is known that the mark has been left by the donor that gave the model and conversely in cases when it is established that the mark originates from a different source). To assess the system performance, a first dataset containing 1229 donors elaborated during the FearID research project was used. Based on these data, for mark-to-print comparisons, the system performed with an equal error rate (EER) of 2.3% and about 88% of marks are found in the first 3 positions of a hitlist. When performing print-to-print transactions, results show an equal error rate of 0.5%. The system was then tested using real-case data obtained from police forces. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  18. Automated recognition of helium speech. Phase I: Investigation of microprocessor based analysis/synthesis system

    Science.gov (United States)

    Jelinek, H. J.

    1986-01-01

    This is the Final Report of Electronic Design Associates on its Phase I SBIR project. The purpose of this project is to develop a method for correcting helium speech, as experienced in diver-surface communication. The goal of the Phase I study was to design, prototype, and evaluate a real time helium speech corrector system based upon digital signal processing techniques. The general approach was to develop hardware (an IBM PC board) to digitize helium speech and software (a LAMBDA computer based simulation) to translate the speech. As planned in the study proposal, this initial prototype may now be used to assess expected performance from a self contained real time system which uses an identical algorithm. The Final Report details the work carried out to produce the prototype system. Four major project tasks were: a signal processing scheme for converting helium speech to normal sounding speech was generated. The signal processing scheme was simulated on a general purpose (LAMDA) computer. Actual helium speech was supplied to the simulation and the converted speech was generated. An IBM-PC based 14 bit data Input/Output board was designed and built. A bibliography of references on speech processing was generated.

  19. Data structures, computer graphics, and pattern recognition

    CERN Document Server

    Klinger, A; Kunii, T L

    1977-01-01

    Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr

  20. Speaker diarization system on the 2007 NIST rich transcription meeting recognition evaluation

    Science.gov (United States)

    Sun, Hanwu; Nwe, Tin Lay; Koh, Eugene Chin Wei; Bin, Ma; Li, Haizhou

    2007-09-01

    This paper presents a speaker diarization system developed at the Institute for Infocomm Research (I2R) for NIST Rich Transcription 2007 (RT-07) evaluation task. We describe in details our primary approaches for the speaker diarization on the Multiple Distant Microphones (MDM) conditions in conference room scenario. Our proposed system consists of six modules: 1). Least-mean squared (NLMS) adaptive filter for the speaker direction estimate via Time Difference of Arrival (TDOA), 2). An initial speaker clustering via two-stage TDOA histogram distribution quantization approach, 3). Multiple microphone speaker data alignment via GCC-PHAT Time Delay Estimate (TDE) among all the distant microphone channel signals, 4). A speaker clustering algorithm based on GMM modeling approach, 5). Non-speech removal via speech/non-speech verification mechanism and, 6). Silence removal via "Double-Layer Windowing"(DLW) method. We achieves error rate of 31.02% on the 2006 Spring (RT-06s) MDM evaluation task and a competitive overall error rate of 15.32% for the NIST Rich Transcription 2007 (RT-07) MDM evaluation task.

  1. Autism, emotion recognition and the mirror neuron system: the case of music.

    Science.gov (United States)

    Molnar-Szakacs, Istvan; Wang, Martha J; Laugeson, Elizabeth A; Overy, Katie; Wu, Wai-Ling; Piggot, Judith

    2009-11-16

    Understanding emotions is fundamental to our ability to navigate and thrive in a complex world of human social interaction. Individuals with Autism Spectrum Disorders (ASD) are known to experience difficulties with the communication and understanding of emotion, such as the nonverbal expression of emotion and the interpretation of emotions of others from facial expressions and body language. These deficits often lead to loneliness and isolation from peers, and social withdrawal from the environment in general. In the case of music however, there is evidence to suggest that individuals with ASD do not have difficulties recognizing simple emotions. In addition, individuals with ASD have been found to show normal and even superior abilities with specific aspects of music processing, and often show strong preferences towards music. It is possible these varying abilities with different types of expressive communication may be related to a neural system referred to as the mirror neuron system (MNS), which has been proposed as deficient in individuals with autism. Music's power to stimulate emotions and intensify our social experiences might activate the MNS in individuals with ASD, and thus provide a neural foundation for music as an effective therapeutic tool. In this review, we present literature on the ontogeny of emotion processing in typical development and in individuals with ASD, with a focus on the case of music.

  2. Multi Modality Brain Mapping System (MBMS) Using Artificial Intelligence and Pattern Recognition

    Science.gov (United States)

    Kateb, Babak (Inventor); Nikzad, Shouleh (Inventor)

    2017-01-01

    A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image.

  3. Complexity and diversity of the NKR-P1:Clr (Klrb1:Clec2 recognition systems

    Directory of Open Access Journals (Sweden)

    Christina L Kirkham

    2014-06-01

    Full Text Available The NKR-P1 receptors were identified as prototypical natural killer (NK cell surface antigens and later shown to be conserved from rodents to humans on NK cells and subsets of T cells. C-type lectin-like in nature, they were originally shown to be capable of activating NK cell function and to recognize ligands on tumour cells. However, certain family members have subsequently been shown to be capable of inhibiting NK cell activity, and to recognize proteins encoded by a family of genetically linked C-type lectin-related (Clr ligands. Some of these ligands are expressed by normal, healthy cells, and modulated during transformation, infection, and cellular stress, while other ligands are upregulated during the immune response and during pathological circumstances. Here, we discuss historical and recent developments in NKR-P1 biology that demonstrate this NK receptor-ligand system to be far more complex and diverse than originally anticipated.

  4. Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets.

    Science.gov (United States)

    Soria Morillo, Luis M; Alvarez-Garcia, Juan A; Gonzalez-Abril, Luis; Ortega Ramírez, Juan A

    2016-07-15

    In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

  5. A Transient Fault Recognition Method for an AC-DC Hybrid Transmission System Based on MMC Information Fusion

    Directory of Open Access Journals (Sweden)

    Jikai Chen

    2016-12-01

    Full Text Available At present, the research is still in the primary stage in the process of fault disturbance energy transfer in the multilevel modular converter based high voltage direct current (HVDC-MMC. An urgent problem is how to extract and analyze the fault features hidden in MMC electrical information in further studies on the HVDC system. Aiming at the above, this article analyzes the influence of AC transient disturbance on electrical signals of MMC. At the same time, it is found that the energy distribution of electrical signals in MMC is different for different arms in the same frequency bands after the discrete wavelet packet transformation (DWPT. Renyi wavelet packet energy entropy (RWPEE and Renyi wavelet packet time entropy (RWPTE are proposed and applied to AC transient fault feature extraction from electrical signals in MMC. Using the feature extraction results of Renyi wavelet packet entropy (RWPE, a novel recognition method is put forward to recognize AC transient faults using the information fusion technology. Theoretical analysis and experimental results show that the proposed method is available to recognize transient AC faults.

