Science.gov

Sample records for adaptive automatic recognition

  1. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    PubMed Central

    Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  2. Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.

    PubMed

    Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  3. Automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Espy-Wilson, Carol

    2005-04-01

    Great strides have been made in the development of automatic speech recognition (ASR) technology over the past thirty years. Most of this effort has been centered around the extension and improvement of Hidden Markov Model (HMM) approaches to ASR. Current commercially-available and industry systems based on HMMs can perform well for certain situational tasks that restrict variability such as phone dialing or limited voice commands. However, the holy grail of ASR systems is performance comparable to humans-in other words, the ability to automatically transcribe unrestricted conversational speech spoken by an infinite number of speakers under varying acoustic environments. This goal is far from being reached. Key to the success of ASR is effective modeling of variability in the speech signal. This tutorial will review the basics of ASR and the various ways in which our current knowledge of speech production, speech perception and prosody can be exploited to improve robustness at every level of the system.

  4. Practical automatic Arabic license plate recognition system

    NASA Astrophysics Data System (ADS)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

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

  5. Support vector machine for automatic pain recognition

    NASA Astrophysics Data System (ADS)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

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

  6. Automatic Modulation Recognition of Mixed Multiple Source Signals

    NASA Astrophysics Data System (ADS)

    Tan, Xiaobo; Zhang, Hang; Lu, Wei

    2011-03-01

    In this paper, automatic modulation recognition of mixed multiple source signals is discussed. At first, the algorithm of equivariant adaptive source separation (EASI) is employed to separate signals from their mixed waveforms. Four features of five modulated signals are extracted and then two classifiers, decision tree and neutral network are used to complete modulation classification. The effects of symbol shaping on features extraction and validation of source separation are also investigated. Simulations show that the average probability of correct recognition of the classifiers is very depended on the performance of source separation. When SNR (Signal to Noise Ratio) is larger than 15 dB and the number of mixed source signals is less than 4, the average probability of correct recognition is above 0.6 for decision tree classifier and 0.63 for neutral network classifier. Simulations and discussions about automatic modulation recognition for source signals surfed Rayleigh flat fading are also presented.

  7. Automatic TLI recognition system, general description

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report is a general description of an automatic target recognition system developed at the Idaho National Engineering Laboratory for the Department of Energy. A user`s manual is a separate volume, Automatic TLI Recognition System, User`s Guide, and a programmer`s manual is Automatic TLI Recognition System, Programmer`s Guide. This system was designed as an automatic target recognition system for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system naturally incorporates image data fusion, and it 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. In addition to its primary function as a trainable target recognition system, this is also a versatile, general-purpose tool for image manipulation and analysis, which can be either keyboard-driven or script-driven. This report includes descriptions of three variants of the computer hardware, a description of the mathematical basis if the training process, and a description with examples of the system capabilities.

  8. Automatic speech recognition in air traffic control

    NASA Technical Reports Server (NTRS)

    Karlsson, Joakim

    1990-01-01

    Automatic Speech Recognition (ASR) technology and its application to the Air Traffic Control system are described. The advantages of applying ASR to Air Traffic Control, as well as criteria for choosing a suitable ASR system are presented. Results from previous research and directions for future work at the Flight Transportation Laboratory are outlined.

  9. Automatic TLI recognition system beta prototype testing

    SciTech Connect

    Lassahn, G.D.

    1996-06-01

    This report describes the beta prototype automatic target recognition system ATR3, and some performance tests done with this system. This is a fully operational system, with a high computational speed. It is useful for findings any kind of target in digitized image data, and as a general purpose image analysis tool.

  10. Automatic TLI recognition system, user`s guide

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report describes how to use an automatic target recognition system (version 14). In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a programmer`s manual, Automatic TLI Recognition System, Programmer`s Guide.

  11. Automatic speech recognition technology development at ITT Defense Communications Division

    NASA Technical Reports Server (NTRS)

    White, George M.

    1977-01-01

    An assessment of the applications of automatic speech recognition to defense communication systems is presented. Future research efforts include investigations into the following areas: (1) dynamic programming; (2) recognition of speech degraded by noise; (3) speaker independent recognition; (4) large vocabulary recognition; (5) word spotting and continuous speech recognition; and (6) isolated word recognition.

  12. Automatic stereoscopic system for person recognition

    NASA Astrophysics Data System (ADS)

    Murynin, Alexander B.; Matveev, Ivan A.; Kuznetsov, Victor D.

    1999-06-01

    A biometric access control system based on identification of human face is presented. The system developed performs remote measurements of the necessary face features. Two different scenarios of the system behavior are implemented. The first one assumes the verification of personal data entered by visitor from console using keyboard or card reader. The system functions as an automatic checkpoint, that strictly controls access of different visitors. The other scenario makes it possible to identify visitors without any person identifier or pass. Only person biometrics are used to identify the visitor. The recognition system automatically finds necessary identification information preliminary stored in the database. Two laboratory models of recognition system were developed. The models are designed to use different information types and sources. In addition to stereoscopic images inputted to computer from cameras the models can use voice data and some person physical characteristics such as person's height, measured by imaging system.

  13. Embedded knowledge-based system for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Aboutalib, A. O.

    1990-10-01

    The development of a reliable Automatic Target Recognition (ATE) system is considered a very critical and challenging problem. Existing ATE Systems have inherent limitations in terms of recognition performance and the ability to learn and adapt. Artificial Intelligence Techniques have the potential to improve the performance of ATh Systems. In this paper, we presented a novel Knowledge-Engineering tool, termed, the Automatic Reasoning Process (ARP) , that can be used to automatically develop and maintain a Knowledge-Base (K-B) for the ATR Systems. In its learning mode, the ARP utilizes Learning samples to automatically develop the ATR K-B, which consists of minimum size sets of necessary and sufficient conditions for each target class. In its operational mode, the ARP infers the target class from sensor data using the ATh K-B System. The ARP also has the capability to reason under uncertainty, and can support both statistical and model-based approaches for ATR development. The capabilities of the ARP are compared and contrasted to those of another Knowledge-Engineering tool, termed, the Automatic Rule Induction (ARI) which is based on maximizing the mutual information. The AR? has been implemented in LISP on a VAX-GPX workstation.

  14. Image understanding research for automatic target recognition

    SciTech Connect

    Bhanu, B. ); Jones, T.L. )

    1993-10-01

    Automatic Target Recognition (ATR) is an extremely important capability for defense applications. Many aspects of Image Understanding (IU) research are traditionally used to solve ATR problems. In this paper, the authors discuss ATR applications and problems in developing real-world ATR systems, and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance the effectiveness of ATR-based weapon systems. Finally, they conclude that technological gains in developing robust ATR systems will also lead to significant advances in many other areas of applications of image understanding.

  15. Automatic anatomy recognition on CT images with pathology

    NASA Astrophysics Data System (ADS)

    Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

  16. Automatic TLI recognition system, programmer`s guide

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report describes the software of an automatic target recognition system (version 14), from a programmer`s point of view. The intent is to provide information that will help people who wish to modify the software. In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a user`s manual, Automatic TLI Recognition System, User`s Guide. 2 refs.

  17. Automatic recognition of corpus callosum from sagittal brain MR images

    NASA Astrophysics Data System (ADS)

    Lee, Chulhee; Unser, Michael A.; Ketter, Terence A.

    1995-08-01

    We propose a new method to find the corpus callosum from sagittal brain MR images automatically. First, we calculate the statistical characteristics of the corpus callosum and obtain shape information. The recognition algorithm consists of two stages: extracting regions satisfying the statistical characteristics (gray level distribtuions) of the corpus callosum, and finding a region matching the shape information. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. Experiments show promising results.

  18. Wavelet-based learning vector quantization for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Chan, Lipchen A.; Nasrabadi, Nasser M.; Mirelli, Vincent

    1996-06-01

    An automatic target recognition classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. A recognition rate of 69.0 percent is achieved on a highly cluttered test set.

  19. Hidden Markov models in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Wrzoskowicz, Adam

    1993-11-01

    This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

  20. Military applications of automatic speech recognition and future requirements

    NASA Technical Reports Server (NTRS)

    Beek, Bruno; Cupples, Edward J.

    1977-01-01

    An updated summary of the state-of-the-art of automatic speech recognition and its relevance to military applications is provided. A number of potential systems for military applications are under development. These include: (1) digital narrowband communication systems; (2) automatic speech verification; (3) on-line cartographic processing unit; (4) word recognition for militarized tactical data system; and (5) voice recognition and synthesis for aircraft cockpit.

  1. Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

    NASA Astrophysics Data System (ADS)

    Nazimov, Alexey I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Koronovskii, Alexey A.; Sitnikova, Evgenija Y.

    2013-02-01

    The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

  2. Confidence and rejection in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Colton, Larry Don

    Automatic speech recognition (ASR) is performed imperfectly by computers. For some designated part (e.g., word or phrase) of the ASR output, rejection is deciding (yes or no) whether it is correct, and confidence is the probability (0.0 to 1.0) of it being correct. This thesis presents new methods of rejecting errors and estimating confidence for telephone speech. These are also called word or utterance verification and can be used in wordspotting or voice-response systems. Open-set or out-of-vocabulary situations are a primary focus. Language models are not considered. In vocabulary-dependent rejection all words in the target vocabulary are known in advance and a strategy can be developed for confirming each word. A word-specific artificial neural network (ANN) is shown to discriminate well, and scores from such ANNs are shown on a closed-set recognition task to reorder the N-best hypothesis list (N=3) for improved recognition performance. Segment-based duration and perceptual linear prediction (PLP) features are shown to perform well for such ANNs. The majority of the thesis concerns vocabulary- and task-independent confidence and rejection based on phonetic word models. These can be computed for words even when no training examples of those words have been seen. New techniques are developed using phoneme ranks instead of probabilities in each frame. These are shown to perform as well as the best other methods examined despite the data reduction involved. Certain new weighted averaging schemes are studied but found to give no performance benefit. Hierarchical averaging is shown to improve performance significantly: frame scores combine to make segment (phoneme state) scores, which combine to make phoneme scores, which combine to make word scores. Use of intermediate syllable scores is shown to not affect performance. Normalizing frame scores by an average of the top probabilities in each frame is shown to improve performance significantly. Perplexity of the wrong

  3. Automatic target recognition apparatus and method

    DOEpatents

    Baumgart, Chris W.; Ciarcia, Christopher A.

    2000-01-01

    An automatic target recognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.

  4. Remote weapon station for automatic target recognition system demand analysis

    NASA Astrophysics Data System (ADS)

    Lei, Zhang; Li, Sheng-cai; Shi, Cai

    2015-08-01

    Introduces a remote weapon station basic composition and the main advantage, analysis of target based on image automatic recognition system for remote weapon station of practical significance, the system elaborated the image based automatic target recognition system in the photoelectric stabilized technology, multi-sensor image fusion technology, integrated control target image enhancement, target behavior risk analysis technology, intelligent based on the character of the image automatic target recognition algorithm research, micro sensor technology as the key technology of the development in the field of demand.

  5. Automatic target recognition using vector quantization and neural networks

    NASA Astrophysics Data System (ADS)

    Chan, Lipchen A.; Nasrabadi, Nasser M.

    1999-12-01

    We propose an automatic target recognition (ATR) algorithm that uses a set of dedicated vector quantizers (VQs) and multilayer perceptrons (MLPs). For each target class at a specific range of aspects, the background pixels of an input image are first removed. The extracted target area is then subdivided into several subimages. A dedicated VQ codebook is constructed for each of the resulting subimages. Using the K-means algorithm, each VQ codebook learns a set of patterns representing the local features of a particular target for a specific range of aspects. The resulting codebooks are further trained by a modified learning vector quantization algorithm, which enhances the discriminatory power of the codebooks. Each final codebook is expected to give the lowest mean squared error (MSE) for its correct target class and range of aspects. These MSEs are then input to an array of window-level MLPs (WMLPs), where each WMLP is specialized in recognizing its intended target class for a specific range of aspects. The outputs of these WMLPs are manipulated and passed to a target-level MLP, which produces the final recognition results. We trained and tested the proposed ATR algorithm on large and realistic data sets and obtained impressive results using the wavelet-based adaptive produce VQs configuration.

  6. System integration of pattern recognition, adaptive aided, upper limb prostheses

    NASA Technical Reports Server (NTRS)

    Lyman, J.; Freedy, A.; Solomonow, M.

    1975-01-01

    The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

  7. A real-time optical automatic target recognition system

    NASA Astrophysics Data System (ADS)

    Chen, Huaixin; Nan, Jianshe; Li, Xiaosun; Wei, Honggang

    2004-04-01

    Automatic target recognition (ATR) technique has been applied in both civil and military. In this paper, we present a new optical pattern recognition system for target recognition. This system includes synthetic discriminate function (SDF) based practical optimized filters for the 3-D targets, the Reference Filter Libs for high correlation SNR, the mapping between the input (object regions) and the output (correlation peaks), and neural networks (ANN) for final decision making. The Real-time optical target recognition is realized by temporal multiplexing technique with electronic addressing spatial light modulator. The experiment results show that the proposed OPR system is efficient and reliable.

  8. Neural nets for adaptive filtering and adaptive pattern recognition

    SciTech Connect

    Widrow, B.; Winter, R.

    1988-03-01

    The fields of adaptive signal processing and adaptive neural networks have been developing independently but have that adaptive linear combiner (ALC) in common. With its inputs connected to a tapped delay line, the ALC becomes a key component of an adaptive filter. With its output connected to a quantizer, the ALC becomes an adaptive threshold element of adaptive neuron. Adaptive threshold elements, on the other hand, are the building blocks of neural networks. Today neural nets are the focus of widespread research interest. Areas of investigation include pattern recognition and trainable logic. Neural network systems have not yet had the commercial impact of adaptive filtering. The commonality of the ALC to adaptive signal processing and adaptive neural networks suggests the two fields have much to share with each other. This article describes practical applications of the ALC in signal processing and pattern recognition.

  9. A Survey on Automatic Speaker Recognition Systems

    NASA Astrophysics Data System (ADS)

    Saquib, Zia; Salam, Nirmala; Nair, Rekha P.; Pandey, Nipun; Joshi, Akanksha

    Human listeners are capable of identifying a speaker, over the telephone or an entryway out of sight, by listening to the voice of the speaker. Achieving this intrinsic human specific capability is a major challenge for Voice Biometrics. Like human listeners, voice biometrics uses the features of a person's voice to ascertain the speaker's identity. The best-known commercialized forms of voice Biometrics is Speaker Recognition System (SRS). Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. This literature survey paper gives brief introduction on SRS, and then discusses general architecture of SRS, biometric standards relevant to voice/speech, typical applications of SRS, and current research in Speaker Recognition Systems. We have also surveyed various approaches for SRS.

  10. 38 CFR 51.31 - Automatic recognition.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will...

  11. 38 CFR 51.31 - Automatic recognition.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will...

  12. 38 CFR 51.31 - Automatic recognition.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will...

  13. 38 CFR 51.31 - Automatic recognition.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will...

  14. 38 CFR 51.31 - Automatic recognition.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will...

  15. RFID: A Revolution in Automatic Data Recognition

    ERIC Educational Resources Information Center

    Deal, Walter F., III

    2004-01-01

    Radio frequency identification, or RFID, is a generic term for technologies that use radio waves to automatically identify people or objects. There are several methods of identification, but the most common is to store a serial number that identifies a person or object, and perhaps other information, on a microchip that is attached to an antenna…

  16. Automatic TLI recognition system. Part 2: User`s guide

    SciTech Connect

    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 is a user`s manual for an Automatic Target Recognition (ATR) system. This guide is intended to provide enough information and instruction to allow individuals to the system for their own applications.

  17. Intelligibility of laryngectomees' substitute speech: automatic speech recognition and subjective rating.

    PubMed

    Schuster, Maria; Haderlein, Tino; Nöth, Elmar; Lohscheller, Jörg; Eysholdt, Ulrich; Rosanowski, Frank

    2006-02-01

    Substitute speech after laryngectomy is characterized by restricted aero-acoustic properties in comparison with laryngeal speech and has therefore lower intelligibility. Until now, an objective means to determine and quantify the intelligibility has not existed, although the intelligibility can serve as a global outcome parameter of voice restoration after laryngectomy. An automatic speech recognition system was applied on recordings of a standard text read by 18 German male laryngectomees with tracheoesophageal substitute speech. The system was trained with normal laryngeal speakers and not adapted to severely disturbed voices. Substitute speech was compared to laryngeal speech of a control group. Subjective evaluation of intelligibility was performed by a panel of five experts and compared to automatic speech evaluation. Substitute speech showed lower syllables/s and lower word accuracy than laryngeal speech. Automatic speech recognition for substitute speech yielded word accuracy between 10.0 and 50% (28.7+/-12.1%) with sufficient discrimination. It complied with experts' subjective evaluations of intelligibility. The multi-rater kappa of the experts alone did not differ from the multi-rater kappa of experts and the recognizer. Automatic speech recognition serves as a good means to objectify and quantify global speech outcome of laryngectomees. For clinical use, the speech recognition system will be adapted to disturbed voices and can also be applied in other languages. PMID:16001246

  18. Automatic recognition of malicious intent indicators.

    SciTech Connect

    Drescher, D. J.; Yee, Mark L.; Giron, Casey; Fogler, Robert Joseph; Nguyen, Hung D.; Koch, Mark William

    2010-09-01

    A major goal of next-generation physical protection systems is to extend defenses far beyond the usual outer-perimeter-fence boundaries surrounding protected facilities. Mitigation of nuisance alarms is among the highest priorities. A solution to this problem is to create a robust capability to Automatically Recognize Malicious Indicators of intruders. In extended defense applications, it is not enough to distinguish humans from all other potential alarm sources as human activity can be a common occurrence outside perimeter boundaries. Our approach is unique in that it employs a stimulus to determine a malicious intent indicator for the intruder. The intruder's response to the stimulus can be used in an automatic reasoning system to decide the intruder's intent.

  19. Automatic emotion recognition based on body movement analysis: a survey.

    PubMed

    Zacharatos, Haris; Gatzoulis, Christos; Chrysanthou, Yiorgos L

    2014-01-01

    Humans are emotional beings, and their feelings influence how they perform and interact with computers. One of the most expressive modalities for humans is body posture and movement, which researchers have recently started exploiting for emotion recognition. This survey describes emerging techniques and modalities related to emotion recognition based on body movement, as well as recent advances in automatic emotion recognition. It also describes application areas and notation systems and explains the importance of movement segmentation. It then discusses unsolved problems and provides promising directions for future research. The Web extra (a PDF file) contains tables with additional information related to the article. PMID:25216477

  20. Automatic Speech Recognition: Reliability and Pedagogical Implications for Teaching Pronunciation

    ERIC Educational Resources Information Center

    Kim, In-Seok

    2006-01-01

    This study examines the reliability of automatic speech recognition (ASR) software used to teach English pronunciation, focusing on one particular piece of software, "FluSpeak, as a typical example." Thirty-six Korean English as a Foreign Language (EFL) college students participated in an experiment in which they listened to 15 sentences that…

  1. Assessing the Development of Automaticity in Second Language Word Recognition.

    ERIC Educational Resources Information Center

    Segalowitz, Sidney J.; Segalowitz, Norman S.; Wood, Anthony G.

    1998-01-01

    In a study of development of automaticity in second-language word recognition, 105 English-speakers speaking French performed multiple lexical-decision tasks, and differences in coefficient of variation of lexical decision reaction time were compared cross- sectionally and longitudinally. Results confirm that with extended learning experience, the…

  2. Using Automatic Speech Recognition Technology with Elicited Oral Response Testing

    ERIC Educational Resources Information Center

    Cox, Troy L.; Davies, Randall S.

    2012-01-01

    This study examined the use of automatic speech recognition (ASR) scored elicited oral response (EOR) tests to assess the speaking ability of English language learners. It also examined the relationship between ASR-scored EOR and other language proficiency measures and the ability of the ASR to rate speakers without bias to gender or native…

  3. Automatization and Orthographic Development in Second Language Visual Word Recognition

    ERIC Educational Resources Information Center

    Kida, Shusaku

    2016-01-01

    The present study investigated second language (L2) learners' acquisition of automatic word recognition and the development of L2 orthographic representation in the mental lexicon. Participants in the study were Japanese university students enrolled in a compulsory course involving a weekly 30-minute sustained silent reading (SSR) activity with…

  4. Length Scales in Bayesian Automatic Adaptive Quadrature

    NASA Astrophysics Data System (ADS)

    Adam, Gh.; Adam, S.