  6. Helicase-Dependent Isothermal Amplification of DNA and RNA by Using Self-Avoiding Molecular Recognition Systems.

    Science.gov (United States)

    Yang, Zunyi; McLendon, Chris; Hutter, Daniel; Bradley, Kevin M; Hoshika, Shuichi; Frye, Carole B; Benner, Steven A

    2015-06-15

    Assays that detect DNA or RNA (xNA) are highly sensitive, as small amounts of xNA can be amplified by PCR. Unfortunately, PCR is inconvenient in low-resource environments, and requires equipment and power that might not be available in these environments. Isothermal procedures, which avoid thermal cycling, are often confounded by primer dimers, off-target priming, and other artifacts. Here, we show how a "self avoiding molecular recognition system" (SAMRS) eliminates these artifacts and gives clean amplicons in a helicase-dependent isothermal amplification (SAMRS-HDA). We also show that incorporating SAMRS into the 3'-ends of primers facilitates the design and screening of primers for HDA assays. Finally, we show that SAMRS-HDA can be twofold multiplexed, difficult to achieve with HDA using standard primers. Thus, SAMRS-HDA is a more versatile approach than standard HDA, with a broader applicability for xNA-targeted diagnostics and research. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Unmanned Aerial Vehicle-Based Automobile License Plate Recognition System for Institutional Parking Lots

    Directory of Open Access Journals (Sweden)

    Julian Dasilva

    2017-10-01

    Full Text Available Unmanned aerial vehicles (UAVs, also known as drones have many applications and they are a current trend across many industries. They can be used for delivery, sports, surveillance, professional photography, cinematography, military combat, natural disaster assistance, security, and the list grows every day. Programming opens an avenue to automate many processes of daily life and with the drone as aerial programmable eyes, security and surveillance can become more efficient and cost effective. At Barry University, parking is becoming an issue as the number of people visiting the school greatly outnumbers the convenient parking locations. This has caused a multitude of hazards in parking lots due to people illegally parking, as well as unregistered vehicles parking in reserved areas. In this paper, we explain how automated drone surveillance is utilized to detect unauthorized parking at Barry University. The automated process is incorporated into Java application and completed in three steps: collecting visual data, processing data automatically, and sending automated responses and queues to the operator of the system.

  8. Excimer Laser Surgery: Biometrical Iris Eye Recognition with Cyclorotational Control Eye Tracker System.

    Science.gov (United States)

    Pajic, Bojan; Cvejic, Zeljka; Mijatovic, Zoran; Indjin, Dragan; Mueller, Joerg

    2017-05-25

    A prospective comparative study assessing the importance of the intra-operative dynamic rotational tracking-especially in the treatment of astigmatisms in corneal refractive Excimer laser correction-concerning clinical outcomes is presented. The cyclotorsion from upright to supine position was measured using iris image comparison. The Group 1 of patients was additionally treated with cyclorotational control and Group 2 only with X-Y control. Significant differences were observed between the groups regarding the mean postoperative cylinder refraction ( p < 0.05). The mean cyclotorsion can be calculated to 3.75° with a standard deviation of 3.1°. The total range of torsion was from -14.9° to +12.6°. Re-treatment rate was 2.2% in Group 1 and 8.2% in Group 2, which is highly significant ( p < 0.01). The investigation confirms that the dynamic rotational tracking system used for LASIK results in highly predictable refraction quality with significantly less postoperative re-treatments.

  9. Speech activity detection for the automated speaker recognition system of critical use

    Directory of Open Access Journals (Sweden)

    M. M. Bykov

    2017-06-01

    Full Text Available In the article, the authors developed a method for detecting speech activity for an automated system for recognizing critical use of speeches with wavelet parameterization of speech signal and classification at intervals of “language”/“pause” using a curvilinear neural network. The method of wavelet-parametrization proposed by the authors allows choosing the optimal parameters of wavelet transformation in accordance with the user-specified error of presentation of speech signal. Also, the method allows estimating the loss of information depending on the selected parameters of continuous wavelet transformation (NPP, which allowed to reduce the number of scalable coefficients of the LVP of the speech signal in order of magnitude with the allowable degree of distortion of the local spectrum of the LVP. An algorithm for detecting speech activity with a curvilinear neural network classifier is also proposed, which shows the high quality of segmentation of speech signals at intervals "language" / "pause" and is resistant to the presence in the speech signal of narrowband noise and technogenic noise due to the inherent properties of the curvilinear neural network.

  10. Massive parallel optical pattern recognition and retrieval via a two-stage high-capacity multichannel holographic random access memory system

    International Nuclear Information System (INIS)

    Cai, Luzhong; Liu, Hua-Kuang

    2000-01-01

    The multistage holographic optical random access memory (HORAM) system reported recently by Liu et al. provides a new degree of freedom for improving storage capacity. We further present a theoretical and practical analysis of the HORAM system with experimental results. Our discussions include the system design and geometrical requirements, its applications for multichannel pattern recognition and associative memory, the 2-D and 3-D information storage capacity, and multichannel image storage and retrieval via VanderLugt correlator (VLC) filters and joint transform holograms. A series of experiments are performed to demonstrate the feasibility of the multichannel pattern recognition and image retrieval with both the VLC and joint transform correlator (JTC) architectures. The experimental results with as many as 2025 channels show good agreement with the theoretical analysis. (c) 2000 Society of Photo-Optical Instrumentation Engineers

  11. Probabilistic Open Set Recognition

    Science.gov (United States)

    Jain, Lalit Prithviraj

    Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

  12. Case-Based Policy and Goal Recognition

    Science.gov (United States)

    2015-09-30

    Policy and Goal Recognizer (PaGR), a case- based system for multiagent keyhole recognition. PaGR is a knowledge recognition component within a decision...However, unlike our agent in the BVR domain, these recognition agents have access to perfect information. Single-agent keyhole plan recognition can be...listed below: 1. Facing Target 2. Closing on Target 3. Target Range 4. Within a Target’s Weapon Range 5. Has Target within Weapon Range 6. Is in Danger

  13. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  14. A model for abnormal activity recognition and alert generation system for elderly care by hidden conditional random fields using R-transform and generalized discriminant analysis features.

    Science.gov (United States)

    Khan, Zafar Ali; Sohn, Won

    2012-10-01

    The growing population of elderly people living alone increases the need for automatic healthcare monitoring systems for elderly care. Automatic vision sensor-based systems are increasingly used for human activity recognition (HAR) in recent years. This study presents an improved model, tested using actors, of a sensor-based HAR system to recognize daily life activities of elderly people at home and generate an alert in case of abnormal HAR. Datasets consisting of six abnormal activities (falling backward, falling forward, falling rightward, falling leftward, chest pain, and fainting) and four normal activities (walking, rushing, sitting down, and standing up) are generated from different view angles (90°, -90°, 45°, -45°). Feature extraction and dimensions reduction are performed by R-transform followed by generalized discriminant analysis (GDA) methods. R-transform extracts symmetric, scale, and translation-invariant features from the sequences of activities. GDA increases the discrimination between different classes of highly similar activities. Silhouette sequences are quantified by the Linde-Buzo-Gray algorithm and recognized by hidden conditional random fields. Experimental results provide an average recognition rate of 94.2% for abnormal activities and 92.7% for normal activities. The recognition rate for the highly similar activities from different view angles shows the flexibility and efficacy of the proposed abnormal HAR and alert generation system for elderly care.

  15. Indoor navigation by image recognition

    Science.gov (United States)

    Choi, Io Teng; Leong, Chi Chong; Hong, Ka Wo; Pun, Chi-Man

    2017-07-01

    With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user's location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

  16. Performance Comparison of Several Pre-Processing Methods in a Hand Gesture Recognition System based on Nearest Neighbor for Different Background Conditions

    Directory of Open Access Journals (Sweden)

    Regina Lionnie

    2013-09-01

    Full Text Available This paper presents a performance analysis and comparison of several pre-processing  methods  used  in  a  hand  gesture  recognition  system.  The  preprocessing methods are based on the combinations ofseveral image processing operations,  namely  edge  detection,  low  pass  filtering,  histogram  equalization, thresholding and desaturation. The hand gesture recognition system is designed to classify an input image into one of six possibleclasses. The input images are taken with various background conditions. Our experiments showed that the best result is achieved when the pre-processing method consists of only a desaturation operation, achieving a classification accuracy of up to 83.15%.

  17. Radiation Dose-Rate Extraction from the Camera Image of Quince 2 Robot System using Optical Character Recognition

    International Nuclear Information System (INIS)

    Cho, Jai Wan; Jeong, Kyung Min

    2012-01-01

    In the case of the Japanese Quince 2 robot system, 7 CCD/CMOS cameras were used. 2 CCD cameras of Quince robot are used for the forward and backward monitoring of the surroundings during navigation. And 2 CCD (or CMOS) cameras are used for monitoring the status of front-end and back-end motion mechanics such as flippers and crawlers. A CCD camera with wide field of view optics is used for monitoring the status of the communication (VDSL) cable reel. And another 2 CCD cameras are assigned for reading the indication value of the radiation dosimeter and the instrument. The Quince 2 robot measured radiation in the unit 2 reactor building refueling floor of the Fukushima nuclear power plant. The CCD camera with wide field-of-view (fisheye) lens reads indicator of the dosimeter loaded on the Quince 2 robot, which was sent to carry out investigating the unit 2 reactor building refueling floor situation. The camera image with gamma ray dose-rate information is transmitted to the remote control site via VDSL communication line. At the remote control site, the radiation information in the unit 2 reactor building refueling floor can be perceived by monitoring the camera image. To make up the radiation profile in the surveyed refueling floor, the gamma ray dose-rate information in the image should be converted to numerical value. In this paper, we extract the gamma ray dose-rate value in the unit 2 reactor building refueling floor using optical character recognition method

  18. Radiation Dose-Rate Extraction from the Camera Image of Quince 2 Robot System using Optical Character Recognition

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jai Wan; Jeong, Kyung Min [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2012-05-15

    In the case of the Japanese Quince 2 robot system, 7 CCD/CMOS cameras were used. 2 CCD cameras of Quince robot are used for the forward and backward monitoring of the surroundings during navigation. And 2 CCD (or CMOS) cameras are used for monitoring the status of front-end and back-end motion mechanics such as flippers and crawlers. A CCD camera with wide field of view optics is used for monitoring the status of the communication (VDSL) cable reel. And another 2 CCD cameras are assigned for reading the indication value of the radiation dosimeter and the instrument. The Quince 2 robot measured radiation in the unit 2 reactor building refueling floor of the Fukushima nuclear power plant. The CCD camera with wide field-of-view (fisheye) lens reads indicator of the dosimeter loaded on the Quince 2 robot, which was sent to carry out investigating the unit 2 reactor building refueling floor situation. The camera image with gamma ray dose-rate information is transmitted to the remote control site via VDSL communication line. At the remote control site, the radiation information in the unit 2 reactor building refueling floor can be perceived by monitoring the camera image. To make up the radiation profile in the surveyed refueling floor, the gamma ray dose-rate information in the image should be converted to numerical value. In this paper, we extract the gamma ray dose-rate value in the unit 2 reactor building refueling floor using optical character recognition method

  19. News Schemes for Activity Recognition Systems Using PCA-WSVM, ICA-WSVM, and LDA-WSVM

    Directory of Open Access Journals (Sweden)

    M’hamed Bilal Abidine

    2015-08-01

    Full Text Available Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA, Independent Component Analysis (ICA, and Linear Discriminant Analysis (LDA. The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM classifier. This classifier is used to handle the problem of imbalanced activity data from the sensor readings. The experiments were implemented on multiple real-world datasets with Conditional Random Fields (CRF, standard Support Vector Machines (SVM, Weighted SVM, and combined methods PCA+WSVM, ICA+WSVM, and LDA+WSVM showed that LDA+WSVM had a higher recognition rate than other methods for activity recognition.