    2016-02-01

    Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1-16 (2012)] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule), mesoscopic (Simpson rule), and macroscopic (quadrature sums of high algebraic degrees of precision). Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.

  5. Automatic Recognition of Object Names in Literature

    NASA Astrophysics Data System (ADS)

    Bonnin, C.; Lesteven, S.; Derriere, S.; Oberto, A.

    2008-08-01

    SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

  6. Testing Saliency Parameters for Automatic Target Recognition

    NASA Technical Reports Server (NTRS)

    Pandya, Sagar

    2012-01-01

    A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

  7. Automatic recognition of harmonic bird sounds

    NASA Astrophysics Data System (ADS)

    Heller, Jason R.; Pinezich, John D.

    2005-09-01

    The method of sound recognition relies on a transformation of a sound into a spectrogram followed by extraction of the harmonics as curves. The extracted curves are called frequency tracks. A procedure called find-feasible-sets is used to extract sets of tracks that may correspond to harmonic sounds. If a set of tracks overlap each other sufficiently in time, then the set is designated a feasible set. Following the extraction of the feasible sets, the procedure find-maximal-subsets is applied to each feasible set. This procedure uses a function called harmonic-relate that determines if two tracks are harmonically related. All tracks that are not harmonically related to any other tracks in the feasible set are discarded. Furthermore, the feasible set is divided into maximal subsets. A maximal subset is a subset of the feasible set in which every track is harmonically related to one fixed track in the set called the reference track but no other tracks in the feasible set are related to the reference track. Each frequency track in a track set is transformed into a feature vector whose components describe the frequency, slope, and shape of the track. The species of birds analyzed are bluejay and herring gull.

  8. Automatic Speech Recognition from Neural Signals: A Focused Review

    PubMed Central

    Herff, Christian; Schultz, Tanja

    2016-01-01

    Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system. PMID:27729844

  9. Automatic Facial Expression Recognition and Operator Functional State

    NASA Technical Reports Server (NTRS)

    Blanson, Nina

    2011-01-01

    The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions.

  10. Digital signal processing algorithms for automatic voice recognition

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1987-01-01

    The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

  11. Automatic Facial Expression Recognition and Operator Functional State

    NASA Technical Reports Server (NTRS)

    Blanson, Nina

    2012-01-01

    The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions

  12. Adaptive pattern recognition and neural networks

    SciTech Connect

    Pao, Yohhan.

    1989-01-01

    The application of neural-network computers to pattern-recognition tasks is discussed in an introduction for advanced students. Chapters are devoted to the nature of the pattern-recognition task, the Bayesian approach to the estimation of class membership, the fuzzy-set approach, patterns with nonnumeric feature values, learning discriminants and the generalized perceptron, recognition and recall on the basis of partial cues, associative memories, self-organizing nets, the functional-link net, fuzzy logic in the linking of symbolic and subsymbolic processing, and adaptive pattern recognition and its applications. Also included are C-language programs for (1) a generalized delta-rule net for supervised learning and (2) unsupervised learning based on the discovery of clustered structure. 183 refs.

  13. Production ready feature recognition based automatic group technology part coding

    SciTech Connect

    Ames, A.L.

    1990-01-01

    During the past four years, a feature recognition based expert system for automatically performing group technology part coding from solid model data has been under development. The system has become a production quality tool, capable of quickly the geometry based portions of a part code with no human intervention. It has been tested on over 200 solid models, half of which are models of production Sandia designs. Its performance rivals that of humans performing the same task, often surpassing them in speed and uniformity. The feature recognition capability developed for part coding is being extended to support other applications, such as manufacturability analysis, automatic decomposition (for finite element meshing and machining), and assembly planning. Initial surveys of these applications indicate that the current capability will provide a strong basis for other applications and that extensions toward more global geometric reasoning and tighter coupling with solid modeler functionality will be necessary.

  14. Advanced automatic target recognition for police helicopter missions

    NASA Astrophysics Data System (ADS)

    Stahl, Christoph; Schoppmann, Paul

    2000-08-01

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

  15. An automatic recognition method of pointer instrument based on improved Hough transform

    NASA Astrophysics Data System (ADS)

    Xu, Li; Fang, Tian; Gao, Xiaoyu

    2015-10-01

    For the automatic recognition of pointer instrument, the method for the automatic recognition of pointer instrument based on improved Hough Transform was proposed in this paper. The automatic recognition of pointer instrument is applied to all kinds of lighting conditions, but the accuracy of it binaryzation will be influenced when the light is too strong or too dark. Therefore, the improved Ostu method was suggested to realize recognition for adaptive thresholding of pointer instrument under all kinds of lighting conditions. On the basis of dial image characteristics, Otsu method is used to get the value of maximum between-cluster variance and initial threshold than analyze its maximum between-cluster variance value to determine the light and shade of the image. When the images are too bright or too dark, the smaller pixels should be given up and then calculate the initial threshold by Otsu method again and again until the best binaryzation image was obtained. Hence, transform the pointer straight line of the binaryzation image to Hough parameter space through improved Hough Transform to determine the position of the pointer straight line by searching the maximum value of arrays of the same angle. Finally, according to angle method, the pointer reading was obtained by the linear relationship for the initial scale and angle of the pointer instrument. Results show that the improved Otsu method make pointer instrument possible to obtained the accuracy binaryzation image even though the light is too bright or too dark , which improves the adaptability of pointer instrument to automatic recognize the light under different conditions. For the pressure gauges with range of 60MPa, the relative error identification reached to 0.005 when use the improved Hough Transform Algorithm.

  16. Speech recognition for embedded automatic positioner for laparoscope

    NASA Astrophysics Data System (ADS)

    Chen, Xiaodong; Yin, Qingyun; Wang, Yi; Yu, Daoyin

    2014-07-01

    In this paper a novel speech recognition methodology based on Hidden Markov Model (HMM) is proposed for embedded Automatic Positioner for Laparoscope (APL), which includes a fixed point ARM processor as the core. The APL system is designed to assist the doctor in laparoscopic surgery, by implementing the specific doctor's vocal control to the laparoscope. Real-time respond to the voice commands asks for more efficient speech recognition algorithm for the APL. In order to reduce computation cost without significant loss in recognition accuracy, both arithmetic and algorithmic optimizations are applied in the method presented. First, depending on arithmetic optimizations most, a fixed point frontend for speech feature analysis is built according to the ARM processor's character. Then the fast likelihood computation algorithm is used to reduce computational complexity of the HMM-based recognition algorithm. The experimental results show that, the method shortens the recognition time within 0.5s, while the accuracy higher than 99%, demonstrating its ability to achieve real-time vocal control to the APL.

  17. Error Rates in Users of Automatic Face Recognition Software

    PubMed Central

    White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

    2015-01-01

    In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

  18. Automatic integration of social information in emotion recognition.

    PubMed

    Mumenthaler, Christian; Sander, David

    2015-04-01

    This study investigated the automaticity of the influence of social inference on emotion recognition. Participants were asked to recognize dynamic facial expressions of emotion (fear or anger in Experiment 1 and blends of fear and surprise or of anger and disgust in Experiment 2) in a target face presented at the center of a screen while a subliminal contextual face appearing in the periphery expressed an emotion (fear or anger) or not (neutral) and either looked at the target face or not. Results of Experiment 1 revealed that recognition of the target emotion of fear was improved when a subliminal angry contextual face gazed toward-rather than away from-the fearful face. We replicated this effect in Experiment 2, in which facial expression blends of fear and surprise were more often and more rapidly categorized as expressing fear when the subliminal contextual face expressed anger and gazed toward-rather than away from-the target face. With the contextual face appearing for 30 ms in total, including only 10 ms of emotion expression, and being immediately masked, our data provide the first evidence that social influence on emotion recognition can occur automatically. PMID:25688908

  19. Statistical Evaluation of Biometric Evidence in Forensic Automatic Speaker Recognition

    NASA Astrophysics Data System (ADS)

    Drygajlo, Andrzej

    Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned voice recording (trace). This paper aims at presenting forensic automatic speaker recognition (FASR) methods that provide a coherent way of quantifying and presenting recorded voice as biometric evidence. In such methods, the biometric evidence consists of the quantified degree of similarity between speaker-dependent features extracted from the trace and speaker-dependent features extracted from recorded speech of a suspect. The interpretation of recorded voice as evidence in the forensic context presents particular challenges, including within-speaker (within-source) variability and between-speakers (between-sources) variability. Consequently, FASR methods must provide a statistical evaluation which gives the court an indication of the strength of the evidence given the estimated within-source and between-sources variabilities. This paper reports on the first ENFSI evaluation campaign through a fake case, organized by the Netherlands Forensic Institute (NFI), as an example, where an automatic method using the Gaussian mixture models (GMMs) and the Bayesian interpretation (BI) framework were implemented for the forensic speaker recognition task.

  20. Image quality-based adaptive illumination normalisation for face recognition

    NASA Astrophysics Data System (ADS)

    Sellahewa, Harin; Jassim, Sabah A.

    2009-05-01

    Automatic face recognition is a challenging task due to intra-class variations. Changes in lighting conditions during enrolment and identification stages contribute significantly to these intra-class variations. A common approach to address the effects such of varying conditions is to pre-process the biometric samples in order normalise intra-class variations. Histogram equalisation is a widely used illumination normalisation technique in face recognition. However, a recent study has shown that applying histogram equalisation on well-lit face images could lead to a decrease in recognition accuracy. This paper presents a dynamic approach to illumination normalisation, based on face image quality. The quality of a given face image is measured in terms of its luminance distortion by comparing this image against a known reference face image. Histogram equalisation is applied to a probe image if its luminance distortion is higher than a predefined threshold. We tested the proposed adaptive illumination normalisation method on the widely used Extended Yale Face Database B. Identification results demonstrate that our adaptive normalisation produces better identification accuracy compared to the conventional approach where every image is normalised, irrespective of the lighting condition they were acquired.

  1. Distance-invariant automatic engagement level recognition using visual cues

    NASA Astrophysics Data System (ADS)

    Yun, Woo-han; Lee, Dongjin; Park, Chankyu; Kim, Jaehong

    2015-12-01

    In a camera-based engagement level recognition, a face is an important factor because cues mainly come from a face, which is affected from a distance between a camera and a user. In this paper, we present an automatic engagement level recognition method showing stable performance regardless of a distance between a camera and a user. We show a detailed process about getting a distance-invariant cue and compare its performance with and without the process. We also adopt a temporal pyramid structure to extract temporal statistical feature and present a voting method for an engagement level estimation. We show the results and the analysis using the database acquired in the real environment.

  2. Automatic target recognition using group-structured sparse representation

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Wu, Xuewen; He, Jun; Zhu, Xiaoming; Chen, Chao

    2014-06-01

    Sparse representation classification method has been increasingly used in the fields of computer vision and pattern analysis, due to its high recognition rate, little dependence on the features, robustness to corruption and occlusion, and etc. However, most of these existing methods aim to find the sparsest representations of the test sample y in an overcomplete dictionary, which do not particularly consider the relevant structure between the atoms in the dictionary. Moreover, sufficient training samples are always required by the sparse representation method for effective recognition. In this paper we formulate the classification as a group-structured sparse representation problem using a sparsity-inducing norm minimization optimization and propose a novel sparse representation-based automatic target recognition (ATR) framework for the practical applications in which the training samples are drawn from the simulation models of real targets. The experimental results show that the proposed approach improves the recognition rate of standard sparse models, and our system can effectively and efficiently recognize targets under real environments, especially, where the good characteristics of the sparse representation based classification method are kept.

  3. Algorithms for adaptive nonlinear pattern recognition

    NASA Astrophysics Data System (ADS)

    Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

    2011-09-01

    In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

  4. Automatic Recognition of Sunspots in HSOS Full-Disk Solar Images

    NASA Astrophysics Data System (ADS)

    Zhao, Cui; Lin, GangHua; Deng, YuanYong; Yang, Xiao

    2016-05-01

    A procedure is introduced to recognise sunspots automatically in solar full-disk photosphere images obtained from Huairou Solar Observing Station, National Astronomical Observatories of China. The images are first pre-processed through Gaussian algorithm. Sunspots are then recognised by the morphological Bot-hat operation and Otsu threshold. Wrong selection of sunspots is eliminated by a criterion of sunspot properties. Besides, in order to calculate the sunspots areas and the solar centre, the solar limb is extracted by a procedure using morphological closing and erosion operations and setting an adaptive threshold. Results of sunspot recognition reveal that the number of the sunspots detected by our procedure has a quite good agreement with the manual method. The sunspot recognition rate is 95% and error rate is 1.2%. The sunspot areas calculated by our method have high correlation (95%) with the area data from the United States Air Force/National Oceanic and Atmospheric Administration (USAF/NOAA).

  5. Selected military applications of automatic speech recognition technology

    NASA Astrophysics Data System (ADS)

    Woodard, J. P.; Cupples, E. J.

    1983-12-01

    Voice input for command and control, message sorting by voice, and advanced low-bit-rate voice communications systems are discussed in relation to automatic speech recognition (ASR) technology applications. Several research efforts in the areas of ASR and speech synthesis technology, which forms the basis for voice input/output systems, are described for military airborne and ground-based applications. The success of the speech enhancement unit in noise reduction for both human and machine listeners is documented and shown to be indispensible for the ASR used in message sorting, and for many command-and-control applications in harsh environments. Two experimental low-bit-rate systems, one phonetically based, the other based on vector, or block quantization, are compared. ASR technology is shown to have the potential to increase the effectiveness of man-machine communications in a variety of military applications, such as equipment maintenance and computers. Further research is necessary to solve some basic problems.

  6. On compensation of mismatched recording conditions in the Bayesian approach for forensic automatic speaker recognition.

    PubMed

    Botti, F; Alexander, A; Drygajlo, A

    2004-12-01

    This paper deals with a procedure to compensate for mismatched recording conditions in forensic speaker recognition, using a statistical score normalization. Bayesian interpretation of the evidence in forensic automatic speaker recognition depends on three sets of recordings in order to perform forensic casework: reference (R) and control (C) recordings of the suspect, and a potential population database (P), as well as a questioned recording (QR) . The requirement of similar recording conditions between suspect control database (C) and the questioned recording (QR) is often not satisfied in real forensic cases. The aim of this paper is to investigate a procedure of normalization of scores, which is based on an adaptation of the Test-normalization (T-norm) [2] technique used in the speaker verification domain, to compensate for the mismatch. Polyphone IPSC-02 database and ASPIC (an automatic speaker recognition system developed by EPFL and IPS-UNIL in Lausanne, Switzerland) were used in order to test the normalization procedure. Experimental results for three different recording condition scenarios are presented using Tippett plots and the effect of the compensation on the evaluation of the strength of the evidence is discussed.

  7. Processing, analysis, recognition, and automatic understanding of medical images

    NASA Astrophysics Data System (ADS)

    Tadeusiewicz, Ryszard; Ogiela, Marek R.

    2004-07-01

    Paper presents some new ideas introducing automatic understanding of the medical images semantic content. The idea under consideration can be found as next step on the way starting from capturing of the images in digital form as two-dimensional data structures, next going throw images processing as a tool for enhancement of the images visibility and readability, applying images analysis algorithms for extracting selected features of the images (or parts of images e.g. objects), and ending on the algorithms devoted to images classification and recognition. In the paper we try to explain, why all procedures mentioned above can not give us full satisfaction in many important medical problems, when we do need understand image semantic sense, not only describe the image in terms of selected features and/or classes. The general idea of automatic images understanding is presented as well as some remarks about the successful applications of such ideas for increasing potential possibilities and performance of computer vision systems dedicated to advanced medical images analysis. This is achieved by means of applying linguistic description of the picture merit content. After this we try use new AI methods to undertake tasks of the automatic understanding of images semantics in intelligent medical information systems. A successful obtaining of the crucial semantic content of the medical image may contribute considerably to the creation of new intelligent multimedia cognitive medical systems. Thanks to the new idea of cognitive resonance between stream of the data extracted form the image using linguistic methods and expectations taken from the representation of the medical knowledge, it is possible to understand the merit content of the image even if the form of the image is very different from any known pattern.

  8. Variable frame rate analysis for automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Tan, Zheng-Hua

    2007-09-01

    In this paper we investigate the use of variable frame rate (VFR) analysis in automatic speech recognition (ASR). First, we review VFR technique and analyze its behavior. It is experimentally shown that VFR improves ASR performance for signals with low signal-to-noise ratios since it generates improved acoustic models and substantially reduces insertion and substitution errors although it may increase deletion errors. It is also underlined that the match between the average frame rate and the number of hidden Markov model states is critical in implementing VFR. Secondly, we analyze an effective VFR method that uses a cumulative, weighted cepstral-distance criterion for frame selection and present a revision for it. Lastly, the revised VFR method is combined with spectral- and cepstral-domain enhancement methods including the minimum statistics noise estimation (MSNE) based spectral subtraction and the cepstral mean subtraction, variance normalization and ARMA filtering (MVA) process. Experiments on the Aurora 2 database justify that VFR is highly complementary to the enhancement methods. Enhancement of speech both facilitates the frame selection in VFR and provides de-noised speech for recognition.

  9. Automatic voice recognition using traditional and artificial neural network approaches

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  10. Hearing Status and Language Fluency as Predictors of Automatic Word and Sign Recognition.

    ERIC Educational Resources Information Center

    Marschark, Marc; Shroyer, Edgar H.

    1993-01-01

    This study of the automatic word and sign recognition of 66 hearing and deaf adults found that responding in sign took longer and created more Stroop interference than responding orally, independent of hearing status. Deaf subjects showed greater automaticity in recognizing signs than words, whereas hearing subjects showed greater automaticity in…

  11. Sign language perception research for improving automatic sign language recognition

    NASA Astrophysics Data System (ADS)

    ten Holt, Gineke A.; Arendsen, Jeroen; de Ridder, Huib; Koenderink-van Doorn, Andrea J.; Reinders, Marcel J. T.; Hendriks, Emile A.

    2009-02-01

    Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of data-driven ASLR methods has shortcomings which may not be solvable without the use of knowledge on human sign language processing. Handling variation in the precise execution of signs is an example of such shortcomings: data-driven methods (which include almost all current methods) have difficulty recognizing signs that deviate too much from the examples that were used to train the method. Insight into human sign processing is needed to solve these problems. Perceptual research on sign language can provide such insights. This paper discusses knowledge derived from a set of sign perception experiments, and the application of such knowledge in ASLR. Among the findings are the facts that not all phases and elements of a sign are equally informative, that defining the 'correct' form for a sign is not trivial, and that statistical ASLR methods do not necessarily arrive at sign representations that resemble those of human beings. Apparently, current ASLR methods are quite different from human observers: their method of learning gives them different sign definitions, they regard each moment and element of a sign as equally important and they employ a single definition of 'correct' for all circumstances. If the object is for an ASLR method to handle natural sign language, then the insights from sign perception research must be integrated into ASLR.

  12. Writer adaptation in off-line Arabic handwriting recognition

    NASA Astrophysics Data System (ADS)

    Ball, Gregory R.; Srihari, Sargur N.

    2008-01-01

    Writer adaptation or specialization is the adjustment of handwriting recognition algorithms to a specific writer's style of handwriting. Such adjustment yields significantly improved recognition rates over counterpart general recognition algorithms. We present the first unconstrained off-line handwriting adaptation algorithm for Arabic presented in the literature. We discuss an iterative bootstrapping model which adapts a writer-independent model to a writer-dependent model using a small number of words achieving a large recognition rate increase in the process. Furthermore, we describe a confidence weighting method which generates better results by weighting words based on their length. We also discuss script features unique to Arabic, and how we incorporate them into our adaptation process. Even though Arabic has many more character classes than languages such as English, significant improvement was observed. The testing set consisting of about 100 pages of handwritten text had an initial average overall recognition rate of 67%. After the basic adaptation was finished, the overall recognition rate was 73.3%. As the improvement was most marked for the longer words, and the set of confidently recognized longer words contained many fewer false results, a second method was presented using them alone, resulting in a recognition rate of about 75%. Initially, these words had a 69.5% recognition rate, improving to about a 92% recognition rate after adaptation. A novel hybrid method is presented with a rate of about 77.2%.

  13. Automatic Speech Acquisition and Recognition for Spacesuit Audio Systems

    NASA Technical Reports Server (NTRS)

    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.

  14. Automatic speech recognition in air-ground data link

    NASA Technical Reports Server (NTRS)

    Armstrong, Herbert B.