  20. Risperidone reverses the spatial object recognition impairment and hippocampal BDNF-TrkB signalling system alterations induced by acute MK-801 treatment

    Science.gov (United States)

    Chen, Guangdong; Lin, Xiaodong; Li, Gongying; Jiang, Diego; Lib, Zhiruo; Jiang, Ronghuan; Zhuo, Chuanjun

    2017-01-01

    The aim of the present study was to investigate the effects of a commonly-used atypical antipsychotic, risperidone, on alterations in spatial learning and in the hippocampal brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signalling system caused by acute dizocilpine maleate (MK-801) treatment. In experiment 1, adult male Sprague-Dawley rats subjected to acute treatment of either low-dose MK801 (0.1 mg/kg) or normal saline (vehicle) were tested for spatial object recognition and hippocampal expression levels of BDNF, TrkB and the phophorylation of TrkB (p-TrkB). We found that compared to the vehicle, MK-801 treatment impaired spatial object recognition of animals and downregulated the expression levels of p-TrkB. In experiment 2, MK-801- or vehicle-treated animals were further injected with risperidone (0.1 mg/kg) or vehicle before behavioural testing and sacrifice. Of note, we found that risperidone successfully reversed the deleterious effects of MK-801 on spatial object recognition and upregulated the hippocampal BDNF-TrkB signalling system. Collectively, the findings suggest that cognitive deficits from acute N-methyl-D-aspartate receptor blockade may be associated with the hypofunction of hippocampal BDNF-TrkB signalling system and that risperidone was able to reverse these alterations. PMID:28451387

  1. The influence of age, hearing, and working memory on the speech comprehension benefit derived from an automatic speech recognition system.

    Science.gov (United States)

    Zekveld, Adriana A; Kramer, Sophia E; Kessens, Judith M; Vlaming, Marcel S M G; Houtgast, Tammo

    2009-04-01

    The aim of the current study was to examine whether partly incorrect subtitles that are automatically generated by an Automatic Speech Recognition (ASR) system, improve speech comprehension by listeners with hearing impairment. In an earlier study (Zekveld et al. 2008), we showed that speech comprehension in noise by young listeners with normal hearing improves when presenting partly incorrect, automatically generated subtitles. The current study focused on the effects of age, hearing loss, visual working memory capacity, and linguistic skills on the benefit obtained from automatically generated subtitles during listening to speech in noise. In order to investigate the effects of age and hearing loss, three groups of participants were included: 22 young persons with normal hearing (YNH, mean age = 21 years), 22 middle-aged adults with normal hearing (MA-NH, mean age = 55 years) and 30 middle-aged adults with hearing impairment (MA-HI, mean age = 57 years). The benefit from automatic subtitling was measured by Speech Reception Threshold (SRT) tests (Plomp & Mimpen, 1979). Both unimodal auditory and bimodal audiovisual SRT tests were performed. In the audiovisual tests, the subtitles were presented simultaneously with the speech, whereas in the auditory test, only speech was presented. The difference between the auditory and audiovisual SRT was defined as the audiovisual benefit. Participants additionally rated the listening effort. We examined the influences of ASR accuracy level and text delay on the audiovisual benefit and the listening effort using a repeated measures General Linear Model analysis. In a correlation analysis, we evaluated the relationships between age, auditory SRT, visual working memory capacity and the audiovisual benefit and listening effort. The automatically generated subtitles improved speech comprehension in noise for all ASR accuracies and delays covered by the current study. Higher ASR accuracy levels resulted in more benefit obtained

  2. Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition.

    Science.gov (United States)

    Zhang, Zelun; Poslad, Stefan

    2013-11-01

    Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals.

  3. Loneliness and the social monitoring system: Emotion recognition and eye gaze in a real-life conversation

    NARCIS (Netherlands)

    Lodder, G.M.A.; Scholte, R.H.J.; Goossens, L.; Engels, R.C.M.E.; Verhagen, M.

    2016-01-01

    Based on the belongingness regulation theory (Gardner et al., 2005, Pers. Soc. Psychol. Bull., 31, 1549), this study focuses on the relationship between loneliness and social monitoring. Specifically, we examined whether loneliness relates to performance on three emotion recognition tasks and

  4. RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system.

    Science.gov (United States)

    Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari, Shahram; Wah, Teh Ying; Saeed, Aghabozorgi; Mat Kiah, Miss Laiha; Balas, Valentina Emilia

    2016-03-01

    Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

  5. Fusing Facial Features for Face Recognition

    Directory of Open Access Journals (Sweden)

    Jamal Ahmad Dargham

    2012-06-01

    Full Text Available Face recognition is an important biometric method because of its potential applications in many fields, such as access control, surveillance, and human-computer interaction. In this paper, a face recognition system that fuses the outputs of three face recognition systems based on Gabor jets is presented. The first system uses the magnitude, the second uses the phase, and the third uses the phase-weighted magnitude of the jets. The jets are generated from facial landmarks selected using three selection methods. It was found out that fusing the facial features gives better recognition rate than either facial feature used individually regardless of the landmark selection method.

  6. Similarity measures for face recognition

    CERN Document Server

    Vezzetti, Enrico

    2015-01-01

    Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.

  7. Pattern Recognition Control Design

    Science.gov (United States)

    Gambone, Elisabeth A.

    2018-01-01

    Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

  8. Flexible Piezoelectric Sensor-Based Gait Recognition

    Directory of Open Access Journals (Sweden)

    Youngsu Cha

    2018-02-01

    Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

  9. [Neurological disease and facial recognition].

    Science.gov (United States)

    Kawamura, Mitsuru; Sugimoto, Azusa; Kobayakawa, Mutsutaka; Tsuruya, Natsuko

    2012-07-01

    To discuss the neurological basis of facial recognition, we present our case reports of impaired recognition and a review of previous literature. First, we present a case of infarction and discuss prosopagnosia, which has had a large impact on face recognition research. From a study of patient symptoms, we assume that prosopagnosia may be caused by unilateral right occipitotemporal lesion and right cerebral dominance of facial recognition. Further, circumscribed lesion and degenerative disease may also cause progressive prosopagnosia. Apperceptive prosopagnosia is observed in patients with posterior cortical atrophy (PCA), pathologically considered as Alzheimer's disease, and associative prosopagnosia in frontotemporal lobar degeneration (FTLD). Second, we discuss face recognition as part of communication. Patients with Parkinson disease show social cognitive impairments, such as difficulty in facial expression recognition and deficits in theory of mind as detected by the reading the mind in the eyes test. Pathological and functional imaging studies indicate that social cognitive impairment in Parkinson disease is possibly related to damages in the amygdalae and surrounding limbic system. The social cognitive deficits can be observed in the early stages of Parkinson disease, and even in the prodromal stage, for example, patients with rapid eye movement (REM) sleep behavior disorder (RBD) show impairment in facial expression recognition. Further, patients with myotonic dystrophy type 1 (DM 1), which is a multisystem disease that mainly affects the muscles, show social cognitive impairment similar to that of Parkinson disease. Our previous study showed that facial expression recognition impairment of DM 1 patients is associated with lesion in the amygdalae and insulae. Our study results indicate that behaviors and personality traits in DM 1 patients, which are revealed by social cognitive impairment, are attributable to dysfunction of the limbic system.

  10. Iris Recognition Using Wavelet

    Directory of Open Access Journals (Sweden)

    Khaliq Masood

    2013-08-01

    Full Text Available Biometric systems are getting more attention in the present era. Iris recognition is one of the most secure and authentic among the other biometrics and this field demands more authentic, reliable and fast algorithms to implement these biometric systems in real time. In this paper, an efficient localization technique is presented to identify pupil and iris boundaries using histogram of the iris image. Two small portions of iris have been used for polar transformation to reduce computational time and to increase the efficiency of the system. Wavelet transform is used for feature vector generation. Rotation of iris is compensated without shifts in the iris code. System is tested on Multimedia University Iris Database and results show that proposed system has encouraging performance.

  11. Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

    Directory of Open Access Journals (Sweden)

    Yasmine Probst

    2015-07-01

    Full Text Available Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT, local binary patterns (LBP, and colour are used for describing food images. The popular bag-of-words (BoW model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

  12. Speech recognition implementation in radiology

    International Nuclear Information System (INIS)

    White, Keith S.

    2005-01-01

    Continuous speech recognition (SR) is an emerging technology that allows direct digital transcription of dictated radiology reports. The SR systems are being widely deployed in the radiology community. This is a review of technical and practical issues that should be considered when implementing an SR system. (orig.)

  13. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    International Nuclear Information System (INIS)

    Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C.; Busch, P.

    2010-01-01

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  14. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    Energy Technology Data Exchange (ETDEWEB)

    Makili, L. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, S., E-mail: sebas@dia.uned.e [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, I.; Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Farias, G. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Institut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

    2010-07-15

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  15. The software for automatic creation of the formal grammars used by speech recognition, computer vision, editable text conversion systems, and some new functions

    Science.gov (United States)

    Kardava, Irakli; Tadyszak, Krzysztof; Gulua, Nana; Jurga, Stefan

    2017-02-01

    For more flexibility of environmental perception by artificial intelligence it is needed to exist the supporting software modules, which will be able to automate the creation of specific language syntax and to make a further analysis for relevant decisions based on semantic functions. According of our proposed approach, of which implementation it is possible to create the couples of formal rules of given sentences (in case of natural languages) or statements (in case of special languages) by helping of computer vision, speech recognition or editable text conversion system for further automatic improvement. In other words, we have developed an approach, by which it can be achieved to significantly improve the training process automation of artificial intelligence, which as a result will give us a higher level of self-developing skills independently from us (from users). At the base of our approach we have developed a software demo version, which includes the algorithm and software code for the entire above mentioned component's implementation (computer vision, speech recognition and editable text conversion system). The program has the ability to work in a multi - stream mode and simultaneously create a syntax based on receiving information from several sources.

  16. Biometric correspondence between reface computerized facial approximations and CT-derived ground truth skin surface models objectively examined using an automated facial recognition system.

    Science.gov (United States)

    Parks, Connie L; Monson, Keith L

    2018-05-01

    This study employed an automated facial recognition system as a means of objectively evaluating biometric correspondence between a ReFace facial approximation and the computed tomography (CT) derived ground truth skin surface of the same individual. High rates of biometric correspondence were observed, irrespective of rank class (R k ) or demographic cohort examined. Overall, 48% of the test subjects' ReFace approximation probes (n=96) were matched to his or her corresponding ground truth skin surface image at R 1 , a rank indicating a high degree of biometric correspondence and a potential positive identification. Identification rates improved with each successively broader rank class (R 10 =85%, R 25 =96%, and R 50 =99%), with 100% identification by R 57 . A sharp increase (39% mean increase) in identification rates was observed between R 1 and R 10 across most rank classes and demographic cohorts. In contrast, significantly lower (p0.05) performance differences were observed across demographic cohorts or CT scan protocols. Performance measures observed in this research suggest that ReFace approximations are biometrically similar to the actual faces of the approximated individuals and, therefore, may have potential operational utility in contexts in which computerized approximations are utilized as probes in automated facial recognition systems. Copyright © 2018. Published by Elsevier B.V.

  17. Type I-E CRISPR-Cas Systems Discriminate Target from Non-Target DNA through Base Pairing-Independent PAM Recognition

    Science.gov (United States)

    Datsenko, Kirill A.; Jackson, Ryan N.; Wiedenheft, Blake; Severinov, Konstantin; Brouns, Stan J. J.

    2013-01-01

    Discriminating self and non-self is a universal requirement of immune systems. Adaptive immune systems in prokaryotes are centered around repetitive loci called CRISPRs (clustered regularly interspaced short palindromic repeat), into which invader DNA fragments are incorporated. CRISPR transcripts are processed into small RNAs that guide CRISPR-associated (Cas) proteins to invading nucleic acids by complementary base pairing. However, to avoid autoimmunity it is essential that these RNA-guides exclusively target invading DNA and not complementary DNA sequences (i.e., self-sequences) located in the host's own CRISPR locus. Previous work on the Type III-A CRISPR system from Staphylococcus epidermidis has demonstrated that a portion of the CRISPR RNA-guide sequence is involved in self versus non-self discrimination. This self-avoidance mechanism relies on sensing base pairing between the RNA-guide and sequences flanking the target DNA. To determine if the RNA-guide participates in self versus non-self discrimination in the Type I-E system from Escherichia coli we altered base pairing potential between the RNA-guide and the flanks of DNA targets. Here we demonstrate that Type I-E systems discriminate self from non-self through a base pairing-independent mechanism that strictly relies on the recognition of four unchangeable PAM sequences. In addition, this work reveals that the first base pair between the guide RNA and the PAM nucleotide immediately flanking the target sequence can be disrupted without affecting the interference phenotype. Remarkably, this indicates that base pairing at this position is not involved in foreign DNA recognition. Results in this paper reveal that the Type I-E mechanism of avoiding self sequences and preventing autoimmunity is fundamentally different from that employed by Type III-A systems. We propose the exclusive targeting of PAM-flanked sequences to be termed a target versus non-target discrimination mechanism. PMID:24039596

  18. Dynamic Programming Algorithms in Speech Recognition

    Directory of Open Access Journals (Sweden)

    Titus Felix FURTUNA

    2008-01-01

    Full Text Available In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.