    1989-01-01

    In the present air traffic system, information presented to the transport aircraft cockpit crew may originate from a variety of sources and may be presented to the crew in visual or aural form, either through cockpit instrument displays or, most often, through voice communication. Voice radio communications are the most error prone method for air-ground data link. Voice messages can be misstated or misunderstood and radio frequency congestion can delay or obscure important messages. To prevent proliferation, a multiplexed data link display can be designed to present information from multiple data link sources on a shared cockpit display unit (CDU) or multi-function display (MFD) or some future combination of flight management and data link information. An aural data link which incorporates an automatic speech recognition (ASR) system for crew response offers several advantages over visual displays. The possibility of applying ASR to the air-ground data link was investigated. The first step was to review current efforts in ASR applications in the cockpit and in air traffic control and evaluated their possible data line application. Next, a series of preliminary research questions is to be developed for possible future collaboration.

  15. Network patterns recognition for automatic dermatologic images classification

    NASA Astrophysics Data System (ADS)

    Grana, Costantino; Daniele, Vanini; Pellacani, Giovanni; Seidenari, Stefania; Cucchiara, Rita

    2007-03-01

    In this paper we focus on the problem of automatic classification of melanocytic lesions, aiming at identifying the presence of reticular patterns. The recognition of reticular lesions is an important step in the description of the pigmented network, in order to obtain meaningful diagnostic information. Parameters like color, size or symmetry could benefit from the knowledge of having a reticular or non-reticular lesion. The detection of network patterns is performed with a three-steps procedure. The first step is the localization of line points, by means of the line points detection algorithm, firstly described by Steger. The second step is the linking of such points into a line considering the direction of the line at its endpoints and the number of line points connected to these. Finally a third step discards the meshes which couldn't be closed at the end of the linking procedure and the ones characterized by anomalous values of area or circularity. The number of the valid meshes left and their area with respect to the whole area of the lesion are the inputs of a discriminant function which classifies the lesions into reticular and non-reticular. This approach was tested on two balanced (both sets are formed by 50 reticular and 50 non-reticular images) training and testing sets. We obtained above 86% correct classification of the reticular and non-reticular lesions on real skin images, with a specificity value never lower than 92%.

  16. Semi-automatic recognition of marine debris on beaches.

    PubMed

    Ge, Zhenpeng; Shi, Huahong; Mei, Xuefei; Dai, Zhijun; Li, Daoji

    2016-01-01

    An increasing amount of anthropogenic marine debris is pervading the earth's environmental systems, resulting in an enormous threat to living organisms. Additionally, the large amount of marine debris around the world has been investigated mostly through tedious manual methods. Therefore, we propose the use of a new technique, light detection and ranging (LIDAR), for the semi-automatic recognition of marine debris on a beach because of its substantially more efficient role in comparison with other more laborious methods. Our results revealed that LIDAR should be used for the classification of marine debris into plastic, paper, cloth and metal. Additionally, we reconstructed a 3-dimensional model of different types of debris on a beach with a high validity of debris revivification using LIDAR-based individual separation. These findings demonstrate that the availability of this new technique enables detailed observations to be made of debris on a large beach that was previously not possible. It is strongly suggested that LIDAR could be implemented as an appropriate monitoring tool for marine debris by global researchers and governments.

  17. Semi-automatic recognition of marine debris on beaches

    PubMed Central

    Ge, Zhenpeng; Shi, Huahong; Mei, Xuefei; Dai, Zhijun; Li, Daoji

    2016-01-01

    An increasing amount of anthropogenic marine debris is pervading the earth’s environmental systems, resulting in an enormous threat to living organisms. Additionally, the large amount of marine debris around the world has been investigated mostly through tedious manual methods. Therefore, we propose the use of a new technique, light detection and ranging (LIDAR), for the semi-automatic recognition of marine debris on a beach because of its substantially more efficient role in comparison with other more laborious methods. Our results revealed that LIDAR should be used for the classification of marine debris into plastic, paper, cloth and metal. Additionally, we reconstructed a 3-dimensional model of different types of debris on a beach with a high validity of debris revivification using LIDAR-based individual separation. These findings demonstrate that the availability of this new technique enables detailed observations to be made of debris on a large beach that was previously not possible. It is strongly suggested that LIDAR could be implemented as an appropriate monitoring tool for marine debris by global researchers and governments. PMID:27156433

  18. Semi-automatic recognition of marine debris on beaches

    NASA Astrophysics Data System (ADS)

    Ge, Zhenpeng; Shi, Huahong; Mei, Xuefei; Dai, Zhijun; Li, Daoji

    2016-05-01

    An increasing amount of anthropogenic marine debris is pervading the earth’s environmental systems, resulting in an enormous threat to living organisms. Additionally, the large amount of marine debris around the world has been investigated mostly through tedious manual methods. Therefore, we propose the use of a new technique, light detection and ranging (LIDAR), for the semi-automatic recognition of marine debris on a beach because of its substantially more efficient role in comparison with other more laborious methods. Our results revealed that LIDAR should be used for the classification of marine debris into plastic, paper, cloth and metal. Additionally, we reconstructed a 3-dimensional model of different types of debris on a beach with a high validity of debris revivification using LIDAR-based individual separation. These findings demonstrate that the availability of this new technique enables detailed observations to be made of debris on a large beach that was previously not possible. It is strongly suggested that LIDAR could be implemented as an appropriate monitoring tool for marine debris by global researchers and governments.

  19. Assessing the performance of a covert automatic target recognition algorithm

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.; Lanterman, Aaron D.

    2005-05-01

    Passive radar systems exploit illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. Doing so allows them to operate covertly and inexpensively. Our research seeks to enhance passive radar systems by adding automatic target recognition (ATR) capabilities. In previous papers we proposed conducting ATR by comparing the radar cross section (RCS) of aircraft detected by a passive radar system to the precomputed RCS of aircraft in the target class. To effectively model the low-frequency setting, the comparison is made via a Rician likelihood model. Monte Carlo simulations indicate that the approach is viable. This paper builds on that work by developing a method for quickly assessing the potential performance of the ATR algorithm without using exhaustive Monte Carlo trials. This method exploits the relation between the probability of error in a binary hypothesis test under the Bayesian framework to the Chernoff information. Since the data are well-modeled as Rician, we begin by deriving a closed-form approximation for the Chernoff information between two Rician densities. This leads to an approximation for the probability of error in the classification algorithm that is a function of the number of available measurements. We conclude with an application that would be particularly cumbersome to accomplish via Monte Carlo trials, but that can be quickly addressed using the Chernoff information approach. This application evaluates the length of time that an aircraft must be tracked before the probability of error in the ATR algorithm drops below a desired threshold.

  20. Semi-automatic recognition of marine debris on beaches.

    PubMed

    Ge, Zhenpeng; Shi, Huahong; Mei, Xuefei; Dai, Zhijun; Li, Daoji

    2016-01-01

    An increasing amount of anthropogenic marine debris is pervading the earth's environmental systems, resulting in an enormous threat to living organisms. Additionally, the large amount of marine debris around the world has been investigated mostly through tedious manual methods. Therefore, we propose the use of a new technique, light detection and ranging (LIDAR), for the semi-automatic recognition of marine debris on a beach because of its substantially more efficient role in comparison with other more laborious methods. Our results revealed that LIDAR should be used for the classification of marine debris into plastic, paper, cloth and metal. Additionally, we reconstructed a 3-dimensional model of different types of debris on a beach with a high validity of debris revivification using LIDAR-based individual separation. These findings demonstrate that the availability of this new technique enables detailed observations to be made of debris on a large beach that was previously not possible. It is strongly suggested that LIDAR could be implemented as an appropriate monitoring tool for marine debris by global researchers and governments. PMID:27156433

  1. Automatic recognition of facial movement for paralyzed face.

    PubMed

    Wang, Ting; Dong, Junyu; Sun, Xin; Zhang, Shu; Wang, Shengke

    2014-01-01

    Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate that the proposed method is efficient for both paralyzed and normal faces.

  2. Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

    NASA Technical Reports Server (NTRS)

    Tescher, Andrew G. (Editor)

    1989-01-01

    Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

  3. Irregular and adaptive sampling for automatic geophysic measure systems

    NASA Astrophysics Data System (ADS)

    Avagnina, Davide; Lo Presti, Letizia; Mulassano, Paolo

    2000-07-01

    In this paper a sampling method, based on an irregular and adaptive strategy, is described. It can be used as automatic guide for rovers designed to explore terrestrial and planetary environments. Starting from the hypothesis that a explorative vehicle is equipped with a payload able to acquire measurements of interesting quantities, the method is able to detect objects of interest from measured points and to realize an adaptive sampling, while badly describing the not interesting background.

  4. Adaptive Neural Networks for Automatic Negotiation

    SciTech Connect

    Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

    2007-12-26

    The use of fuzzy logic and fuzzy neural networks has been found effective for the modelling of the uncertain relations between the parameters of a negotiation procedure. The problem with these configurations is that they are static, that is, any new knowledge from theory or experiment lead to the construction of entirely new models. To overcome this difficulty, we apply in this work, an adaptive neural topology to model the negotiation process. Finally a simple simulation is carried in order to test the new method.

  5. Mispronunciation Detection for Language Learning and Speech Recognition Adaptation

    ERIC Educational Resources Information Center

    Ge, Zhenhao

    2013-01-01

    The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.…

  6. A Limited-Vocabulary, Multi-Speaker Automatic Isolated Word Recognition System.

    ERIC Educational Resources Information Center

    Paul, James E., Jr.

    Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation,…

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

    NASA Astrophysics Data System (ADS)

    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.

  8. Why Don't You See What I Mean? Prospects and Limitations of Current Automatic Sign Recognition Research

    ERIC Educational Resources Information Center

    ten Holt, Gineke; Hendriks, Petra; Andriga, Tjeerd

    2006-01-01

    This article presents an overview of current automatic sign recognition research. A review of recent studies, as well as on our own research, has identified several problem areas that hamper successful sign recognition by a computer. Some of these problems are shared with automatic speech recognition, whereas others seem to be unique to automatic…

  9. [Automated recognition of quasars based on adaptive radial basis function neural networks].

    PubMed

    Zhao, Mei-Fang; Luo, A-Li; Wu, Fu-Chao; Hu, Zhan-Yi

    2006-02-01

    Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency.

  10. Multi-Stage System for Automatic Target Recognition

    NASA Technical Reports Server (NTRS)

    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

  11. A distributed automatic target recognition system using multiple low resolution sensors

    NASA Astrophysics Data System (ADS)

    Yue, Zhanfeng; Lakshmi Narasimha, Pramod; Topiwala, Pankaj

    2008-04-01

    In this paper, we propose a multi-agent system which uses swarming techniques to perform high accuracy Automatic Target Recognition (ATR) in a distributed manner. The proposed system can co-operatively share the information from low-resolution images of different looks and use this information to perform high accuracy ATR. An advanced, multiple-agent Unmanned Aerial Vehicle (UAV) systems-based approach is proposed which integrates the processing capabilities, combines detection reporting with live video exchange, and swarm behavior modalities that dramatically surpass individual sensor system performance levels. We employ real-time block-based motion analysis and compensation scheme for efficient estimation and correction of camera jitter, global motion of the camera/scene and the effects of atmospheric turbulence. Our optimized Partition Weighted Sum (PWS) approach requires only bitshifts and additions, yet achieves a stunning 16X pixel resolution enhancement, which is moreover parallizable. We develop advanced, adaptive particle-filtering based algorithms to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. The collaborative ATR system utilizes the homographies between the sensors induced by the ground plane to overlap the local observation with the received images from other UAVs. The motion of the UAVs distorts estimated homography frame to frame. A robust dynamic homography estimation algorithm is proposed to address this, by using the homography decomposition and the ground plane surface estimation.

  12. Research into automatic recognition of joints in human symmetrical movements

    NASA Astrophysics Data System (ADS)

    Fan, Yifang; Li, Zhiyu

    2008-03-01

    High speed photography is a major means of collecting data from human body movement. It enables the automatic identification of joints, which brings great significance to the research, treatment and recovery of injuries, the analysis to the diagnosis of sport techniques and the ergonomics. According to the features that when the adjacent joints of human body are in planetary motion, their distance remains the same, and according to the human body joint movement laws (such as the territory of the articular anatomy and the kinematic features), a new approach is introduced to process the image thresholding of joints filmed by the high speed camera, to automatically identify the joints and to automatically trace the joint points (by labeling markers at the joints). Based upon the closure of marking points, automatic identification can be achieved through thresholding treatment. Due to the screening frequency and the laws of human segment movement, when the marking points have been initialized, their automatic tracking can be achieved with the progressive sequential images.Then the testing results, the data from three-dimensional force platform and the characteristics that human body segment will only rotate around the closer ending segment when the segment has no boding force and only valid to the conservative force all tell that after being analyzed kinematically, the approach is approved to be valid.

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

    NASA Technical Reports Server (NTRS)

    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.

  14. Classification of EEG for Affect Recognition: An Adaptive Approach

    NASA Astrophysics Data System (ADS)

    Alzoubi, Omar; Calvo, Rafael A.; Stevens, Ronald H.

    Research on affective computing is growing rapidly and new applications are being developed more frequently. They use information about the affective/mental states of users to adapt their interfaces or add new functionalities. Face activity, voice, text physiology and other information about the user are used as input to affect recognition modules, which are built as classification algorithms. Brain EEG signals have rarely been used to build such classifiers due to the lack of a clear theoretical framework. We present here an evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment. Our results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.

  15. Phase coherence adaptive processor for automatic signal detection and identification

    NASA Astrophysics Data System (ADS)

    Wagstaff, Ronald A.

    2006-05-01

    A continuously adapting acoustic signal processor with an automatic detection/decision aid is presented. Its purpose is to preserve the signals of tactical interest, and filter out other signals and noise. It utilizes single sensor or beamformed spectral data and transforms the signal and noise phase angles into "aligned phase angles" (APA). The APA increase the phase temporal coherence of signals and leave the noise incoherent. Coherence thresholds are set, which are representative of the type of source "threat vehicle" and the geographic area or volume in which it is operating. These thresholds separate signals, based on the "quality" of their APA coherence. An example is presented in which signals from a submerged source in the ocean are preserved, while clutter signals from ships and noise are entirely eliminated. Furthermore, the "signals of interest" were identified by the processor's automatic detection aid. Similar performance is expected for air and ground vehicles. The processor's equations are formulated in such a manner that they can be tuned to eliminate noise and exploit signal, based on the "quality" of their APA temporal coherence. The mathematical formulation for this processor is presented, including the method by which the processor continuously self-adapts. Results show nearly complete elimination of noise, with only the selected category of signals remaining, and accompanying enhancements in spectral and spatial resolution. In most cases, the concept of signal-to-noise ratio looses significance, and "adaptive automated /decision aid" is more relevant.

  16. Improved Techniques for Automatic Chord Recognition from Music Audio Signals

    ERIC Educational Resources Information Center

    Cho, Taemin

    2014-01-01

    This thesis is concerned with the development of techniques that facilitate the effective implementation of capable automatic chord transcription from music audio signals. Since chord transcriptions can capture many important aspects of music, they are useful for a wide variety of music applications and also useful for people who learn and perform…

  17. Assessing Children's Home Language Environments Using Automatic Speech Recognition Technology

    ERIC Educational Resources Information Center

    Greenwood, Charles R.; Thiemann-Bourque, Kathy; Walker, Dale; Buzhardt, Jay; Gilkerson, Jill

    2011-01-01

    The purpose of this research was to replicate and extend some of the findings of Hart and Risley using automatic speech processing instead of human transcription of language samples. The long-term goal of this work is to make the current approach to speech processing possible by researchers and clinicians working on a daily basis with families and…

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  19. Automatic recognition system of aquatic organisms by classical and fluorescence microscopy

    NASA Astrophysics Data System (ADS)

    Lauffer, M.; Genty, F.; Margueron, S.; Collette, J. L.; Pihan, J. C.

    2015-05-01

    Blooming of algae and more generally phytoplankton in water ponds or marine environments can lead to hyper eutrophication and lethal consequences on other organisms. The selective recognition of invading species is investigated by automatic recognition algorithms of optical and fluorescence imaging. On one hand, morphological characteristics of algae of microscopic imaging are treated. The image processing lead to the identification the genus of aquatic organisms and compared to a morphologic data base. On the other hand, fluorescence images allow an automatic recognition based on multispectral data that identify locally the ratio of different photosynthetic pigments and gives a unique finger print of algae. It is shown that the combination of both methods are useful in the recognition of aquatic organisms.

  20. Automatic speech recognition research at NASA-Ames Research Center

    NASA Technical Reports Server (NTRS)

    Coler, Clayton R.; Plummer, Robert P.; Huff, Edward M.; Hitchcock, Myron H.

    1977-01-01

    A trainable acoustic pattern recognizer manufactured by Scope Electronics is presented. The voice command system VCS encodes speech by sampling 16 bandpass filters with center frequencies in the range from 200 to 5000 Hz. Variations in speaking rate are compensated for by a compression algorithm that subdivides each utterance into eight subintervals in such a way that the amount of spectral change within each subinterval is the same. The recorded filter values within each subinterval are then reduced to a 15-bit representation, giving a 120-bit encoding for each utterance. The VCS incorporates a simple recognition algorithm that utilizes five training samples of each word in a vocabulary of up to 24 words. The recognition rate of approximately 85 percent correct for untrained speakers and 94 percent correct for trained speakers was not considered adequate for flight systems use. Therefore, the built-in recognition algorithm was disabled, and the VCS was modified to transmit 120-bit encodings to an external computer for recognition.

  1. Studies in automatic speech recognition and its application in aerospace

    NASA Astrophysics Data System (ADS)

    Taylor, Michael Robinson

    Human communication is characterized in terms of the spectral and temporal dimensions of speech waveforms. Electronic speech recognition strategies based on Dynamic Time Warping and Markov Model algorithms are described and typical digit recognition error rates are tabulated. The application of Direct Voice Input (DVI) as an interface between man and machine is explored within the context of civil and military aerospace programmes. Sources of physical and emotional stress affecting speech production within military high performance aircraft are identified. Experimental results are reported which quantify fundamental frequency and coarse temporal dimensions of male speech as a function of the vibration, linear acceleration and noise levels typical of aerospace environments; preliminary indications of acoustic phonetic variability reported by other researchers are summarized. Connected whole-word pattern recognition error rates are presented for digits spoken under controlled Gz sinusoidal whole-body vibration. Correlations are made between significant increases in recognition error rate and resonance of the abdomen-thorax and head subsystems of the body. The phenomenon of vibrato style speech produced under low frequency whole-body Gz vibration is also examined. Interactive DVI system architectures and avionic data bus integration concepts are outlined together with design procedures for the efficient development of pilot-vehicle command and control protocols.

  2. Spectral saliency via automatic adaptive amplitude spectrum analysis

    NASA Astrophysics Data System (ADS)

    Wang, Xiaodong; Dai, Jialun; Zhu, Yafei; Zheng, Haiyong; Qiao, Xiaoyan

    2016-03-01

    Suppressing nonsalient patterns by smoothing the amplitude spectrum at an appropriate scale has been shown to effectively detect the visual saliency in the frequency domain. Different filter scales are required for different types of salient objects. We observe that the optimal scale for smoothing amplitude spectrum shares a specific relation with the size of the salient region. Based on this observation and the bottom-up saliency detection characterized by spectrum scale-space analysis for natural images, we propose to detect visual saliency, especially with salient objects of different sizes and locations via automatic adaptive amplitude spectrum analysis. We not only provide a new criterion for automatic optimal scale selection but also reserve the saliency maps corresponding to different salient objects with meaningful saliency information by adaptive weighted combination. The performance of quantitative and qualitative comparisons is evaluated by three different kinds of metrics on the four most widely used datasets and one up-to-date large-scale dataset. The experimental results validate that our method outperforms the existing state-of-the-art saliency models for predicting human eye fixations in terms of accuracy and robustness.

  3. Automatic facial expression recognition based on features extracted from tracking of facial landmarks

    NASA Astrophysics Data System (ADS)

    Ghimire, Deepak; Lee, Joonwhoan

    2014-01-01

    In this paper, we present a fully automatic facial expression recognition system using support vector machines, with geometric features extracted from the tracking of facial landmarks. Facial landmark initialization and tracking is performed by using an elastic bunch graph matching algorithm. The facial expression recognition is performed based on the features extracted from the tracking of not only individual landmarks, but also pair of landmarks. The recognition accuracy on the Extended Kohn-Kanade (CK+) database shows that our proposed set of features produces better results, because it utilizes time-varying graph information, as well as the motion of individual facial landmarks.

  4. Using Automatic Speech Recognition to Dictate Mathematical Expressions: The Development of the "TalkMaths" Application at Kingston University

    ERIC Educational Resources Information Center

    Wigmore, Angela; Hunter, Gordon; Pflugel, Eckhard; Denholm-Price, James; Binelli, Vincent

    2009-01-01

    Speech technology--especially automatic speech recognition--has now advanced to a level where it can be of great benefit both to able-bodied people and those with various disabilities. In this paper we describe an application "TalkMaths" which, using the output from a commonly-used conventional automatic speech recognition system, enables the user…

  5. Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

    ERIC Educational Resources Information Center

    van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

    2016-01-01

    The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

  6. Speech Recognition-based and Automaticity Programs to Help Students with Severe Reading and Spelling Problems

    ERIC Educational Resources Information Center

    Higgins, Eleanor L.; Raskind, Marshall H.

    2004-01-01

    This study was conducted to assess the effectiveness of two programs developed by the Frostig Center Research Department to improve the reading and spelling of students with learning disabilities (LD): a computer Speech Recognition-based Program (SRBP) and a computer and text-based Automaticity Program (AP). Twenty-eight LD students with reading…

  7. Developing and Evaluating an Oral Skills Training Website Supported by Automatic Speech Recognition Technology

    ERIC Educational Resources Information Center

    Chen, Howard Hao-Jan

    2011-01-01

    Oral communication ability has become increasingly important to many EFL students. Several commercial software programs based on automatic speech recognition (ASR) technologies are available but their prices are not affordable for many students. This paper will demonstrate how the Microsoft Speech Application Software Development Kit (SASDK), a…

  8. Is Handedness Recognition Automatic?: A Study Using a Simon-Like Paradigm

    ERIC Educational Resources Information Center

    Ottoboni, Giovanni; Tessari, Alessia; Cubelli, Roberto; Umilta, Carlo

    2005-01-01

    The authors used a modified Simon task (J. R. Simon, 1969) to assess the automatic recognition of handedness. Participants responded to the color of a circle in the center of the photograph of a right or a left hand, displayed in the center of the computer screen. A regular Simon effect was found for back views, whereas a reverse Simon effect was…

  9. Errors in Automatic Speech Recognition versus Difficulties in Second Language Listening

    ERIC Educational Resources Information Center

    Mirzaei, Maryam Sadat; Meshgi, Kourosh; Akita, Yuya; Kawahara, Tatsuya

    2015-01-01

    Automatic Speech Recognition (ASR) technology has become a part of contemporary Computer-Assisted Language Learning (CALL) systems. ASR systems however are being criticized for their erroneous performance especially when utilized as a mean to develop skills in a Second Language (L2) where errors are not tolerated. Nevertheless, these errors can…

  10. Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Blasch, Erik; Seetharaman, Guna; Darema, Frederica

    2013-05-01

    The Dynamic Data Driven Applications System (DDDAS) concept uses applications modeling, mathematical algorithms, and measurement systems to work with dynamic systems. A dynamic systems such as Automatic Target Recognition (ATR) is subject to sensor, target, and the environment variations over space and time. We use the DDDAS concept to develop an ATR methodology for multiscale-multimodal analysis that seeks to integrated sensing, processing, and exploitation. In the analysis, we use computer vision techniques to explore the capabilities and analogies that DDDAS has with information fusion. The key attribute of coordination is the use of sensor management as a data driven techniques to improve performance. In addition, DDDAS supports the need for modeling from which uncertainty and variations are used within the dynamic models for advanced performance. As an example, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between ATR and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements - an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computations remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and ATR.

  11. Adding articulatory features to acoustic features for automatic speech recognition

    SciTech Connect

    Zlokarnik, I.

    1995-05-01

    A hidden-Markov-model (HMM) based speech recognition system was evaluated that makes use of simultaneously recorded acoustic and articulatory data. The articulatory measurements were gathered by means of electromagnetic articulography and describe the movement of small coils fixed to the speakers` tongue and jaw during the production of German V{sub 1}CV{sub 2} sequences [P. Hoole and S. Gfoerer, J. Acoust. Soc. Am. Suppl. 1 {bold 87}, S123 (1990)]. Using the coordinates of the coil positions as an articulatory representation, acoustic and articulatory features were combined to make up an acoustic--articulatory feature vector. The discriminant power of this combined representation was evaluated for two subjects on a speaker-dependent isolated word recognition task. When the articulatory measurements were used both for training and testing the HMMs, the articulatory representation was capable of reducing the error rate of comparable acoustic-based HMMs by a relative percentage of more than 60%. In a separate experiment, the articulatory movements during the testing phase were estimated using a multilayer perceptron that performed an acoustic-to-articulatory mapping. Under these more realistic conditions, when articulatory measurements are only available during the training, the error rate could be reduced by a relative percentage of 18% to 25%.

  12. Automatic music genres classification as a pattern recognition problem

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  13. Automatic recognition and measurement of butterfly eyespot patterns.

    PubMed

    Silveira, Margarida; Monteiro, Antónia

    2009-02-01

    A favorite wing pattern element in butterflies that has been the focus of intense study in evolutionary and developmental biology, as well as in behavioral ecology, is the eyespot. Because the pace of research on these bull's eye patterns is accelerating we sought to develop a tool to automatically detect and measure butterfly eyespot patterns in digital images of the wings. We used a machine learning algorithm with features based on circularity and symmetry to detect eyespots on the images. The algorithm is first trained with examples from a database of images with two different labels (eyespot and non-eyespot), and subsequently is able to provide classification for a new image. After an eyespot is detected the radius measurements of its color rings are performed by a 1D Hough Transform which corresponds to histogramming. We trained software to recognize eyespot patterns of the nymphalid butterfly Bicyclus anynana but eyespots of other butterfly species were also successfully detected by the software.

  14. Visual object recognition for automatic micropropagation of plants

    NASA Astrophysics Data System (ADS)

    Brendel, Thorsten; Schwanke, Joerg; Jensch, Peter F.

    1994-11-01

    Micropropagation of plants is done by cutting juvenile plants and placing them into special container-boxes with nutrient-solution where the pieces can grow up and be cut again several times. To produce high amounts of biomass it is necessary to do plant micropropagation by a robotic system. In this paper we describe parts of the vision system that recognizes plants and their particular cutting points. Therefore, it is necessary to extract elements of the plants and relations between these elements (for example root, stem, leaf). Different species vary in their morphological appearance, variation is also immanent in plants of the same species. Therefore, we introduce several morphological classes of plants from that we expect same recognition methods.

  15. Automatic air-to-ground target recognition using LWIR FPAs

    NASA Astrophysics Data System (ADS)

    Amadieu, Jean-Louis; Fraysse, Vincent

    1996-06-01

    The theoretical potential of optical sensors in terms of geometrical resolution makes them the ideal solution for achieving the terminal precision guidance of today's missiles. This paper describes such a sensor, working in the 8 to 12 micrometer spectral domain by using a 64 by 64 IRCCD focal plane array, and whose main mission is to recognize various types of armored vehicles within complex scenes that possibly include other vehicles of similar nature. The target recognition process is based upon a Bayesian approach and can be briefly described as follows: after a classical processing stage that performs the filtering and the multi- thresholding, the target recognition algorithm evaluates a similarity level between the objects, including the target, seen in the IR scene and the 'theoretical' target whose some mean, generic features have been implemented in a database. The surroundings of the target and its orientation in the IR scene are 'a priori' unknown. The similarity level is based on calculation of the Mahalanobis distance between the object features vector and the mean features vector of the model; this calculation involves a covariance matrix which is significant of the errors affecting the measured features and that in particular stem form the limited spatial resolution of the sensor, the detector noise and the sensor- to-target range estimation error. With respect to the sensor hardware, its main opto-mechanical characteristics as well as some electro-optics data are indicates; some examples of target acquisition in complex scenes involving different kinds of IR counter measures are also presented.

  16. A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

    NASA Astrophysics Data System (ADS)

    Oh, Yoo Rhee; Kim, Hong Kook

    In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

  17. Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control.

    PubMed

    Liu, Jie

    2015-04-01

    The non-stationary property of electromyography (EMG) signals in real life settings usually hinders the clinical application of the myoelectric pattern recognition for prosthesis control. The classical EMG pattern recognition approach consists of two separate steps: training and testing, without considering the changes between training and testing data induced by electrode shift, fatigue, impedance changes and psychological factors, and often results in performance degradation. The aim of this study was to develop an adaptive myoelectric pattern recognition system, aiming to retrain the classifier online with the testing data without supervision, providing a self-correction mechanism for suppressing misclassifications. This paper presents an adaptive unsupervised classifier based on support vector machine (SVM) to improve the classification performance. Experimental data from 15 healthy subjects were used to evaluate performance. Preliminary study on intra-session and inter-session EMG data was conducted to verify the performance of the unsupervised adaptive SVM classifier. The unsupervised adaptive SVM classifier outperformed the conventional SVM by 3.3% and 8.0% for the combination of time-domain and autoregressive features in the intra-session and inter-session tests, respectively. The proposed approach is capable of incorporating the useful information in testing data to the classification model by taking into account the overtime changes in the testing data with respect to the training data to retrain the original classifier, therefore providing a self-correction mechanism for suppressing misclassifications.

  18. An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

    PubMed

    Li, Zhen; Wei, Zhiqiang; Yue, Yaofeng; Wang, Hao; Jia, Wenyan; Burke, Lora E; Baranowski, Thomas; Sun, Mingui

    2015-05-01

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

  19. Automatic recognition and measurement of butterfly eyespot patterns.

    PubMed

    Silveira, Margarida; Monteiro, Antónia

    2009-02-01

    A favorite wing pattern element in butterflies that has been the focus of intense study in evolutionary and developmental biology, as well as in behavioral ecology, is the eyespot. Because the pace of research on these bull's eye patterns is accelerating we sought to develop a tool to automatically detect and measure butterfly eyespot patterns in digital images of the wings. We used a machine learning algorithm with features based on circularity and symmetry to detect eyespots on the images. The algorithm is first trained with examples from a database of images with two different labels (eyespot and non-eyespot), and subsequently is able to provide classification for a new image. After an eyespot is detected the radius measurements of its color rings are performed by a 1D Hough Transform which corresponds to histogramming. We trained software to recognize eyespot patterns of the nymphalid butterfly Bicyclus anynana but eyespots of other butterfly species were also successfully detected by the software. PMID:18955106

  20. EEG Responses to Auditory Stimuli for Automatic Affect Recognition

    PubMed Central

    Hettich, Dirk T.; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

    2016-01-01

    Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

  1. EEG Responses to Auditory Stimuli for Automatic Affect Recognition.

    PubMed

    Hettich, Dirk T; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

    2016-01-01

    Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

  2. Novel automatic target recognition approach for multispectral data

    NASA Astrophysics Data System (ADS)

    Salazar, Jose S.; Koch, Mark W.; Yocky, David A.

    2002-11-01

    Automating the detection and identification of significant threats using multispectral (MS) imagery is a critical issue in remote sensing. Unlike previous multispectral target recognition approaches, we utilize a three-stage process that not only takes into account the spectral content, but also the spatial information. The first stage applies a matched filter to the calibrated MS data. Here, the matched filter is tuned to the spectral components of a given target and produces an image intensity map of where the best matches occur. The second stage represents a novel detection algorithm, known as the focus of attention (FOA) stage. The FOA performs an initial screening of the data based on intensity and size checks on the matched filter output. Next, using the target's pure components, the third stage performs constrained linear unmixing on MS pixels within the FOA detected regions. Knowledge sources derived from this process are combined using a sequential probability ratio test (SPRT). The SPRT can fuse contaminated, uncertain and disparate information from multiple sources. We demonstrate our approach on identifying a specific target using actual data collected in ideal conditions and also use approximately 35 square kilometers of urban clutter as false alarm data.

  3. A method of automatic recognition of airport in complex environment from remote sensing image

    NASA Astrophysics Data System (ADS)

    Hao, Qiwei; Ni, Guoqiang; Guo, Pan; Chen, Xiaomei; Tang, Yi

    2009-11-01

    In this paper, a new method is proposed for airport recognition in complex environments. The algorithm takes all advantages of essential characteristics of the airport target. Structural characteristics of the airport are used to establish assumption process. Improved Hough transformation (HT) is used to check out those right straight-lines which stand for actual position and direction of runways. Morphological processing is used to remove road segments and isolated points. Finally, we combine these segments carefully to describe the whole airport area, and then our automatic recognition of airport target is realized.

  4. Kernel sparse coding method for automatic target recognition in infrared imagery using covariance descriptor

    NASA Astrophysics Data System (ADS)

    Yang, Chunwei; Yao, Junping; Sun, Dawei; Wang, Shicheng; Liu, Huaping

    2016-05-01

    Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descriptor combining gray intensity and gradient information of the infrared target is extracted as a feature representation. Then, due to the reason that covariance descriptor lies in non-Euclidean manifold, kernel sparse coding theory is used to solve this problem. We verify the efficacy of the proposed algorithm in terms of the confusion matrices on the real images consisting of seven categories of infrared vehicle targets.

  5. Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

    PubMed

    Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

    2015-12-01

    The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types. PMID:26723348

  6. Early Detection of Severe Apnoea through Voice Analysis and Automatic Speaker Recognition Techniques

    NASA Astrophysics Data System (ADS)

    Fernández, Ruben; Blanco, Jose Luis; Díaz, David; Hernández, Luis A.; López, Eduardo; Alcázar, José

    This study is part of an on-going collaborative effort between the medical and the signal processing communities to promote research on applying voice analysis and Automatic Speaker Recognition techniques (ASR) for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based diagnosis could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we present and discuss the possibilities of using generative Gaussian Mixture Models (GMMs), generally used in ASR systems, to model distinctive apnoea voice characteristics (i.e. abnormal nasalization). Finally, we present experimental findings regarding the discriminative power of speaker recognition techniques applied to severe apnoea detection. We have achieved an 81.25 % correct classification rate, which is very promising and underpins the interest in this line of inquiry.

  7. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

    NASA Astrophysics Data System (ADS)

    Fernández Pozo, Rubén; Blanco Murillo, Jose Luis; Hernández Gómez, Luis; López Gonzalo, Eduardo; Alcázar Ramírez, José; Toledano, Doroteo T.

    2009-12-01

    This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

  8. Studies on quantitative analysis and automatic recognition of cell types of lung cancer.

    PubMed

    Chen, Yi-Chen; Hu, Kuang-Hu; Li, Fang-Zhen; Li, Shu-Yu; Su, Wan-Fang; Huang, Zhi-Ying; Hu, Ying-Xiong

    2006-01-01

    Recognition of lung cancer cells is very important to the clinical diagnosis of lung cancer. In this paper we present a novel method to extract the structure characteristics of lung cancer cells and automatically recognize their types. Firstly soft mathematical morphology methods are used to enhance the grayscale image, to improve the definition of images, and to eliminate most of disturbance, noise and information of subordinate images, so the contour of target lung cancer cell and biological shape characteristic parameters can be extracted accurately. Then the minimum distance classifier is introduced to realize the automatic recognition of different types of lung cancer cells. A software system named "CANCER.LUNG" is established to demonstrate the efficiency of this method. The clinical experiments show that this method can accurately and objectively recognize the type of lung cancer cells, which can significantly improve the pathology research on the pathological changes of lung cancer and clinical assistant diagnoses.

  9. Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector.

    PubMed

    Lei, Baiying; Tan, Ee-Leng; Chen, Siping; Zhuo, Liu; Li, Shengli; Ni, Dong; Wang, Tianfu

    2015-01-01

    Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.

  10. Stable Odor Recognition by a neuro-adaptive Electronic Nose

    NASA Astrophysics Data System (ADS)

    Martinelli, Eugenio; Magna, Gabriele; Polese, Davide; Vergara, Alexander; Schild, Detlev; di Natale, Corrado

    2015-06-01

    Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors’ instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds’ identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all.

  11. Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

    NASA Astrophysics Data System (ADS)

    Liu, Yu; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Guo, Shuxu; Attor, Rosemary; Reinicke, Danica; Torigian, Drew A.

    2016-03-01

    Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used -- optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.

  12. Field testing of a 3D automatic target recognition and pose estimation algorithm

    NASA Astrophysics Data System (ADS)

    Ruel, Stephane; English, Chad E.; Melo, Len; Berube, Andrew; Aikman, Doug; Deslauriers, Adam M.; Church, Philip M.; Maheux, Jean

    2004-09-01

    Neptec Design Group Ltd. has developed a 3D Automatic Target Recognition (ATR) and pose estimation technology demonstrator in partnership with the Canadian DND. The system prototype was deployed for field testing at Defence Research and Development Canada (DRDC)-Valcartier. This paper discusses the performance of the developed algorithm using 3D scans acquired with an imaging LIDAR. 3D models of civilian and military vehicles were built using scans acquired with a triangulation laser scanner. The models were then used to generate a knowledge base for the recognition algorithm. A commercial imaging LIDAR was used to acquire test scans of the target vehicles with varying range, pose and degree of occlusion. Recognition and pose estimation results are presented for at least 4 different poses of each vehicle at each test range. Results obtained with targets partially occluded by an artificial plane, vegetation and military camouflage netting are also presented. Finally, future operational considerations are discussed.

  13. Adaptive Activity and Environment Recognition for Mobile Phones

    PubMed Central

    Parviainen, Jussi; Bojja, Jayaprasad; Collin, Jussi; Leppänen, Jussi; Eronen, Antti

    2014-01-01

    In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy. PMID:25372620

  14. Adaptive activity and environment recognition for mobile phones.

    PubMed

    Parviainen, Jussi; Bojja, Jayaprasad; Collin, Jussi; Leppänen, Jussi; Eronen, Antti

    2014-11-03

    In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy.

  15. Speaker-Adaptive Speech Recognition Based on Surface Electromyography

    NASA Astrophysics Data System (ADS)

    Wand, Michael; Schultz, Tanja

    We present our recent advances in silent speech interfaces using electromyographic signals that capture the movements of the human articulatory muscles at the skin surface for recognizing continuously spoken speech. Previous systems were limited to speaker- and session-dependent recognition tasks on small amounts of training and test data. In this article we present speaker-independent and speaker-adaptive training methods which allow us to use a large corpus of data from many speakers to train acoustic models more reliably. We use the speaker-dependent system as baseline, carefully tuning the data preprocessing and acoustic modeling. Then on our corpus we compare the performance of speaker-dependent and speaker-independent acoustic models and carry out model adaptation experiments.

  16. Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences

    PubMed Central

    Okanoya, Kazuo

    2016-01-01

    Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep convolutional neural network and a hidden Markov model was effective. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization. PMID:27442240

  17. Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences.

    PubMed

    Koumura, Takuya; Okanoya, Kazuo

    2016-01-01

    Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep convolutional neural network and a hidden Markov model was effective. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization. PMID:27442240

  18. Automatic recognition of fundamental tissues on histology images of the human cardiovascular system.

    PubMed

    Mazo, Claudia; Trujillo, Maria; Alegre, Enrique; Salazar, Liliana

    2016-10-01

    Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition.

  19. The application of pattern recognition in the automatic classification of microscopic rock images

    NASA Astrophysics Data System (ADS)

    Młynarczuk, Mariusz; Górszczyk, Andrzej; Ślipek, Bartłomiej

    2013-10-01

    The classification of rocks is an inherent part of modern geology. The manual identification of rock samples is a time-consuming process, and-due to the subjective nature of human judgement-burdened with risk. In the course of the study discussed in the present paper, the authors investigated the possibility of automating this process. During the study, nine different rock samples were used. Their digital images were obtained from thin sections, with a polarizing microscope. These photographs were subsequently classified in an automatic manner, by means of four pattern recognition methods: the nearest neighbor algorithm, the K-nearest neighbor, the nearest mode algorithm, and the method of optimal spherical neighborhoods. The effectiveness of these methods was tested in four different color spaces: RGB, CIELab, YIQ, and HSV. The results of the study show that the automatic recognition of the discussed rock types is possible. The study also revealed that, if the CIELab color space and the nearest neighbor classification method are used, the rock samples in question are classified correctly, with the recognition levels of 99.8%.