  19. Pedestrian recognition using automotive radar sensors

    OpenAIRE

    A. Bartsch; F. Fitzek; R. H. Rasshofer

    2012-01-01

    The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insight...

  20. TeenBP: Development and Piloting of an EHR-Linked Clinical Decision Support System to Improve Recognition of Hypertension in Adolescents.

    Science.gov (United States)

    Kharbanda, Elyse O; Nordin, James D; Sinaiko, Alan R; Ekstrom, Heidi L; Stultz, Jerry M; Sherwood, Nancy E; Fontaine, Patricia L; Asche, Steve E; Dehmer, Steven P; Amundson, Jerry H; Appana, Deepika X; Bergdall, Anna R; Hayes, Marcia G; O'Connor, Patrick J

    2015-01-01

    Blood pressure (BP) is routinely measured in children and adolescents during primary care visits. However, elevated BP or hypertension is frequently not diagnosed or evaluated further by primary care providers. Barriers to recognition include lack of clinician buy-in, competing priorities, and complexity of the standard BP tables. We have developed and piloted TeenBP- a web-based, electronic health record (EHR) linked system designed to improve recognition of prehypertension and hypertension in adolescents during primary care visits. Important steps in developing TeenBP included the following: review of national BP guidelines, consideration of clinic workflow, engagement of clinical leaders, and evaluation of the impact on clinical sites. Use of a web-based platform has facilitated updates to the TeenBP algorithm and to the message content. In addition, the web-based platform has allowed for development of a sophisticated display of patient-specific information at the point of care. In the TeenBP pilot, conducted at a single pediatric and family practice site with six clinicians, over a five-month period, more than half of BPs in the hypertensive range were clinically recognized. Furthermore, in this small pilot the TeenBP clinical decision support (CDS) was accepted by providers and clinical staff. Effectiveness of the TeenBP CDS will be determined in a two-year cluster-randomized clinical trial, currently underway at 20 primary care sites. Use of technology to extract and display clinically relevant data stored within the EHR may be a useful tool for improving recognition of adolescent hypertension during busy primary care visits. In the future, the methods developed specifically for TeenBP are likely to be translatable to a wide range of acute and chronic issues affecting children and adolescents.

  1. Graphical symbol recognition

    OpenAIRE

    K.C. , Santosh; Wendling , Laurent

    2015-01-01

    International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

  2. Invariant Face recognition Using Infrared Images

    International Nuclear Information System (INIS)

    Zahran, E.G.

    2012-01-01

    Over the past few decades, face recognition has become a rapidly growing research topic due to the increasing demands in many applications of our daily life such as airport surveillance, personal identification in law enforcement, surveillance systems, information safety, securing financial transactions, and computer security. The objective of this thesis is to develop a face recognition system capable of recognizing persons with a high recognition capability, low processing time, and under different illumination conditions, and different facial expressions. The thesis presents a study for the performance of the face recognition system using two techniques; the Principal Component Analysis (PCA), and the Zernike Moments (ZM). The performance of the recognition system is evaluated according to several aspects including the recognition rate, and the processing time. Face recognition systems that use visual images are sensitive to variations in the lighting conditions and facial expressions. The performance of these systems may be degraded under poor illumination conditions or for subjects of various skin colors. Several solutions have been proposed to overcome these limitations. One of these solutions is to work in the Infrared (IR) spectrum. IR images have been suggested as an alternative source of information for detection and recognition of faces, when there is little or no control over lighting conditions. This arises from the fact that these images are formed due to thermal emissions from skin, which is an intrinsic property because these emissions depend on the distribution of blood vessels under the skin. On the other hand IR face recognition systems still have limitations with temperature variations and recognition of persons wearing eye glasses. In this thesis we will fuse IR images with visible images to enhance the performance of face recognition systems. Images are fused using the wavelet transform. Simulation results show that the fusion of visible and

  3. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

    2007-01-01

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

  4. Evaluation of Beef by Electronic Tongue System TS-5000Z: Flavor Assessment, Recognition and Chemical Compositions According to Its Correlation with Flavor.

    Directory of Open Access Journals (Sweden)

    Xinzhuang Zhang

    Full Text Available The aim of this study was to assess the ability of electronic tongue system TS-5000Z to evaluate meat quality based on flavor assessment, recognition and correlation with the meat chemical composition. Meat was sampled from eighteen beef cattle including 6 Wagyu breed cattle, 6 Angus breed cattle and 6 Simmental breed cattle. Chemical composition including dry matter, crude protein, fat, ash, cholesterol and taurine and flavor of the meat were measured. The results showed that different breed cattle had different chemical compositions and flavor, which contains sourness, umami, saltiness, bitterness, astringency, aftertaste from astringency, aftertaste from bitterness and aftertaste from umami, respectively. A principal component analysis (PCA showed an easily visible separation between different breeds of cattle and indicated that TS-5000Z made a rapid identification of different breeds of cattle. In addition, TS-5000Z seemed to be used to predict the chemical composition according to its correlation with the flavor. In conclusion, TS-5000Z would be used as a rapid analytical tool to evaluate the beef quality both qualitatively and quantitatively, based on flavor assessment, recognition and chemical composition according to its correlation with flavor.