  20. Automatic track recognition for large-angle minimum ionizing particles in nuclear emulsions

    NASA Astrophysics Data System (ADS)

    Fukuda, T.; Fukunaga, S.; Ishida, H.; Matsumoto, T.; Matsuo, T.; Mikado, S.; Nishimura, S.; Ogawa, S.; Shibuya, H.; Sudou, J.; Ariga, A.; Tufanli, S.

    2014-12-01

    We previously developed an automatic track scanning system which enables the detection of large-angle nuclear fragments in the nuclear emulsion films of the OPERA experiment. As a next step, we have investigated this system's track recognition capability for large-angle minimum ionizing particles (1.0 <= |tan θ| <= 3.5). This paper shows that, for such tracks, the system has a detection efficiency of 95% or higher and reports the achieved angular accuracy of the automatically recognized tracks. This technology is of general purpose and will likely contribute not only to various analyses in the OPERA experiment, but also to future experiments, e.g. on low-energy neutrino and hadron interactions, or to future research on cosmic rays using nuclear emulsions carried by balloons.

  1. Assessing the impact of graphical quality on automatic text recognition in digital maps

    NASA Astrophysics Data System (ADS)

    Chiang, Yao-Yi; Leyk, Stefan; Honarvar Nazari, Narges; Moghaddam, Sima; Tan, Tian Xiang

    2016-08-01

    Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

  2. Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition.

    PubMed

    Li, Yun; Chen, Xiang; Zhang, Xu; Wang, Kongqiao; Yang, Jihai

    2011-01-01

    The identification of constituent components of each sign gesture is a practical way of establishing large-vocabulary sign language recognition (SLR) system. Aiming at developing such a system using portable accelerometer (ACC) and surface electromyographic (sEMG) sensors, this work proposes a method for automatic SLR at the component level. The preliminary experimental results demonstrate the effectiveness of the proposed method and the feasibility of interpreting sign components from ACC and sEMG data. Our study improves the performance of SLR based on ACC and sEMG sensors and will promote the realization of a large-vocabulary portable SLR system. PMID:22255059

  3. Automatic Speech Recognition in Air Traffic Control: a Human Factors Perspective

    NASA Technical Reports Server (NTRS)

    Karlsson, Joakim

    1990-01-01

    The introduction of Automatic Speech Recognition (ASR) technology into the Air Traffic Control (ATC) system has the potential to improve overall safety and efficiency. However, because ASR technology is inherently a part of the man-machine interface between the user and the system, the human factors issues involved must be addressed. Here, some of the human factors problems are identified and related methods of investigation are presented. Research at M.I.T.'s Flight Transportation Laboratory is being conducted from a human factors perspective, focusing on intelligent parser design, presentation of feedback, error correction strategy design, and optimal choice of input modalities.

  4. Automatic target recognition algorithm based on statistical dispersion of infrared multispectral image

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Cao, Le-lin; Wu, Chun-feng; Hou, Qing-yu

    2009-07-01

    A novel automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI) is proposed. Firstly, infrared multispectral characteristic matrix of the scenario is constructed based on infrared multispectral characteristic information (such as radiation intensity and spectral distribution etc.) of targets, background and decoys. Then the infrared multispectral characteristic matrix of targets is reconstructed after segmenting image by maximum distance method and fusing spatial and spectral information. Finally, an statistical dispersion of infrared multispectral images(SDOIMI) recognition criteria is formulated in terms of spectral radiation difference of interesting targets. In simulation, nine sub-bands multispectral images of real ship target and shipborne aerosol infrared decoy modulated by laser simulating ship geometry appearance are obtained via using spectral radiation curves. Digital simulation experiment result verifies that the algorithm is effective and feasible.

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

    NASA Astrophysics Data System (ADS)

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

    1995-08-01

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

  6. Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition.

    PubMed

    Sariyanidi, Evangelos; Gunes, Hatice; Cavallaro, Andrea

    2015-06-01

    Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems. PMID:26357337

  7. Semi-automatic ground truth generation for license plate recognition system

    NASA Astrophysics Data System (ADS)

    Wang, Shen-Zheng; Zhao, San-Lung; Chen, Yi-Yuan; Lan, Kung-Ming

    2011-09-01

    License plate recognition (LPR) system is to help alert relevant personnel of any passing vehicle in the surveillance area. In order to test algorithms for license plate recognition, it is necessary to have input frames in which the ground truth is determined. The purpose of ground truth data is here to provide an absolute reference for performance evaluation or training purposes. However, annotating ground truth data for real-life inputs is very disturbing task because of timeconsuming manual. In this paper, we proposed a method of semi-automatic ground truth generation for license plate recognition in video sequences. The method started from region of interesting detection to rapidly extract characters lines followed by a license plate recognition system to verify the license plate regions and recognized the numbers. On the top of the LPR system, we incorporated a tracking-validation mechanism to detect the time interval of passing vehicles in input sequences. The tracking mechanism is initialized by a single license plate region in one frame. Moreover, in order to tolerate the variation of the license plate appearances in the input sequences, the validator would be updated by capturing positive and negatives samples during tracking. Experimental results show that the proposed method can achieve promising results.

  8. The role of binary mask patterns in automatic speech recognition in background noise

    PubMed Central

    Narayanan, Arun; Wang, DeLiang

    2013-01-01

    Processing noisy signals using the ideal binary mask improves automatic speech recognition (ASR) performance. This paper presents the first study that investigates the role of binary mask patterns in ASR under various noises, signal-to-noise ratios (SNRs), and vocabulary sizes. Binary masks are computed either by comparing the SNR within a time-frequency unit of a mixture signal with a local criterion (LC), or by comparing the local target energy with the long-term average spectral energy of speech. ASR results show that (1) akin to human speech recognition, binary masking significantly improves ASR performance even when the SNR is as low as −60 dB; (2) the ASR performance profiles are qualitatively similar to those obtained in human intelligibility experiments; (3) the difference between the LC and mixture SNR is more correlated to the recognition accuracy than LC; (4) LC at which the performance peaks is lower than 0 dB, which is the threshold that maximizes the SNR gain of processed signals. This broad agreement with human performance is rather surprising. The results also indicate that maximizing the SNR gain is probably not an appropriate goal for improving either human or machine recognition of noisy speech. PMID:23654411

  9. An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

  10. Conversion of the Haydn symphonies into electronic form using automatic score recognition: a pilot study

    NASA Astrophysics Data System (ADS)

    Carter, Nicholas P.

    1994-03-01

    As part of the development of an automatic recognition system for printed music scores, a series of `real-world' tasks are being undertaken. The first of these involves the production of a new edition of an existing 104-page, engraved, chamber-music score for Oxford University Press. The next substantial project, which is described here, has begun with a pilot study with a view to conversion of the 104 Haydn symphonies from a printed edition into machine- readable form. The score recognition system is based on a structural decomposition approach which provides advantages in terms of speed and tolerance of significant variations in font, scale, rotation and noise. Inevitably, some editing of the output data files is required, partially due to the limited vocabulary of symbols supported by the system and their permitted superimpositions. However, the possibility of automatically processing the bulk of the contents of over 600 pages of orchestral score in less than a day of compute time makes the conversion task manageable. The influence that this undertaking is having on the future direction of system development also is discussed.

  11. TrigNER: automatically optimized biomedical event trigger recognition on scientific documents

    PubMed Central

    2014-01-01

    Background Cellular events play a central role in the understanding of biological processes and functions, providing insight on both physiological and pathogenesis mechanisms. Automatic extraction of mentions of such events from the literature represents an important contribution to the progress of the biomedical domain, allowing faster updating of existing knowledge. The identification of trigger words indicating an event is a very important step in the event extraction pipeline, since the following task(s) rely on its output. This step presents various complex and unsolved challenges, namely the selection of informative features, the representation of the textual context, and the selection of a specific event type for a trigger word given this context. Results We propose TrigNER, a machine learning-based solution for biomedical event trigger recognition, which takes advantage of Conditional Random Fields (CRFs) with a high-end feature set, including linguistic-based, orthographic, morphological, local context and dependency parsing features. Additionally, a completely configurable algorithm is used to automatically optimize the feature set and training parameters for each event type. Thus, it automatically selects the features that have a positive contribution and automatically optimizes the CRF model order, n-grams sizes, vertex information and maximum hops for dependency parsing features. The final output consists of various CRF models, each one optimized to the linguistic characteristics of each event type. Conclusions TrigNER was tested in the BioNLP 2009 shared task corpus, achieving a total F-measure of 62.7 and outperforming existing solutions on various event trigger types, namely gene expression, transcription, protein catabolism, phosphorylation and binding. The proposed solution allows researchers to easily apply complex and optimized techniques in the recognition of biomedical event triggers, making its application a simple routine task. We believe

  12. Neural mechanisms of context effects on face recognition: automatic binding and context shift decrements.

    PubMed

    Hayes, Scott M; Baena, Elsa; Truong, Trong-Kha; Cabeza, Roberto

    2010-11-01

    Although people do not normally try to remember associations between faces and physical contexts, these associations are established automatically, as indicated by the difficulty of recognizing familiar faces in different contexts ("butcher-on-the-bus" phenomenon). The present fMRI study investigated the automatic binding of faces and scenes. In the face-face (F-F) condition, faces were presented alone during both encoding and retrieval, whereas in the face/scene-face (FS-F) condition, they were presented overlaid on scenes during encoding but alone during retrieval (context change). Although participants were instructed to focus only on the faces during both encoding and retrieval, recognition performance was worse in the FS-F than in the F-F condition ("context shift decrement" [CSD]), confirming automatic face-scene binding during encoding. This binding was mediated by the hippocampus as indicated by greater subsequent memory effects (remembered > forgotten) in this region for the FS-F than the F-F condition. Scene memory was mediated by right parahippocampal cortex, which was reactivated during successful retrieval when the faces were associated with a scene during encoding (FS-F condition). Analyses using the CSD as a regressor yielded a clear hemispheric asymmetry in medial temporal lobe activity during encoding: Left hippocampal and parahippocampal activity was associated with a smaller CSD, indicating more flexible memory representations immune to context changes, whereas right hippocampal/rhinal activity was associated with a larger CSD, indicating less flexible representations sensitive to context change. Taken together, the results clarify the neural mechanisms of context effects on face recognition.

  13. Human abdomen recognition using camera and force sensor in medical robot system for automatic ultrasound scan.

    PubMed

    Bin Mustafa, Ammar Safwan; Ishii, Takashi; Matsunaga, Yoshiki; Nakadate, Ryu; Ishii, Hiroyuki; Ogawa, Kouji; Saito, Akiko; Sugawara, Motoaki; Niki, Kiyomi; Takanishi, Atsuo

    2013-01-01

    Physicians use ultrasound scans to obtain real-time images of internal organs, because such scans are safe and inexpensive. However, people in remote areas face difficulties to be scanned due to aging society and physician's shortage. Hence, it is important to develop an autonomous robotic system to perform remote ultrasound scans. Previously, we developed a robotic system for automatic ultrasound scan focusing on human's liver. In order to make it a completely autonomous system, we present in this paper a way to autonomously localize the epigastric region as the starting position for the automatic ultrasound scan. An image processing algorithm marks the umbilicus and mammary papillae on a digital photograph of the patient's abdomen. Then, we made estimation for the location of the epigastric region using the distances between these landmarks. A supporting algorithm distinguishes rib position from epigastrium using the relationship between force and displacement. We implemented these algorithms with the automatic scanning system into an apparatus: a Mitsubishi Electric's MELFA RV-1 six axis manipulator. Tests on 14 healthy male subjects showed the apparatus located the epigastric region with a success rate of 94%. The results suggest that image recognition was effective in localizing a human body part. PMID:24110822

  14. Real-time imaging systems' combination of methods to achieve automatic target recognition

    NASA Astrophysics Data System (ADS)

    Maraviglia, Carlos G.; Williams, Elmer F.; Pezzulich, Alan Z.

    1998-03-01

    Using a combination of strategies real time imaging weapons systems are achieving their goals of detecting their intended targets. The demands of acquiring a target in a cluttered environment in a timely manner with a high degree of confidence demands compromise be made as to having a truly automatic system. A combination of techniques such as dedicated image processing hardware, real time operating systems, mixes of algorithmic methods, and multi-sensor detectors are a forbearance of the unleashed potential of future weapons system and their incorporation in truly autonomous target acquisition. Elements such as position information, sensor gain controls, way marks for mid course correction, and augmentation with different imaging spectrums as well as future capabilities such as neural net expert systems and decision processors over seeing a fusion matrix architecture may be considered tools for a weapon system's achievement of its ultimate goal. Currently, acquiring a target in a cluttered environment in a timely manner with a high degree of confidence demands compromises be made as to having a truly automatic system. It is now necessary to include a human in the track decision loop, a system feature that may be long lived. Automatic Track Recognition will still be the desired goal in future systems due to the variability of military missions and desirability of an expendable asset. Furthermore, with the increasing incorporation of multi-sensor information into the track decision the human element's real time contribution must be carefully engineered.

  15. Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots.

    PubMed

    Zhang, Lijuan; Guo, Tianci; Xi, Bin; Fan, Yang; Wang, Kun; Bi, Jiacheng; Wang, Ying

    2015-02-01

    The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24 h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system.

  16. Automatic anatomy recognition in post-tonsillectomy MR images of obese children with OSAS

    NASA Astrophysics Data System (ADS)

    Tong, Yubing; Udupa, Jayaram K.; Odhner, Dewey; Sin, Sanghun; Arens, Raanan

    2015-03-01

    Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.

  17. Parallel System Architecture (PSA): An efficient approach for automatic recognition of volcano-seismic events

    NASA Astrophysics Data System (ADS)

    Cortés, Guillermo; García, Luz; Álvarez, Isaac; Benítez, Carmen; de la Torre, Ángel; Ibáñez, Jesús

    2014-02-01

    Automatic recognition of volcano-seismic events is becoming one of the most demanded features in the early warning area at continuous monitoring facilities. While human-driven cataloguing is time-consuming and often an unreliable task, an appropriate machine framework allows expert technicians to focus only on result analysis and decision-making. This work presents an alternative to serial architectures used in classic recognition systems introducing a parallel implementation of the whole process: configuration, feature extraction, feature selection and classification stages are independently carried out for each type of events in order to exploit the intrinsic properties of each signal class. The system uses Gaussian Mixture Models (GMMs) to classify the database recorded at Deception Volcano Island (Antarctica) obtaining a baseline recognition rate of 84% with a cepstral-based waveform parameterization in the serial architecture. The parallel approach increases the results to close to 92% using mixture-based parameterization vectors or up to 91% when the vector size is reduced by 19% via the Discriminative Feature Selection (DFS) algorithm. Besides the result improvement, the parallel architecture represents a major step in terms of flexibility and reliability thanks to the class-focused analysis, providing an efficient tool for monitoring observatories which require real-time solutions.

  18. Automatic recognition of surface landmarks of anatomical structures of back and posture

    NASA Astrophysics Data System (ADS)

    Michoński, Jakub; Glinkowski, Wojciech; Witkowski, Marcin; Sitnik, Robert

    2012-05-01

    Faulty postures, scoliosis and sagittal plane deformities should be detected as early as possible to apply preventive and treatment measures against major clinical consequences. To support documentation of the severity of deformity and diminish x-ray exposures, several solutions utilizing analysis of back surface topography data were introduced. A novel approach to automatic recognition and localization of anatomical landmarks of the human back is presented that may provide more repeatable results and speed up the whole procedure. The algorithm was designed as a two-step process involving a statistical model built upon expert knowledge and analysis of three-dimensional back surface shape data. Voronoi diagram is used to connect mean geometric relations, which provide a first approximation of the positions, with surface curvature distribution, which further guides the recognition process and gives final locations of landmarks. Positions obtained using the developed algorithms are validated with respect to accuracy of manual landmark indication by experts. Preliminary validation proved that the landmarks were localized correctly, with accuracy depending mostly on the characteristics of a given structure. It was concluded that recognition should mainly take into account the shape of the back surface, putting as little emphasis on the statistical approximation as possible.

  19. Noise Robust Feature Scheme for Automatic Speech Recognition Based on Auditory Perceptual Mechanisms

    NASA Astrophysics Data System (ADS)

    Cai, Shang; Xiao, Yeming; Pan, Jielin; Zhao, Qingwei; Yan, Yonghong

    Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.

  20. User acceptance of intelligent avionics: A study of automatic-aided target recognition

    NASA Technical Reports Server (NTRS)

    Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

    1991-01-01

    User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

  1. Automatic target recognition and tracking using an acousto-optic image correlator

    SciTech Connect

    Molley, P.A.; Kast, B.A. )

    1992-05-01

    This paper discusses a hybrid electro-optic image processor, developed for automatic target recognition and tracking using an acousto-optic correlator and digital electronics. The optical system performs the computationally intensive correlation operation on the large 2-D input scenes. The electronics provide the decision-making capability and also perform part of the postprocessing needed for increasing the peak-to-clutter ratio in cluttered scenes. The system is able to analyze each correlation plane and apply a real-time template selection algorithm to accommodate scale or rotation changes of the target. A demonstration of the current system capabilities is presented using a terrain board with several different types of stationary and moving model vehicles.

  2. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    PubMed Central

    Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin

    2014-01-01

    Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728

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

    NASA Astrophysics Data System (ADS)

    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.

  4. Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems

    PubMed Central

    Chung, Yongwha; Oh, Seunggeun; Lee, Jonguk; Park, Daihee; Chang, Hong-Hee; Kim, Suk

    2013-01-01

    Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution. PMID:24072029

  5. Difficulties in Automatic Speech Recognition of Dysarthric Speakers and Implications for Speech-Based Applications Used by the Elderly: A Literature Review

    ERIC Educational Resources Information Center

    Young, Victoria; Mihailidis, Alex

    2010-01-01

    Despite their growing presence in home computer applications and various telephony services, commercial automatic speech recognition technologies are still not easily employed by everyone; especially individuals with speech disorders. In addition, relatively little research has been conducted on automatic speech recognition performance with older…

  6. Three-dimensional transformation for automatic target recognition using lidar data

    NASA Astrophysics Data System (ADS)

    Nieves, Ruben D.; Reynolds, William D., Jr.

    2010-04-01

    The three-dimensional (3-D) nature and the unorganized structure of topographic LIDAR data pose several challenges for target recognition tasks. In the past, several approaches have applied two-dimensional transformations such as spinimages or Digital Elevation Maps (DEMs) as an intermediate step for analyzing the 3-D data with two-dimensional (2-D) methods. However, these techniques are computationally intensive and often sacrifice some of the overall geometrical relationship of the target points. In this paper, we present a simple and efficient 3-D spatial transformation that preserves the geometrical attributes of the LIDAR data in all its dimensions. This transformation permits the utilization of well established statistical and shapebased descriptors for the implementation of an automatic target recognition algorithm. We evaluate our transformation and analysis technique on a set of simulated LIDAR point clouds of ground vehicles with varied obstructions and noise levels. Classification results demonstrate that our approach is efficient, tolerant to scale, rotation, and robust to noise and other degradations.

  7. A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

    PubMed Central

    Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

    2014-01-01

    This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

  8. A probabilistic union model with automatic order selection for noisy speech recognition.

    PubMed

    Jancovic, P; Ming, J

    2001-09-01

    A critical issue in exploiting the potential of the sub-band-based approach to robust speech recognition is the method of combining the sub-band observations, for selecting the bands unaffected by noise. A new method for this purpose, i.e., the probabilistic union model, was recently introduced. This model has been shown to be capable of dealing with band-limited corruption, requiring no knowledge about the band position and statistical distribution of the noise. A parameter within the model, which we call its order, gives the best results when it equals the number of noisy bands. Since this information may not be available in practice, in this paper we introduce an automatic algorithm for selecting the order, based on the state duration pattern generated by the hidden Markov model (HMM). The algorithm has been tested on the TIDIGITS database corrupted by various types of additive band-limited noise with unknown noisy bands. The results have shown that the union model equipped with the new algorithm can achieve a recognition performance similar to that achieved when the number of noisy bands is known. The results show a very significant improvement over the traditional full-band model, without requiring prior information on either the position or the number of noisy bands. The principle of the algorithm for selecting the order based on state duration may also be applied to other sub-band combination methods.

  9. Formant analysis in dysphonic patients and automatic Arabic digit speech recognition

    PubMed Central

    2011-01-01

    Background and objective There has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice. Materials and methods The speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal. Results There was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment. Conclusion The results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients. PMID:21624137

  10. A Study of Web-Based Oral Activities Enhanced by Automatic Speech Recognition for EFL College Learning

    ERIC Educational Resources Information Center

    Chiu, Tsuo-Lin; Liou, Hsien-Chin; Yeh, Yuli

    2007-01-01

    Recently, a promising topic in computer-assisted language learning is the application of Automatic Speech Recognition (ASR) technology for assisting learners to engage in meaningful speech interactions. Simulated real-life conversation supported by the application of ASR has been suggested as helpful for speaking. In this study, a web-based…

  11. An Exploration of the Potential of Automatic Speech Recognition to Assist and Enable Receptive Communication in Higher Education

    ERIC Educational Resources Information Center

    Wald, Mike

    2006-01-01

    The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search…

  12. Model-based automatic target recognition using hierarchical foveal machine vision

    NASA Astrophysics Data System (ADS)

    McKee, Douglas C.; Bandera, Cesar; Ghosal, Sugata; Rauss, Patrick J.

    1996-06-01

    This paper presents a target detection and interrogation techniques for a foveal automatic target recognition (ATR) system based on the hierarchical scale-space processing of imagery from a rectilinear tessellated multiacuity retinotopology. Conventional machine vision captures imagery and applies early vision techniques with uniform resolution throughout the field-of-view (FOV). In contrast, foveal active vision features graded acuity imagers and processing coupled with context sensitive gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision can operate more efficiently in dynamic scenarios with localized relevance than uniform acuity vision because resolution is treated as a dynamically allocable resource. Foveal ATR exploits the difference between detection and recognition resolution requirements and sacrifices peripheral acuity to achieve a wider FOV (e.g. faster search), greater localized resolution where needed (e.g., more confident recognition at the fovea), and faster frame rates (e.g., more reliable tracking and navigation) without increasing processing requirements. The rectilinearity of the retinotopology supports a data structure that is a subset of the image pyramid. This structure lends itself to multiresolution and conventional 2-D algorithms, and features a shift invariance of perceived target shape that tolerates sensor pointing errors and supports multiresolution model-based techniques. The detection technique described in this paper searches for regions-of- interest (ROIs) using the foveal sensor's wide FOV peripheral vision. ROIs are initially detected using anisotropic diffusion filtering and expansion template matching to a multiscale Zernike polynomial-based target model. Each ROI is then interrogated to filter out false target ROIs by sequentially pointing a higher acuity region of the sensor at each ROI centroid and conducting a fractal dimension test that distinguishes targets from structured clutter.

  13. Using Multi-threading for the Automatic Load Balancing of 2D Adaptive Finite Element Meshes

    NASA Technical Reports Server (NTRS)

    Heber, Gerd; Biswas, Rupak; Thulasiraman, Parimala; Gao, Guang R.; Saini, Subhash (Technical Monitor)

    1998-01-01

    In this paper, we present a multi-threaded approach for the automatic load balancing of adaptive finite element (FE) meshes The platform of our choice is the EARTH multi-threaded system which offers sufficient capabilities to tackle this problem. We implement the adaption phase of FE applications oil triangular meshes and exploit the EARTH token mechanism to automatically balance the resulting irregular and highly nonuniform workload. We discuss the results of our experiments oil EARTH-SP2, on implementation of EARTH on the IBM SP2 with different load balancing strategies that are built into the runtime system.

  14. Automatic Concept Relationships Discovery for an Adaptive E-Course

    ERIC Educational Resources Information Center

    Simko, Marian; Bielikova, Maria

    2009-01-01

    To make learning process more effective, the educational systems deliver content adapted to specific user needs. Adequate personalization requires the domain of learning to be described explicitly in a particular detail, involving relationships between knowledge elements referred to as concepts. Manual creation of necessary annotations is in the…

  15. An Automatic Online Calibration Design in Adaptive Testing

    ERIC Educational Resources Information Center

    Makransky, Guido; Glas, Cees A. W.

    2010-01-01

    An accurately calibrated item bank is essential for a valid computerized adaptive test. However, in some settings, such as occupational testing, there is limited access to test takers for calibration. As a result of the limited access to possible test takers, collecting data to accurately calibrate an item bank in an occupational setting is…

  16. Adaptive automatic segmentation of Leishmaniasis parasite in Indirect Immunofluorescence images.

    PubMed

    Ouertani, F; Amiri, H; Bettaib, J; Yazidi, R; Ben Salah, A

    2014-01-01

    This paper describes the first steps for the automation of the serum titration process. In fact, this process requires an Indirect Immunofluorescence (IIF) diagnosis automation. We deal with the initial phase that represents the fluorescence images segmentation. Our approach consists of three principle stages: (1) a color based segmentation which aims at extracting the fluorescent foreground based on k-means clustering, (2) the segmentation of the fluorescent clustered image, and (3) a region-based feature segmentation, intended to remove the fluorescent noisy regions and to locate fluorescent parasites. We evaluated the proposed method on 40 IIF images. Experimental results show that such a method provides reliable and robust automatic segmentation of fluorescent Promastigote parasite. PMID:25571049

  17. [Adaptive algorithm for automatic measurement of retinal vascular diameter].

    PubMed

    Münch, K; Vilser, W; Senff, I

    1995-11-01

    A new adaptive computer-aided method for the measurement of blood vessel diameters has been developed. Within areas of interest in the image, the algorithm detects, line-wise, the edges of the vessels, which are then used for image-wise approximation and noise filtration. A high level of adaptivity with respect to numerous measuring parameters ensures its use in a wide range of applications. Thus, it has been shown to significantly improve clinically relevant reproducibility in the area of follow-up observations. The standard deviation for vessel diameter was (2.2 +/- 0.7)% in the case of arteries and (1.8 +/- 0.5)% in the case of veins. Testing the algorithm in images of poor quality revealed its high level of reliability and sensitivity.

  18. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review.

    PubMed

    Alberdi, Ane; Aztiria, Asier; Basarab, Adrian

    2016-02-01

    Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

  19. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review.

    PubMed

    Alberdi, Ane; Aztiria, Asier; Basarab, Adrian

    2016-02-01

    Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system. PMID:26621099

  20. Dosimetric Evaluation of Automatic Segmentation for Adaptive IMRT for Head-and-Neck Cancer

    SciTech Connect

    Tsuji, Stuart Y.; Hwang, Andrew; Weinberg, Vivian; Yom, Sue S.; Quivey, Jeanne M.; Xia Ping

    2010-07-01

    Purpose: Adaptive planning to accommodate anatomic changes during treatment requires repeat segmentation. This study uses dosimetric endpoints to assess automatically deformed contours. Methods and Materials: Sixteen patients with head-and-neck cancer had adaptive plans because of anatomic change during radiotherapy. Contours from the initial planning computed tomography (CT) were deformed to the mid-treatment CT using an intensity-based free-form registration algorithm then compared with the manually drawn contours for the same CT using the Dice similarity coefficient and an overlap index. The automatic contours were used to create new adaptive plans. The original and automatic adaptive plans were compared based on dosimetric outcomes of the manual contours and on plan conformality. Results: Volumes from the manual and automatic segmentation were similar; only the gross tumor volume (GTV) was significantly different. Automatic plans achieved lower mean coverage for the GTV: V95: 98.6 {+-} 1.9% vs. 89.9 {+-} 10.1% (p = 0.004) and clinical target volume: V95: 98.4 {+-} 0.8% vs. 89.8 {+-} 6.2% (p < 0.001) and a higher mean maximum dose to 1 cm{sup 3} of the spinal cord 39.9 {+-} 3.7 Gy vs. 42.8 {+-} 5.4 Gy (p = 0.034), but no difference for the remaining structures. Conclusions: Automatic segmentation is not robust enough to substitute for physician-drawn volumes, particularly for the GTV. However, it generates normal structure contours of sufficient accuracy when assessed by dosimetric end points.

  1. Automatic disease screening method using image processing for dried blood microfluidic drop stain pattern recognition.

    PubMed

    Sikarwar, Basant S; Roy, Mukesh; Ranjan, Priya; Goyal, Ayush

    2016-07-01

    This paper examines programmed automatic recognition of infection from samples of dried stains of micro-scale drops of patient blood. This technique has the upside of being low-cost and less-intrusive and not requiring puncturing the patient with a needle for drawing blood, which is especially critical for infants and the matured. It also does not require expensive pathological blood test laboratory equipment. The method is shown in this work to be successful for ailment identification in patients suffering from tuberculosis and anaemia. Illness affects the physical properties of blood, which thus influence the samples of dried micro-scale blood drop stains. For instance, if a patient has a severe drop in platelet count, which is often the case of dengue or malaria patients, the blood's physical property of viscosity drops substantially, i.e. the blood is thinner. Thus, the blood micro-scale drop stain samples can be utilised for diagnosing maladies. This paper presents programmed automatic examination of the dried micro-scale drop blood stain designs utilising an algorithm based on pattern recognition. The samples of micro-scale blood drop stains of ordinary non-infected people are clearly recognisable as well as the samples of micro-scale blood drop stains of sick people, due to key distinguishing features. As a contextual analysis, the micro-scale blood drop stains of patients infected with tuberculosis have been contrasted with the micro-scale blood drop stains of typical normal healthy people. The paper dives into the fundamental flow mechanics behind how the samples of the dried micro-scale blood drop stain is shaped. What has been found is a thick ring like feature in the dried micro-scale blood drop stains of non-ailing people and thin shape like lines in the dried micro-scale blood drop stains of patients with anaemia or tuberculosis disease. The ring like feature at the periphery is caused by an outward stream conveying suspended particles to the edge

  2. An algorithm for automatic target recognition using passive radar and an EKF for estimating aircraft orientation

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.

    2005-07-01

    Rather than emitting pulses, passive radar systems rely on "illuminators of opportunity," such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern

  3. Automatic recognition of abnormal cells in cytological tests using multispectral imaging

    NASA Astrophysics Data System (ADS)

    Gertych, A.; Galliano, G.; Bose, S.; Farkas, D. L.

    2010-03-01

    Cervical cancer is the leading cause of gynecologic disease-related death worldwide, but is almost completely preventable with regular screening, for which cytological testing is a method of choice. Although such testing has radically lowered the death rate from cervical cancer, it is plagued by low sensitivity and inter-observer variability. Moreover, its effectiveness is still restricted because the recognition of shape and morphology of nuclei is compromised by overlapping and clumped cells. Multispectral imaging can aid enhanced morphological characterization of cytological specimens. Features including spectral intensity and texture, reflecting relevant morphological differences between normal and abnormal cells, can be derived from cytopathology images and utilized in a detection/classification scheme. Our automated processing of multispectral image cubes yields nuclear objects which are subjected to classification facilitated by a library of spectral signatures obtained from normal and abnormal cells, as marked by experts. Clumps are processed separately with reduced set of signatures. Implementation of this method yields high rate of successful detection and classification of nuclei into predefined malignant and premalignant types and correlates well with those obtained by an expert. Our multispectral approach may have an impact on the diagnostic workflow of cytological tests. Abnormal cells can be automatically highlighted and quantified, thus objectivity and performance of the reading can be improved in a way which is currently unavailable in clinical setting.

  4. A robust and automatic method for human parasite egg recognition in microscopic images.

    PubMed

    Li, Zhixun; Gong, Huiling; Zhang, Wei; Chen, Lian; Tao, Juncai; Song, Langui; Wu, Zhongdao

    2015-10-01

    With the accelerated movement of population, human parasitoses become an increasingly serious public health's problem. Currently, detections of parasite eggs through microscopic images are still the golden standard for diagnoses. However, this conventional method relies heavily on the experiences of inspectors, thus giving rise to misdiagnoses and missed diagnoses occasionally. And, as the number of clinical specimens increases rapidly, manual identification seems impractical. Hence, a fully automatic method is in desperate need. In this paper, we propose a robust method to segment and recognize the parasite eggs. Their contours are extracted using phase coherence technology, and the support vector machine (SVM) method based on shape and texture features is employed to classification of parasite eggs. Our novel method was comparable to the traditional method. The overall recognition rate was up to 95%, and the overall robustness indexes, including si, fnvf, fvpf, tpvf, were 95.7, 4.9, 3.7, 95.1, respectively, suggesting that our method is effective and the robustness is good, which has great potential to become a diagnostic method in the parasitological clinic.

  5. An automatic geo-spatial object recognition algorithm for high resolution satellite images

    NASA Astrophysics Data System (ADS)

    Ergul, Mustafa; Alatan, A. Aydın.

    2013-10-01

    This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification step, the foreground objects are first extracted by a semi-supervised image segmentation algorithm, utilizing detection results from the previous step, and then, the shape descriptors for segmented objects are utilized to prune out the false positives. Based on simulation results, it can be argued that the proposed algorithm achieves both high precision and high recall rates as a result of taking advantage of both the local feature-based and the shape-based object detection approaches. The superiority of the proposed method is due to the ability of minimization of false alarm rate and since most of the object shapes contain more characteristic and discriminative information about their identity and functionality.

  6. Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Skowronski, Mark D.; Harris, John G.

    2004-09-01

    Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor cepstral coefficients (HFCC), a modification of MFCC that uses the known relationship between center frequency and critical bandwidth from human psychoacoustics to decouple filter bandwidth from filter spacing. In this work, the authors introduce a variation of HFCC called HFCC-E in which filter bandwidth is linearly scaled in order to investigate the effects of wider filter bandwidth on noise robustness. Experimental results show an increase in signal-to-noise ratio of 7 dB over traditional MFCC algorithms when filter bandwidth increases in HFCC-E. An important attribute of both HFCC and HFCC-E is that the algorithms only differ from MFCC in the filter bank coefficients: increased noise robustness using wider filters is achieved with no additional computational cost.

  7. Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition.

    PubMed

    Skowronski, Mark D; Harris, John G

    2004-09-01

    Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor cepstral coefficients (HFCC), a modification of MFCC that uses the known relationship between center frequency and critical bandwidth from human psychoacoustics to decouple filter bandwidth from filter spacing. In this work, the authors introduce a variation of HFCC called HFCC-E in which filter bandwidth is linearly scaled in order to investigate the effects of wider filter bandwidth on noise robustness. Experimental results show an increase in signal-to-noise ratio of 7 dB over traditional MFCC algorithms when filter bandwidth increases in HFCC-E. An important attribute of both HFCC and HFCC-E is that the algorithms only differ from MFCC in the filter bank coefficients: increased noise robustness using wider filters is achieved with no additional computational cost.

  8. AUTOMATISM.

    PubMed

    MCCALDON, R J

    1964-10-24

    Individuals can carry out complex activity while in a state of impaired consciousness, a condition termed "automatism". Consciousness must be considered from both an organic and a psychological aspect, because impairment of consciousness may occur in both ways. Automatism may be classified as normal (hypnosis), organic (temporal lobe epilepsy), psychogenic (dissociative fugue) or feigned. Often painstaking clinical investigation is necessary to clarify the diagnosis. There is legal precedent for assuming that all crimes must embody both consciousness and will. Jurists are loath to apply this principle without reservation, as this would necessitate acquittal and release of potentially dangerous individuals. However, with the sole exception of the defence of insanity, there is at present no legislation to prohibit release without further investigation of anyone acquitted of a crime on the grounds of "automatism".

  9. Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

    PubMed

    Agarwalla, Swapna; Sarma, Kandarpa Kumar

    2016-06-01

    Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time

  10. Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

    PubMed

    Agarwalla, Swapna; Sarma, Kandarpa Kumar

    2016-06-01

    Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time

  11. Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Maas, Christian; Schmalzl, Jörg

    2013-08-01

    Ground Penetrating Radar (GPR) is used for the localization of supply lines, land mines, pipes and many other buried objects. These objects can be recognized in the recorded data as reflection hyperbolas with a typical shape depending on depth and material of the object and the surrounding material. To obtain the parameters, the shape of the hyperbola has to be fitted. In the last years several methods were developed to automate this task during post-processing. In this paper we show another approach for the automated localization of reflection hyperbolas in GPR data by solving a pattern recognition problem in grayscale images. In contrast to other methods our detection program is also able to immediately mark potential objects in real-time. For this task we use a version of the Viola-Jones learning algorithm, which is part of the open source library "OpenCV". This algorithm was initially developed for face recognition, but can be adapted to any other simple shape. In our program it is used to narrow down the location of reflection hyperbolas to certain areas in the GPR data. In order to extract the exact location and the velocity of the hyperbolas we apply a simple Hough Transform for hyperbolas. Because the Viola-Jones Algorithm reduces the input for the computational expensive Hough Transform dramatically the detection system can also be implemented on normal field computers, so on-site application is possible. The developed detection system shows promising results and detection rates in unprocessed radargrams. In order to improve the detection results and apply the program to noisy radar images more data of different GPR systems as input for the learning algorithm is necessary.

  12. Investigation of measureable parameters that correlate with automatic target recognition performance in synthetic aperture sonar

    NASA Astrophysics Data System (ADS)

    Gazagnaire, Julia; Cobb, J. T.; Isaacs, Jason

    2015-05-01

    There is a desire in the Mine Counter Measure community to develop a systematic method to predict and/or estimate the performance of Automatic Target Recognition (ATR) algorithms that are detecting and classifying mine-like objects within sonar data. Ideally, parameters exist that can be measured directly from the sonar data that correlate with ATR performance. In this effort, two metrics were analyzed for their predictive potential using high frequency synthetic aperture sonar (SAS) images. The first parameter is a measure of contrast. It is essentially the variance in pixel intensity over a fixed partition of relatively small size. An analysis was performed to determine the optimum block size for this contrast calculation. These blocks were then overlapped in the horizontal and vertical direction over the entire image. The second parameter is the one-dimensional K-shape parameter. The K-distribution is commonly used to describe sonar backscatter return from range cells that contain a finite number of scatterers. An Ada-Boosted Decision Tree classifier was used to calculate the probability of classification (Pc) and false alarm rate (FAR) for several types of targets in SAS images from three different data sets. ROC curves as a function of the measured parameters were generated and the correlation between the measured parameters in the vicinity of each of the contacts and the ATR performance was investigated. The contrast and K-shape parameters were considered separately. Additionally, the contrast and K-shape parameter were associated with background texture types using previously labeled high frequency SAS images.

  13. Automatic target recognition using both measurements from identity sensors and motion information from tracking sensors

    NASA Astrophysics Data System (ADS)

    Copsey, Keith D.

    2005-05-01

    The majority of automatic target recognition (ATR) studies are formulated as a traditional classification problem. Specifically, using a training set of target exemplars, a classifier is developed for application to isolated measurements of targets. Performance is assessed using a test set of target exemplars. Unfortunately, this is a simplification of the ATR problem. Often, the operating conditions differ from those prevailing at the time of training data collection, which can have severe effects on the obtained performance. It is therefore becoming increasingly recognised that development of robust ATR systems requires more than just consideration of the traditional classification problem. In particular, one should make use of any extra information or data that is available. The example in this paper focuses on a hybrid ATR system being designed to utilise both measurements from identity sensors (such as radar profiles) and motion information from tracking sensors to classify targets. The first-stage of the system uses mixture-model classifiers to classify targets into generic classes based upon data from (long range) tracking sensors. Where the generic classes are related to platform types (e.g. fast-jets, heavy bombers and commercial aircraft), the initial classifications can be used to assist the military commander's early decision making. The second-stage of the system uses measurements from (closer-range) identity sensors to classify the targets into individual target types, while taking into account the (uncertain) outputs from the first-stage. A Bayesian classifier is proposed for the second-stage, so that the first-stage outputs can be incorporated into the second-stage prior class probabilities.

  14. A Corpus-Based Approach for Automatic Thai Unknown Word Recognition Using Boosting Techniques

    NASA Astrophysics Data System (ADS)

    Techo, Jakkrit; Nattee, Cholwich; Theeramunkong, Thanaruk

    While classification techniques can be applied for automatic unknown word recognition in a language without word boundary, it faces with the problem of unbalanced datasets where the number of positive unknown word candidates is dominantly smaller than that of negative candidates. To solve this problem, this paper presents a corpus-based approach that introduces a so-called group-based ranking evaluation technique into ensemble learning in order to generate a sequence of classification models that later collaborate to select the most probable unknown word from multiple candidates. Given a classification model, the group-based ranking evaluation (GRE) is applied to construct a training dataset for learning the succeeding model, by weighing each of its candidates according to their ranks and correctness when the candidates of an unknown word are considered as one group. A number of experiments have been conducted on a large Thai medical text to evaluate performance of the proposed group-based ranking evaluation approach, namely V-GRE, compared to the conventional naïve Bayes classifier and our vanilla version without ensemble learning. As the result, the proposed method achieves an accuracy of 90.93±0.50% when the first rank is selected while it gains 97.26±0.26% when the top-ten candidates are considered, that is 8.45% and 6.79% improvement over the conventional record-based naïve Bayes classifier and the vanilla version. Another result on applying only best features show 93.93±0.22% and up to 98.85±0.15% accuracy for top-1 and top-10, respectively. They are 3.97% and 9.78% improvement over naive Bayes and the vanilla version. Finally, an error analysis is given.