  5. A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

    Directory of Open Access Journals (Sweden)

    Ahmad Jalal

    2017-08-01

    Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

  6. Cullin1-P is an Essential Component of Non-Self Recognition System in Self-Incompatibility in Petunia.

    Science.gov (United States)

    Kubo, Ken-Ichi; Tsukahara, Mai; Fujii, Sota; Murase, Kohji; Wada, Yuko; Entani, Tetsuyuki; Iwano, Megumi; Takayama, Seiji

    2016-11-01

    Self-incompatibility (SI) in flowering plants is a genetic reproductive barrier to distinguish self- and non-self pollen to promote outbreeding. In Solanaceae, self-pollen is rejected by the ribonucleases expressed in the styles (S-RNases), via its cytotoxic function. On the other side, the male-determinant is the S-locus F-box proteins (SLFs) expressed in pollen. Multiple SLFs collaboratively detoxify non-self S-RNases, therefore, non-self recognition is the mode of self-/non-self discrimination in Solanaceae. It is considered that SLFs function as a substrate-recognition module of the Skp1-Cullin1-F-box (SCF) complex that inactivates non-self S-RNases via their polyubiquitination, which leads to degradation by 26S proteasome. In fact, PhSSK1 (Petunia hybrida SLF-interacting Skp1-like1) was identified as a specific component of SCF SLF and was shown to be essential for detoxification of S-RNase in Petunia However, different molecules are proposed as the candidate Cullin1, another component of SCF SLF , and there is as yet no definite conclusion. Here, we identified five Cullin1s from the expressed sequence tags (ESTs) derived from the male reproductive organ in Petunia Among them, only PhCUL1-P was co-immunoprecipitated with S 7 -SLF2. In vitro protein-binding assay suggested that PhSSK1 specifically forms a complex with PhCUL1-P in an SLF-dependent manner. Knockdown of PhCUL1-P suppressed fertility of transgenic pollen in cross-compatible pollination in the functional S-RNase-dependent manner. These results suggested that SCF SLF selectively uses PhCUL1-P. Phylogeny of Cullin1s indicates that CUL1-P is recruited into the SI machinery during the evolution of Solanaceae, suggesting that the SI components have evolved differently among species in Solanaceae and Rosaceae, despite both families sharing the S-RNase-based SI. © The Author 2016. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For

  7. Enhancing spoken connected-digit recognition accuracy by error ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    nition systems have gained acceptable accuracy levels, the accuracy of recognition of current connected ... bar code and ISBN1 library code to name a few. ..... Kopec G, Bush M 1985 Network-based connected-digit recognition. IEEE Trans.

  8. Sleep Enhances Explicit Recollection in Recognition Memory

    Science.gov (United States)

    Drosopoulos, Spyridon; Wagner, Ullrich; Born, Jan

    2005-01-01

    Recognition memory is considered to be supported by two different memory processes, i.e., the explicit recollection of information about a previous event and an implicit process of recognition based on a contextual sense of familiarity. Both types of memory supposedly rely on distinct memory systems. Sleep is known to enhance the consolidation of…

  9. Recognition and Toleration

    DEFF Research Database (Denmark)

    Lægaard, Sune

    2010-01-01

    Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

  10. An automated patient recognition method based on an image-matching technique using previous chest radiographs in the picture archiving and communication system environment

    International Nuclear Information System (INIS)

    Morishita, Junji; Katsuragawa, Shigehiko; Kondo, Keisuke; Doi, Kunio

    2001-01-01

    An automated patient recognition method for correcting 'wrong' chest radiographs being stored in a picture archiving and communication system (PACS) environment has been developed. The method is based on an image-matching technique that uses previous chest radiographs. For identification of a 'wrong' patient, the correlation value was determined for a previous image of a patient and a new, current image of the presumed corresponding patient. The current image was shifted horizontally and vertically and rotated, so that we could determine the best match between the two images. The results indicated that the correlation values between the current and previous images for the same, 'correct' patients were generally greater than those for different, 'wrong' patients. Although the two histograms for the same patient and for different patients overlapped at correlation values greater than 0.80, most parts of the histograms were separated. The correlation value was compared with a threshold value that was determined based on an analysis of the histograms of correlation values obtained for the same patient and for different patients. If the current image is considered potentially to belong to a 'wrong' patient, then a warning sign with the probability for a 'wrong' patient is provided to alert radiology personnel. Our results indicate that at least half of the 'wrong' images in our database can be identified correctly with the method described in this study. The overall performance in terms of a receiver operating characteristic curve showed a high performance of the system. The results also indicate that some readings of 'wrong' images for a given patient in the PACS environment can be prevented by use of the method we developed. Therefore an automated warning system for patient recognition would be useful in correcting 'wrong' images being stored in the PACS environment

  11. A New Contactless Fault Diagnosis Approach for Pantograph-Catenary System Using Pattern Recognition and Image Processing Methods

    Directory of Open Access Journals (Sweden)

    AYDIN, I.

    2014-08-01

    Full Text Available Comfort and safety of railway transport has become more important as train speeds continue to increase. In electrified railways, the electrical current of the train is produced by the sliding contact between the pantograph and catenary. The quality of the current depends on the reliability of contact between the pantograph and catenary. So, pantograph inspection is very important task in electrified railways and it is periodically made for preventing dangerous situations. This inspection is operated manually by taking the pantograph to the service for visual anomalies. However, this monitoring is impractical because of time consuming and slowness, as locomotive remains disabled. An innovative method based on image processing and pattern recognition is proposed in this paper for online monitoring of the catenary-pantograph interaction. The images are acquired from a digital line-scan camera. Data are simultaneously processed according to edge detection and Hough transform, and then the obtained features are provided to a D-Markov based state machine, and the pantograph related faults, such as overheating of the pantograph strip, bursts of arcing, and irregular positioning of the contact line are diagnosed. The proposed method is verified by real faulty and healthy pantograph videos.

  12. Towards automatic forensic face recognition

    NARCIS (Netherlands)

    Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    2011-01-01

    In this paper we present a methodology and experimental results for evidence evaluation in the context of forensic face recognition. In forensic applications, the matching score (hereafter referred to as similarity score) from a biometric system must be represented as a Likelihood Ratio (LR). In our

  13. Kazakh Traditional Dance Gesture Recognition

    Science.gov (United States)

    Nussipbekov, A. K.; Amirgaliyev, E. N.; Hahn, Minsoo

    2014-04-01

    Full body gesture recognition is an important and interdisciplinary research field which is widely used in many application spheres including dance gesture recognition. The rapid growth of technology in recent years brought a lot of contribution in this domain. However it is still challenging task. In this paper we implement Kazakh traditional dance gesture recognition. We use Microsoft Kinect camera to obtain human skeleton and depth information. Then we apply tree-structured Bayesian network and Expectation Maximization algorithm with K-means clustering to calculate conditional linear Gaussians for classifying poses. And finally we use Hidden Markov Model to detect dance gestures. Our main contribution is that we extend Kinect skeleton by adding headwear as a new skeleton joint which is calculated from depth image. This novelty allows us to significantly improve the accuracy of head gesture recognition of a dancer which in turn plays considerable role in whole body gesture recognition. Experimental results show the efficiency of the proposed method and that its performance is comparable to the state-of-the-art system performances.

  14. 8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

    Science.gov (United States)

    2010-01-01

    ...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

  15. A Kinect-Based Sign Language Hand Gesture Recognition System for Hearing- and Speech-Impaired: A Pilot Study of Pakistani Sign Language.

    Science.gov (United States)

    Halim, Zahid; Abbas, Ghulam

    2015-01-01

    Sign language provides hearing and speech impaired individuals with an interface to communicate with other members of the society. Unfortunately, sign language is not understood by most of the common people. For this, a gadget based on image processing and pattern recognition can provide with a vital aid for detecting and translating sign language into a vocal language. This work presents a system for detecting and understanding the sign language gestures by a custom built software tool and later translating the gesture into a vocal language. For the purpose of recognizing a particular gesture, the system employs a Dynamic Time Warping (DTW) algorithm and an off-the-shelf software tool is employed for vocal language generation. Microsoft(®) Kinect is the primary tool used to capture video stream of a user. The proposed method is capable of successfully detecting gestures stored in the dictionary with an accuracy of 91%. The proposed system has the ability to define and add custom made gestures. Based on an experiment in which 10 individuals with impairments used the system to communicate with 5 people with no disability, 87% agreed that the system was useful.

  16. Can the usage of human growth hormones affect facial appearance and the accuracy of face recognition systems?

    Science.gov (United States)

    Rose, Jake; Martin, Michael; Bourlai, Thirimachos

    2014-06-01

    In law enforcement and security applications, the acquisition of face images is critical in producing key trace evidence for the successful identification of potential threats. The goal of the study is to demonstrate that steroid usage significantly affects human facial appearance and hence, the performance of commercial and academic face recognition (FR) algorithms. In this work, we evaluate the performance of state-of-the-art FR algorithms on two unique face image datasets of subjects before (gallery set) and after (probe set) steroid (or human growth hormone) usage. For the purpose of this study, datasets of 73 subjects were created from multiple sources found on the Internet, containing images of men and women before and after steroid usage. Next, we geometrically pre-processed all images of both face datasets. Then, we applied image restoration techniques on the same face datasets, and finally, we applied FR algorithms in order to match the pre-processed face images of our probe datasets against the face images of the gallery set. Experimental results demonstrate that only a specific set of FR algorithms obtain the most accurate results (in terms of the rank-1 identification rate). This is because there are several factors that influence the efficiency of face matchers including (i) the time lapse between the before and after image pre-processing and restoration face photos, (ii) the usage of different drugs (e.g. Dianabol, Winstrol, and Decabolan), (iii) the usage of different cameras to capture face images, and finally, (iv) the variability of standoff distance, illumination and other noise factors (e.g. motion noise). All of the previously mentioned complicated scenarios make clear that cross-scenario matching is a very challenging problem and, thus, further investigation is required.

  17. Systemic L-Kynurenine sulfate administration disrupts object recognition memory, alters open field behavior and decreases c-Fos immunopositivity in C57Bl/6 mice.

    Science.gov (United States)

    Varga, Dániel; Herédi, Judit; Kánvási, Zita; Ruszka, Marian; Kis, Zsolt; Ono, Etsuro; Iwamori, Naoki; Iwamori, Tokuko; Takakuwa, Hiroki; Vécsei, László; Toldi, József; Gellért, Levente

    2015-01-01

    L-Kynurenine (L-KYN) is a central metabolite of tryptophan degradation through the kynurenine pathway (KP). The systemic administration of L-KYN sulfate (L-KYNs) leads to a rapid elevation of the neuroactive KP metabolite kynurenic acid (KYNA). An elevated level of KYNA may have multiple effects on the synaptic transmission, resulting in complex behavioral changes, such as hypoactivity or spatial working memory deficits. These results emerged from studies that focused on rats, after low-dose L-KYNs treatment. However, in several studies neuroprotection was achieved through the administration of high-dose L-KYNs. In the present study, our aim was to investigate whether the systemic administration of a high dose of L-KYNs (300 mg/bwkg; i.p.) would produce alterations in behavioral tasks (open field or object recognition) in C57Bl/6j mice. To evaluate the changes in neuronal activity after L-KYNs treatment, in a separate group of animals we estimated c-Fos expression levels in the corresponding subcortical brain areas. The L-KYNs treatment did not affect the general ambulatory activity of C57Bl/6j mice, whereas it altered their moving patterns, elevating the movement velocity and resting time. Additionally, it seemed to increase anxiety-like behavior, as peripheral zone preference of the open field arena emerged and the rearing activity was attenuated. The treatment also completely abolished the formation of object recognition memory and resulted in decreases in the number of c-Fos-immunopositive-cells in the dorsal part of the striatum and in the CA1 pyramidal cell layer of the hippocampus. We conclude that a single exposure to L-KYNs leads to behavioral disturbances, which might be related to the altered basal c-Fos protein expression in C57Bl/6j mice.

  18. Gait recognition based on integral outline

    Science.gov (United States)

    Ming, Guan; Fang, Lv

    2017-02-01

    Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

  19. Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network

    Directory of Open Access Journals (Sweden)

    Fatchul Arifin

    2010-08-01

    Full Text Available The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.

  20. Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

    Directory of Open Access Journals (Sweden)

    Geonwoo Park

    2018-02-01

    Full Text Available When we develop voice-activated human-appliance interface systems in smart homes, named entity recognition (NER is an essential tool for extracting execution targets from natural language commands. Previous studies on NER systems generally include supervised machine-learning methods that require a substantial amount of human-annotated training corpus. In the smart home environment, categories of named entities should be defined according to voice-activated devices (e.g., food names for refrigerators and song titles for music players. The previous machine-learning methods make it difficult to change categories of named entities because a large amount of the training corpus should be newly constructed by hand. To address this problem, we present a semi-supervised NER system to minimize the time-consuming and labor-intensive task of constructing the training corpus. Our system uses distant supervision methods with two kinds of auto-labeling processes: auto-labeling based on heuristic rules for single-class named entity corpus generation and auto-labeling based on a pre-trained single-class NER model for multi-class named entity corpus generation. Then, our system improves NER accuracy by using a bagging-based active learning method. In our experiments that included a generic domain that featured 11 named entity classes and a context-specific domain about baseball that featured 21 named entity classes, our system demonstrated good performances in both domains, with F1-measures of 0.777 and 0.958, respectively. Since our system was built from a relatively small human-annotated training corpus, we believe it is a viable alternative to current NER systems in smart home environments.