  15. Adaptive finite element program for automatic modeling of thermal processes during laser-tissue interaction

    NASA Astrophysics Data System (ADS)

    Yakunin, Alexander N.; Scherbakov, Yury N.

    1994-02-01

    The absence of satisfactory criteria for discrete model parameters choice during computer modeling of thermal processes of laser-biotissue interaction may be the premier sign for the accuracy of the numerical results obtained. The approach realizing the new concept of direct automatical adaptive grid construction is suggested. The intellectual program provides high calculation accuracy and is simple in practical usage so that a physician receives the ability to prescribe treatment without any assistance of a specialist in mathematical modeling.

  16. Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

    SciTech Connect

    Zhang Tiezhi . E-mail: tiezhi.zhang@beaumont.edu; Chi Yuwei; Meldolesi, Elisa; Yan Di

    2007-06-01

    Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces. Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs. Conclusion: With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments.

  17. Learning and plan refinement in a knowledge-based system for automatic speech recognition

    SciTech Connect

    De Mori, R.; Lam, L.; Gilloux, M.

    1987-03-01

    This paper shows how a semiautomatic design of a speech recognition system can be done as a planning activity. Recognition performances are used for deciding plan refinement. Inductive learning is performed for setting action preconditions. Experimental results in the recognition of connected letters spoken by 100 speakers are presented.

  18. Recognition of voice commands using adaptation of foreign language speech recognizer via selection of phonetic transcriptions

    NASA Astrophysics Data System (ADS)

    Maskeliunas, Rytis; Rudzionis, Vytautas

    2011-06-01

    In recent years various commercial speech recognizers have become available. These recognizers provide the possibility to develop applications incorporating various speech recognition techniques easily and quickly. All of these commercial recognizers are typically targeted to widely spoken languages having large market potential; however, it may be possible to adapt available commercial recognizers for use in environments where less widely spoken languages are used. Since most commercial recognition engines are closed systems the single avenue for the adaptation is to try set ways for the selection of proper phonetic transcription methods between the two languages. This paper deals with the methods to find the phonetic transcriptions for Lithuanian voice commands to be recognized using English speech engines. The experimental evaluation showed that it is possible to find phonetic transcriptions that will enable the recognition of Lithuanian voice commands with recognition accuracy of over 90%.

  19. Action adaptation during natural unfolding social scenes influences action recognition and inferences made about actor beliefs.

    PubMed

    Keefe, Bruce D; Wincenciak, Joanna; Jellema, Tjeerd; Ward, James W; Barraclough, Nick E

    2016-07-01

    When observing another individual's actions, we can both recognize their actions and infer their beliefs concerning the physical and social environment. The extent to which visual adaptation influences action recognition and conceptually later stages of processing involved in deriving the belief state of the actor remains unknown. To explore this we used virtual reality (life-size photorealistic actors presented in stereoscopic three dimensions) to see how visual adaptation influences the perception of individuals in naturally unfolding social scenes at increasingly higher levels of action understanding. We presented scenes in which one actor picked up boxes (of varying number and weight), after which a second actor picked up a single box. Adaptation to the first actor's behavior systematically changed perception of the second actor. Aftereffects increased with the duration of the first actor's behavior, declined exponentially over time, and were independent of view direction. Inferences about the second actor's expectation of box weight were also distorted by adaptation to the first actor. Distortions in action recognition and actor expectations did not, however, extend across different actions, indicating that adaptation is not acting at an action-independent abstract level but rather at an action-dependent level. We conclude that although adaptation influences more complex inferences about belief states of individuals, this is likely to be a result of adaptation at an earlier action recognition stage rather than adaptation operating at a higher, more abstract level in mentalizing or simulation systems. PMID:27472496

  20. Tracker-aided adaptive multi-frame recognition of a specific target

    NASA Astrophysics Data System (ADS)

    Mahalanobis, Abhijit

    2016-05-01

    We consider the problem of recognizing a particular target of interest (i.e. the "correct" target) while rejecting other targets and background clutter. In such instances, the probability of recognizing the correct target (PCT) is a suitable metric for assessing the performance of the target recognition algorithm. We present a definition for PCT and illustrate how it differs from conventional metrics for target recognition by means of an example. It is further shown that an adaptive target recognition algorithm, which relies on track position to obtain multiple looks at the target, can significantly improve PCT while reducing the track uncertainty.

  1. A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

    SciTech Connect

    Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.

    2014-06-15

    Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment

  2. Automatic off-body overset adaptive Cartesian mesh method based on an octree approach

    SciTech Connect

    Peron, Stephanie; Benoit, Christophe

    2013-01-01

    This paper describes a method for generating adaptive structured Cartesian grids within a near-body/off-body mesh partitioning framework for the flow simulation around complex geometries. The off-body Cartesian mesh generation derives from an octree structure, assuming each octree leaf node defines a structured Cartesian block. This enables one to take into account the large scale discrepancies in terms of resolution between the different bodies involved in the simulation, with minimum memory requirements. Two different conversions from the octree to Cartesian grids are proposed: the first one generates Adaptive Mesh Refinement (AMR) type grid systems, and the second one generates abutting or minimally overlapping Cartesian grid set. We also introduce an algorithm to control the number of points at each adaptation, that automatically determines relevant values of the refinement indicator driving the grid refinement and coarsening. An application to a wing tip vortex computation assesses the capability of the method to capture accurately the flow features.

  3. Automatic Iceball Segmentation With Adapted Shape Priors for MRI-Guided Cryoablation

    PubMed Central

    Liu, Xinyang; Tuncali, Kemal; Wells, William M.; Zientara, Gary P.

    2014-01-01

    Purpose To develop and evaluate an automatic segmentation method that extracts the 3D configuration of the ablation zone, the iceball, from images acquired during the freezing phase of MRI-guided cryoablation. Materials and Methods Intraprocedural images at 63 timepoints from 13 kidney tumor cryoablation procedures were examined retrospectively. The images were obtained using a 3 Tesla wide-bore MRI scanner and axial HASTE sequence. Initialized with semiautomatically localized cryoprobes, the iceball was segmented automatically at each timepoint using the graph cut (GC) technique with adapted shape priors. Results The average Dice Similarity Coefficients (DSC), compared with manual segmentations, were 0.88, 0.92, 0.92, 0.93, and 0.93 at 3, 6, 9, 12, and 15 min time-points, respectively, and the average DSC of the total 63 segmentations was 0.92 ± 0.03. The proposed method improved the accuracy significantly compared with the approach without shape prior adaptation (P = 0.026). The number of probes involved in the procedure had no apparent influence on the segmentation results using our technique. The average computation time was 20 s, which was compatible with an intraprocedural setting. Conclusion Our automatic iceball segmentation method demonstrated high accuracy and robustness for practical use in monitoring the progress of MRI-guided cryoablation. PMID:24338961

  4. Adaptive optical radial basis function neural network for handwritten digit recognition

    NASA Astrophysics Data System (ADS)

    Foor, Wesley E.; Neifeld, Mark A.

    1994-06-01

    An adaptive optical radial basis function classifier for handwritten digit recognition is experimentally demonstrated. We describe a spatially- multiplexed system incorporating on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input and 198 stored reference patterns in parallel using dual vector-matrix multipliers. For this experimental software is used to perform the on-line learning of the weights and basis function widths. An experimental recognition rate of 86.7% correct out of 300 testing samples is achieved with the adaptive training versus 52.3% correct for non-adaptive training. The experimental results from the optical system are compared with data from a computer model of the system in order to identify noise sources and indicate possible improvements for system performance.

  5. Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation

    NASA Astrophysics Data System (ADS)

    Xu, Xiangbo; Chen, Shao; Zhang, Yanan

    2016-07-01

    To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.

  6. Evidence for altered amygdala activation in schizophrenia in an adaptive emotion recognition task.

    PubMed

    Mier, Daniela; Lis, Stefanie; Zygrodnik, Karina; Sauer, Carina; Ulferts, Jens; Gallhofer, Bernd; Kirsch, Peter

    2014-03-30

    Deficits in social cognition seem to present an intermediate phenotype for schizophrenia, and are known to be associated with an altered amygdala response to faces. However, current results are heterogeneous with respect to whether this altered amygdala response in schizophrenia is hypoactive or hyperactive in nature. The present study used functional magnetic resonance imaging to investigate emotion-specific amygdala activation in schizophrenia using a novel adaptive emotion recognition paradigm. Participants comprised 11 schizophrenia outpatients and 16 healthy controls who viewed face stimuli expressing emotions of anger, fear, happiness, and disgust, as well as neutral expressions. The adaptive emotion recognition approach allows the assessment of group differences in both emotion recognition performance and associated neuronal activity while also ensuring a comparable number of correctly recognized emotions between groups. Schizophrenia participants were slower and had a negative bias in emotion recognition. In addition, they showed reduced differential activation during recognition of emotional compared with neutral expressions. Correlation analyses revealed an association of a negative bias with amygdala activation for neutral facial expressions that was specific to the patient group. We replicated previous findings of affected emotion recognition in schizophrenia. Furthermore, we demonstrated that altered amygdala activation in the patient group was associated with the occurrence of a negative bias. These results provide further evidence for impaired social cognition in schizophrenia and point to a central role of the amygdala in negative misperceptions of facial stimuli in schizophrenia.

  7. Automatic carrier landing system for V/STOL aircraft using L1 adaptive and optimal control

    NASA Astrophysics Data System (ADS)

    Hariharapura Ramesh, Shashank

    This thesis presents a framework for developing automatic carrier landing systems for aircraft with vertical or short take-off and landing capability using two different control strategies---gain-scheduled linear optimal control, and L1 adaptive control. The carrier landing sequence of V/STOL aircraft involves large variations in dynamic pressure and aerodynamic coefficients arising because of the transition from aerodynamic-supported to jet-borne flight, descent to the touchdown altitude, and turns performed to align with the runway. Consequently, the dynamics of the aircraft exhibit a highly non-linear dynamical behavior with variations in flight conditions prior to touchdown. Therefore, the implication is the need for non-linear control techniques to achieve automatic landing. Gain-scheduling has been one of the most widely employed techniques for control of aircraft, which involves designing linear controllers for numerous trimmed flight conditions, and interpolating them to achieve a global non-linear control. Adaptive control technique, on the other hand, eliminates the need to schedule the controller parameters as they adapt to changing flight conditions.

  8. Automaticity and Stability of Adaptation to a Foreign-Accented Speaker.

    PubMed

    Witteman, Marijt J; Bardhan, Neil P; Weber, Andrea; McQueen, James M

    2015-06-01

    In three cross-modal priming experiments we asked whether adaptation to a foreign-accented speaker is automatic, and whether adaptation can be seen after a long delay between initial exposure and test. Dutch listeners were exposed to a Hebrew-accented Dutch speaker with two types of Dutch words: those that contained [I] (globally accented words), and those in which the Dutch [i] was shortened to [I] (specific accent marker words). Experiment 1, which served as a baseline, showed that native Dutch participants showed facilitatory priming for globally accented, but not specific accent, words. In experiment 2, participants performed a 3.5-minute phoneme monitoring task, and were tested on their comprehension of the accented speaker 24 hours later using the same cross-modal priming task as in experiment 1. During the phoneme monitoring task, listeners were asked to detect a consonant that was not strongly accented. In experiment 3, the delay between exposure and test was extended to 1 week. Listeners in experiments 2 and 3 showed facilitatory priming for both globally accented and specific accent marker words. Together, these results show that adaptation to a foreign-accented speaker can be rapid and automatic, and can be observed after a prolonged delay in testing. PMID:26677641

  9. Automatism

    PubMed Central

    McCaldon, R. J.

    1964-01-01

    Individuals can carry out complex activity while in a state of impaired consciousness, a condition termed “automatism”. Consciousness must be considered from both an organic and a psychological aspect, because impairment of consciousness may occur in both ways. Automatism may be classified as normal (hypnosis), organic (temporal lobe epilepsy), psychogenic (dissociative fugue) or feigned. Often painstaking clinical investigation is necessary to clarify the diagnosis. There is legal precedent for assuming that all crimes must embody both consciousness and will. Jurists are loath to apply this principle without reservation, as this would necessitate acquittal and release of potentially dangerous individuals. However, with the sole exception of the defence of insanity, there is at present no legislation to prohibit release without further investigation of anyone acquitted of a crime on the grounds of “automatism”. PMID:14199824

  10. Ethanolamine Signaling Promotes Salmonella Niche Recognition and Adaptation during Infection

    PubMed Central

    Anderson, Christopher J.; Clark, David E.; Adli, Mazhar; Kendall, Melissa M.

    2015-01-01

    Chemical and nutrient signaling are fundamental for all cellular processes, including interactions between the mammalian host and the microbiota, which have a significant impact on health and disease. Ethanolamine is an essential component of cell membranes and has profound signaling activity within mammalian cells by modulating inflammatory responses and intestinal physiology. Here, we describe a virulence-regulating pathway in which the foodborne pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) exploits ethanolamine signaling to recognize and adapt to distinct niches within the host. The bacterial transcription factor EutR promotes ethanolamine metabolism in the intestine, which enables S. Typhimurium to establish infection. Subsequently, EutR directly activates expression of the Salmonella pathogenicity island 2 in the intramacrophage environment, and thus augments intramacrophage survival. Moreover, EutR is critical for robust dissemination during mammalian infection. Our findings reveal that S. Typhimurium co-opts ethanolamine as a signal to coordinate metabolism and then virulence. Because the ability to sense ethanolamine is a conserved trait among pathogenic and commensal bacteria, our work indicates that ethanolamine signaling may be a key step in the localized adaptation of bacteria within their mammalian hosts. PMID:26565973

  11. Applications of automatic mesh generation and adaptive methods in computational medicine

    SciTech Connect

    Schmidt, J.A.; Macleod, R.S.; Johnson, C.R.; Eason, J.C.

    1995-12-31

    Important problems in Computational Medicine exist that can benefit from the implementation of adaptive mesh refinement techniques. Biological systems are so inherently complex that only efficient models running on state of the art hardware can begin to simulate reality. To tackle the complex geometries associated with medical applications we present a general purpose mesh generation scheme based upon the Delaunay tessellation algorithm and an iterative point generator. In addition, automatic, two- and three-dimensional adaptive mesh refinement methods are presented that are derived from local and global estimates of the finite element error. Mesh generation and adaptive refinement techniques are utilized to obtain accurate approximations of bioelectric fields within anatomically correct models of the heart and human thorax. Specifically, we explore the simulation of cardiac defibrillation and the general forward and inverse problems in electrocardiography (ECG). Comparisons between uniform and adaptive refinement techniques are made to highlight the computational efficiency and accuracy of adaptive methods in the solution of field problems in computational medicine.

  12. Adaptive, optical, radial basis function neural network for handwritten digit recognition

    NASA Astrophysics Data System (ADS)

    Foor, Wesley E.; Neifeld, Mark A.

    1995-11-01

    An adaptive, optical, radial basis function classifier for handwritten digit recognition is experimentally demonstrated. We describe a spatially multiplexed system that incorporates an on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input vector and 198 stored reference patterns in parallel by using dual vector-matrix multipliers and a contrast-reversing spatial light modulator. Software is used to emulate an electronic chip that performs the on-line learning of the weights and basis function widths. An experimental recognition rate of 92.7% correct out of 300 testing samples is achieved with the adaptive training, versus 31.0% correct for nonadaptive training. We compare the experimental results with a detailed computer model of the system in order to analyze the influence of various noise sources on the system performance.

  13. Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing

    NASA Astrophysics Data System (ADS)

    He, Yufan; Sun, Yankui; Chen, Min; Zheng, Yuanjie; Liu, Hui; Leon, Cecilia; Beltran, William; Gee, James C.

    2016-03-01

    In recent years, several studies have shown that the canine retina model offers important insight for our understanding of human retinal diseases. Several therapies developed to treat blindness in such models have already moved onto human clinical trials, with more currently under development [1]. Optical coherence tomography (OCT) offers a high resolution imaging modality for performing in-vivo analysis of the retinal layers. However, existing algorithms for automatically segmenting and analyzing such data have been mostly focused on the human retina. As a result, canine retinal images are often still being analyzed using manual segmentations, which is a slow and laborious task. In this work, we propose a method for automatically segmenting 5 boundaries in canine retinal OCT. The algorithm employs the position relationships between different boundaries to adaptively enhance the gradient map. A region growing algorithm is then used on the enhanced gradient maps to find the five boundaries separately. The automatic segmentation was compared against manual segmentations showing an average absolute error of 5.82 +/- 4.02 microns.

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

    NASA Astrophysics Data System (ADS)

    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.

  15. Experience with automatic, dynamic load balancing and adaptive finite element computation

    SciTech Connect

    Wheat, S.R.; Devine, K.D.; Maccabe, A.B.

    1993-10-01

    Distributed memory, Massively Parallel (MP), MIMD technology has enabled the development of applications requiring computational resources previously unobtainable. Structural mechanics and fluid dynamics applications, for example, are often solved by finite element methods (FEMs) requiring, millions of degrees of freedom to accurately simulate physical phenomenon. Adaptive methods, which automatically refine or coarsen meshes and vary the order of accuracy of the numerical solution, offer greater robustness and computational efficiency than traditional FEMs by reducing the amount of computation required away from physical structures such as shock waves and boundary layers. On MP computers, FEMs frequently result in distributed processor load imbalances. To overcome load imbalance, many MP FEMs use static load balancing as a preprocessor to the finite element calculation. Adaptive methods complicate the load imbalance problem since the work per element is not uniform across the solution domain and changes as the computation proceeds. Therefore, dynamic load balancing is required to maintain global load balance. We describe a dynamic, fine-grained, element-based data migration system that maintains global load balance and is effective in the presence of changing work loads. Global load balance is achieved by overlapping neighborhoods of processors, where each neighborhood performs local load balancing. The method utilizes an automatic element management system library to which a programmer integrates the application`s computational description. The library`s flexibility supports a large class of finite element and finite difference based applications.

  16. An automatic dose verification system for adaptive radiotherapy for helical tomotherapy

    NASA Astrophysics Data System (ADS)

    Mo, Xiaohu; Chen, Mingli; Parnell, Donald; Olivera, Gustavo; Galmarini, Daniel; Lu, Weiguo

    2014-03-01

    Purpose: During a typical 5-7 week treatment of external beam radiotherapy, there are potential differences between planned patient's anatomy and positioning, such as patient weight loss, or treatment setup. The discrepancies between planned and delivered doses resulting from these differences could be significant, especially in IMRT where dose distributions tightly conforms to target volumes while avoiding organs-at-risk. We developed an automatic system to monitor delivered dose using daily imaging. Methods: For each treatment, a merged image is generated by registering the daily pre-treatment setup image and planning CT using treatment position information extracted from the Tomotherapy archive. The treatment dose is then computed on this merged image using our in-house convolution-superposition based dose calculator implemented on GPU. The deformation field between merged and planning CT is computed using the Morphon algorithm. The planning structures and treatment doses are subsequently warped for analysis and dose accumulation. All results are saved in DICOM format with private tags and organized in a database. Due to the overwhelming amount of information generated, a customizable tolerance system is used to flag potential treatment errors or significant anatomical changes. A web-based system and a DICOM-RT viewer were developed for reporting and reviewing the results. Results: More than 30 patients were analysed retrospectively. Our in-house dose calculator passed 97% gamma test evaluated with 2% dose difference and 2mm distance-to-agreement compared with Tomotherapy calculated dose, which is considered sufficient for adaptive radiotherapy purposes. Evaluation of the deformable registration through visual inspection showed acceptable and consistent results, except for cases with large or unrealistic deformation. Our automatic flagging system was able to catch significant patient setup errors or anatomical changes. Conclusions: We developed an automatic dose

  17. An approach of crater automatic recognition based on contour digital elevation model from Chang'E Missions

    NASA Astrophysics Data System (ADS)

    Zuo, W.; Li, C.; Zhang, Z.; Li, H.; Feng, J.

    2015-12-01

    In order to provide fundamental information for exploration and related scientific research on the Moon and other planets, we propose a new automatic method to recognize craters on the lunar surface based on contour data extracted from a digital elevation model (DEM). First, we mapped 16-bits DEM to 256 gray scales for data compression, then for the purposes of better visualization, the grayscale is converted into RGB image. After that, a median filter is applied twice to DEM for data optimization, which produced smooth, continuous outlines for subsequent construction of contour plane. Considering the fact that the morphology of crater on contour plane can be approximately expressed as an ellipse or circle, we extract the outer boundaries of contour plane with the same color(gray value) as targets for further identification though a 8- neighborhood counterclockwise searching method. Then, A library of training samples is constructed based on above targets calculated from some sample DEM data, from which real crater targets are labeled as positive samples manually, and non-crater objects are labeled as negative ones. Some morphological feathers are calculated for all these samples, which are major axis (L), circumference(C), area inside the boundary(S), and radius of the largest inscribed circle(R). We use R/L, R/S, C/L, C/S, R/C, S/L as the key factors for identifying craters, and apply Fisher discrimination method on the sample library to calculate the weight of each factor and determine the discrimination formula, which is then applied to DEM data for identifying lunar craters. The method has been tested and verified with DEM data from CE-1 and CE-2, showing strong recognition ability and robustness and is applicable for the recognition of craters with various diameters and significant morphological differences, making fast and accurate automatic crater recognition possible.

  18. Interfacing sound stream segregation to automatic speech recognition-Preliminary results on listening to several sounds simultaneously

    SciTech Connect

    Okuno, Hiroshi G.; Nakatani, Tomohiro; Kawabata, Takeshi

    1996-12-31

    This paper reports the preliminary results of experiments on listening to several sounds at once. Two issues are addressed: segregating speech streams from a mixture of sounds, and interfacing speech stream segregation with automatic speech recognition (ASR). Speech stream segregation (SSS) is modeled as a process of extracting harmonic fragments, grouping these extracted harmonic fragments, and substituting some sounds for non-harmonic parts of groups. This system is implemented by extending the harmonic-based stream segregation system reported at AAAI-94 and IJCAI-95. The main problem in interfacing SSS with HMM-based ASR is how to improve the recognition performance which is degraded by spectral distortion of segregated sounds caused mainly by the binaural input, grouping, and residue substitution. Our solution is to re-train the parameters of the HMM with training data binauralized for four directions, to group harmonic fragments according to their directions, and to substitute the residue of harmonic fragments for non-harmonic parts of each group. Experiments with 500 mixtures of two women`s utterances of a word showed that the cumulative accuracy of word recognition up to the 10th candidate of each woman`s utterance is, on average, 75%.

  19. Action Recognition and Movement Direction Discrimination Tasks Are Associated with Different Adaptation Patterns.

    PubMed

    de la Rosa, Stephan; Ekramnia, Mina; Bülthoff, Heinrich H

    2016-01-01

    The ability to discriminate between different actions is essential for action recognition and social interactions. Surprisingly previous research has often probed action recognition mechanisms with tasks that did not require participants to discriminate between actions, e.g., left-right direction discrimination tasks. It is not known to what degree visual processes in direction discrimination tasks are also involved in the discrimination of actions, e.g., when telling apart a handshake from a high-five. Here, we examined whether action discrimination is influenced by movement direction and whether direction discrimination depends on the type of action. We used an action adaptation paradigm to target action and direction discrimination specific visual processes. In separate conditions participants visually adapted to forward and backward moving handshake and high-five actions. Participants subsequently categorized either the action or the movement direction of an ambiguous action. The results showed that direction discrimination adaptation effects were modulated by the type of action but action discrimination adaptation effects were unaffected by movement direction. These results suggest that action discrimination and direction categorization rely on partly different visual information. We propose that action discrimination tasks should be considered for the exploration of visual action recognition mechanisms.

  20. A smart pattern recognition system for the automatic identification of aerospace acoustic sources

    NASA Technical Reports Server (NTRS)

    Cabell, R. H.; Fuller, C. R.

    1989-01-01

    An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.

  1. Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora

    PubMed Central

    Groza, Tudor; Köhler, Sebastian; Doelken, Sandra; Collier, Nigel; Oellrich, Anika; Smedley, Damian; Couto, Francisco M; Baynam, Gareth; Zankl, Andreas; Robinson, Peter N.

    2015-01-01

    Concept recognition tools rely on the availability of textual corpora to assess their performance and enable the identification of areas for improvement. Typically, corpora are developed for specific purposes, such as gene name recognition. Gene and protein name identification are longstanding goals of biomedical text mining, and therefore a number of different corpora exist. However, phenotypes only recently became an entity of interest for specialized concept recognition systems, and hardly any annotated text is available for performance testing and training. Here, we present a unique corpus, capturing text spans from 228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. Furthermore, we developed a test suite for standardized concept recognition error analysis, incorporating 32 different types of test cases corresponding to 2164 HPO concepts. Finally, three established phenotype concept recognizers (NCBO Annotator, OBO Annotator and Bio-LarK CR) were comprehensively evaluated, and results are reported against both the text corpus and the test suites. The gold standard and test suites corpora are available from http://bio-lark.org/hpo_res.html. Database URL: http://bio-lark.org/hpo_res.html PMID:25725061

  2. Automaticity of Basic-Level Categorization Accounts for Labeling Effects in Visual Recognition Memory

    ERIC Educational Resources Information Center

    Richler, Jennifer J.; Gauthier, Isabel; Palmeri, Thomas J.

    2011-01-01

    Are there consequences of calling objects by their names? Lupyan (2008) suggested that overtly labeling objects impairs subsequent recognition memory because labeling shifts stored memory representations of objects toward the category prototype (representational shift hypothesis). In Experiment 1, we show that processing objects at the basic…

  3. User Experience of a Mobile Speaking Application with Automatic Speech Recognition for EFL Learning

    ERIC Educational Resources Information Center

    Ahn, Tae youn; Lee, Sangmin-Michelle

    2016-01-01

    With the spread of mobile devices, mobile phones have enormous potential regarding their pedagogical use in language education. The goal of this study is to analyse user experience of a mobile-based learning system that is enhanced by speech recognition technology for the improvement of EFL (English as a foreign language) learners' speaking…

  4. The application of automatic recognition techniques in the Apollo 9 SO-65 experiment

    NASA Technical Reports Server (NTRS)

    Macdonald, R. B.

    1970-01-01

    A synoptic feature analysis is reported on Apollo 9 remote earth surface photographs that uses the methods of statistical pattern recognition to classify density points and clusterings in digital conversion of optical data. A computer derived geological map of a geological test site indicates that geological features of the range are separable, but that specific rock types are not identifiable.

  5. An adaptation strategy of using LDA classifier for EMG pattern recognition.

    PubMed

    Zhang, Haoshi; Zhao, Yaonan; Yao, Fuan; Xu, Lisheng; Shang, Peng; Li, Guanglin

    2013-01-01

    The time-varying character of myoelectric signal usually causes a low classification accuracy in traditional supervised pattern recognition method. In this work, an unsupervised adaptation strategy of linear discriminant analysis (ALDA) based on probability weighting and cycle substitution was suggested in order to improve the performance of electromyography (EMG)-based motion classification in multifunctional myoelectric prostheses control in changing environment. The adaptation procedure was firstly introduced, and then the proposed ALDA classifier was trained and tested with surface EMG recordings related to multiple motion patterns. The accuracies of the ALDA classifier and traditional LDA classifier were compared when the EMG recordings were added with different degrees of noise. The experimental results showed that compared to the LDA method, the suggested ALDA method had a better performance in improving the classification accuracy of sEMG pattern recognition, in both stable situation and noise added situation.

  6. Evaluation of Model Recognition for Grammar-Based Automatic 3d Building Model Reconstruction

    NASA Astrophysics Data System (ADS)

    Yu, Qian; Helmholz, Petra; Belton, David

    2016-06-01

    In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.

  7. Audiovisual cues benefit recognition of accented speech in noise but not perceptual adaptation.

    PubMed

    Banks, Briony; Gowen, Emma; Munro, Kevin J; Adank, Patti

    2015-01-01

    Perceptual adaptation allows humans to recognize different varieties of accented speech. We investigated whether perceptual adaptation to accented speech is facilitated if listeners can see a speaker's facial and mouth movements. In Study 1, participants listened to sentences in a novel accent and underwent a period of training with audiovisual or audio-only speech cues, presented in quiet or in background noise. A control group also underwent training with visual-only (speech-reading) cues. We observed no significant difference in perceptual adaptation between any of the groups. To address a number of remaining questions, we carried out a second study using a different accent, speaker and experimental design, in which participants listened to sentences in a non-native (Japanese) accent with audiovisual or audio-only cues, without separate training. Participants' eye gaze was recorded to verify that they looked at the speaker's face during audiovisual trials. Recognition accuracy was significantly better for audiovisual than for audio-only stimuli; however, no statistical difference in perceptual adaptation was observed between the two modalities. Furthermore, Bayesian analysis suggested that the data supported the null hypothesis. Our results suggest that although the availability of visual speech cues may be immediately beneficial for recognition of unfamiliar accented speech in noise, it does not improve perceptual adaptation.

  8. Audiovisual cues benefit recognition of accented speech in noise but not perceptual adaptation.

    PubMed

    Banks, Briony; Gowen, Emma; Munro, Kevin J; Adank, Patti

    2015-01-01

    Perceptual adaptation allows humans to recognize different varieties of accented speech. We investigated whether perceptual adaptation to accented speech is facilitated if listeners can see a speaker's facial and mouth movements. In Study 1, participants listened to sentences in a novel accent and underwent a period of training with audiovisual or audio-only speech cues, presented in quiet or in background noise. A control group also underwent training with visual-only (speech-reading) cues. We observed no significant difference in perceptual adaptation between any of the groups. To address a number of remaining questions, we carried out a second study using a different accent, speaker and experimental design, in which participants listened to sentences in a non-native (Japanese) accent with audiovisual or audio-only cues, without separate training. Participants' eye gaze was recorded to verify that they looked at the speaker's face during audiovisual trials. Recognition accuracy was significantly better for audiovisual than for audio-only stimuli; however, no statistical difference in perceptual adaptation was observed between the two modalities. Furthermore, Bayesian analysis suggested that the data supported the null hypothesis. Our results suggest that although the availability of visual speech cues may be immediately beneficial for recognition of unfamiliar accented speech in noise, it does not improve perceptual adaptation. PMID:26283946

  9. Audiovisual cues benefit recognition of accented speech in noise but not perceptual adaptation

    PubMed Central

    Banks, Briony; Gowen, Emma; Munro, Kevin J.; Adank, Patti

    2015-01-01

    Perceptual adaptation allows humans to recognize different varieties of accented speech. We investigated whether perceptual adaptation to accented speech is facilitated if listeners can see a speaker’s facial and mouth movements. In Study 1, participants listened to sentences in a novel accent and underwent a period of training with audiovisual or audio-only speech cues, presented in quiet or in background noise. A control group also underwent training with visual-only (speech-reading) cues. We observed no significant difference in perceptual adaptation between any of the groups. To address a number of remaining questions, we carried out a second study using a different accent, speaker and experimental design, in which participants listened to sentences in a non-native (Japanese) accent with audiovisual or audio-only cues, without separate training. Participants’ eye gaze was recorded to verify that they looked at the speaker’s face during audiovisual trials. Recognition accuracy was significantly better for audiovisual than for audio-only stimuli; however, no statistical difference in perceptual adaptation was observed between the two modalities. Furthermore, Bayesian analysis suggested that the data supported the null hypothesis. Our results suggest that although the availability of visual speech cues may be immediately beneficial for recognition of unfamiliar accented speech in noise, it does not improve perceptual adaptation. PMID:26283946

  10. The development of adaptive decision making: Recognition-based inference in children and adolescents.

    PubMed

    Horn, Sebastian S; Ruggeri, Azzurra; Pachur, Thorsten

    2016-09-01

    Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment, particularly the predictive power (validity) of recognition. Little is known about developmental differences in use of the RH. In this study, the authors examined (a) to what extent children and adolescents recruit the RH when making judgments, and (b) around what age adaptive use of the RH emerges. Primary schoolchildren (M = 9 years), younger adolescents (M = 12 years), and older adolescents (M = 17 years) made comparative judgments in task environments with either high or low recognition validity. Reliance on the RH was measured with a hierarchical multinomial model. Results indicated that primary schoolchildren already made systematic use of the RH. However, only older adolescents adaptively adjusted their strategy use between environments and were better able to discriminate between situations in which the RH led to correct versus incorrect inferences. These findings suggest that the use of simple heuristics does not progress unidirectionally across development but strongly depends on the task environment, in line with the perspective of ecological rationality. Moreover, adaptive heuristic inference seems to require experience and a developed base of domain knowledge. (PsycINFO Database Record PMID:27505696

  11. Real-Time Reconfigurable Adaptive Speech Recognition Command and Control Apparatus and Method

    NASA Technical Reports Server (NTRS)

    Salazar, George A. (Inventor); Haynes, Dena S. (Inventor); Sommers, Marc J. (Inventor)

    1998-01-01

    An adaptive speech recognition and control system and method for controlling various mechanisms and systems in response to spoken instructions and in which spoken commands are effective to direct the system into appropriate memory nodes, and to respective appropriate memory templates corresponding to the voiced command is discussed. Spoken commands from any of a group of operators for which the system is trained may be identified, and voice templates are updated as required in response to changes in pronunciation and voice characteristics over time of any of the operators for which the system is trained. Provisions are made for both near-real-time retraining of the system with respect to individual terms which are determined not be positively identified, and for an overall system training and updating process in which recognition of each command and vocabulary term is checked, and in which the memory templates are retrained if necessary for respective commands or vocabulary terms with respect to an operator currently using the system. In one embodiment, the system includes input circuitry connected to a microphone and including signal processing and control sections for sensing the level of vocabulary recognition over a given period and, if recognition performance falls below a given level, processing audio-derived signals for enhancing recognition performance of the system.

  12. Automatic Detection and Recognition of Man-Made Objects in High Resolution Remote Sensing Images Using Hierarchical Semantic Graph Model

    NASA Astrophysics Data System (ADS)

    Sun, X.; Thiele, A.; Hinz, S.; Fu, K.

    2013-05-01

    In this paper, we propose a hierarchical semantic graph model to detect and recognize man-made objects in high resolution remote sensing images automatically. Following the idea of part-based methods, our model builds a hierarchical possibility framework to explore both the appearance information and semantic relationships between objects and background. This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. After training local classifiers to calculate parts properties, we use belief propagation to transmit messages quantitatively, which could enhance the utilization of spatial constrains existed in images. Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. The experimental results demonstrate that this method is able to detect manmade objects in complicated surroundings with satisfactory precision and robustness.

  13. A fully automatic framework for cell segmentation on non-confocal adaptive optics images

    NASA Astrophysics Data System (ADS)

    Liu, Jianfei; Dubra, Alfredo; Tam, Johnny

    2016-03-01

    By the time most retinal diseases are diagnosed, macroscopic irreversible cellular loss has already occurred. Earlier detection of subtle structural changes at the single photoreceptor level is now possible, using the adaptive optics scanning light ophthalmoscope (AOSLO). This work aims to develop a fully automatic segmentation framework to extract cell boundaries from non-confocal split-detection AOSLO images of the cone photoreceptor mosaic in the living human eye. Significant challenges include anisotropy, heterogeneous cell regions arising from shading effects, and low contrast between cells and background. To overcome these challenges, we propose the use of: 1) multi-scale Hessian response to detect heterogeneous cell regions, 2) convex hulls to create boundary templates, and 3) circularlyconstrained geodesic active contours to refine cell boundaries. We acquired images from three healthy subjects at eccentric retinal regions and manually contoured cells to generate ground-truth for evaluating segmentation accuracy. Dice coefficient, relative absolute area difference, and average contour distance were 82±2%, 11±6%, and 2.0±0.2 pixels (Mean±SD), respectively. We find that strong shading effects from vessels are a main factor that causes cell oversegmentation and false segmentation of non-cell regions. Our segmentation algorithm can automatically and accurately segment photoreceptor cells on non-confocal AOSLO images, which is the first step in longitudinal tracking of cellular changes in the individual eye over the time course of disease progression.

  14. Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals.

    PubMed

    Dadashi, Farzin; Crettenand, Florent; Millet, Grégoire P; Seifert, Ludovic; Komar, John; Aminian, Kamiar

    2013-01-01

    This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 ± 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 ± 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.

  15. Implementation of automatic white-balance based on adaptive-luminance

    NASA Astrophysics Data System (ADS)

    Zhong, Jian; Yao, Su-Ying; Xu, Jiang-Tao

    2009-03-01

    A novel automatic white-balance algorithm based on adaptive-luminance is proposed in this paper. This algorithm redefines the gray pixels region, which can filter the gray pixels accurately. Furthermore, with the relations between gray pixels’ luminance with standard light source and their chroma Cb, Cr shifts with other color temperatures, the algorithm establishes the equations between the captured pixels and the original ones, which can estimate the gains of RGB channels exactly. To evaluate the proposed algorithm, the objective comparison method and the subjective observation method are both used, and the test results prove that the effects of image emendated by the proposed algorithm are excelled to that by traditional algorithms. Finally, the algorithm is implemented with VLSI design, and the result of synthesis proves that it can satisfy real-time application.

  16. Automatic ultrasonic imaging system with adaptive-learning-network signal-processing techniques

    SciTech Connect

    O'Brien, L.J.; Aravanis, N.A.; Gouge, J.R. Jr.; Mucciardi, A.N.; Lemon, D.K.; Skorpik, J.R.

    1982-04-01

    A conventional pulse-echo imaging system has been modified to operate with a linear ultrasonic array and associated digital electronics to collect data from a series of defects fabricated in aircraft quality steel blocks. A thorough analysis of the defect responses recorded with this modified system has shown that considerable improvements over conventional imaging approaches can be obtained in the crucial areas of defect detection and characterization. A combination of advanced signal processing concepts with the Adaptive Learning Network (ALN) methodology forms the basis for these improvements. Use of established signal processing algorithms such as temporal and spatial beam-forming in concert with a sophisticated detector has provided a reliable defect detection scheme which can be implemented in a microprocessor-based system to operate in an automatic mode.

  17. Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms.

    PubMed

    Arodź, Tomasz; Kurdziel, Marcin; Sevre, Erik O D; Yuen, David A

    2005-08-01

    We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as well. In order to achieve a higher accuracy for this method, we should choose image features that are better suited for analysing digital mammograms than the currently used ones. PMID:15925425

  18. Image automatic-recognition scheme for dice game using structure features technique

    NASA Astrophysics Data System (ADS)

    Chen, Wen-Yuan; Chung, Chin-Ho

    2011-08-01

    In dice recognition, there are several situations that may cause dice image identification problems, including the color of background being the same as the dice, the shape of the dice being round, or the dice touching each other. Especially when the camera zooms in or out, the pip sizes become irregular. We analyze the dice structure features to develop an intelligent memorized least-distance search method (IMLDSM) that can achieve accurate dice detection. Compared to other schemes, our method has two major advantages: (i) Unlike other schemes using complicated classification methods to segment the dice, we adopt a novel, simple, and effective method to analyze dice structure features to achieve more accurate recognition results and (ii) the IMLDSM solves the problems of image zooming in and out, which other methods cannot. After over 250 test images that include different shapes, styles, sizes, and colors used in simulations, this scheme is proven to achieve 100% dice image identification.

  19. [A new automatic quasars recognition technique based on PCA and Hough transform].

    PubMed

    Huang, Ling-yun; Hu, Zhan-yi

    2003-02-01

    The main purpose of quasar recognition is to determine the observed quasar spectrum's redshift value. Previously the template of quasar rest frame in the literature was basically constructed based on astronomers' hypotheses. Due to the inaccuracy of such a template, it is hard to determine the redshift value by matching the observed quasar spectrum with the template directly. This paper's main contributions are two-fold: Firstly, the template in our paper is constructed by the principal component analysis (PCA) method from some selected spectra with known redshift values, hence the obtained template is more realistic. Secondly, a 2D standard Hough transform, rather than a 1D Hough transform, is used. This is because although only redshift needs to be determined in our system, based on our observations, the magnitude of emission peak is also changed, hence a new parameter, namely scale parameter, is also introduced to the Hough transform to enhance the reliability of the recognition. The experiments show that the proposed technique is workable and the correct recognition rate can reach about as high as 90%.

  20. Massively parallel network architectures for automatic recognition of visual speech signals. Final technical report

    SciTech Connect

    Sejnowski, T.J.; Goldstein, M.

    1990-01-01

    This research sought to produce a massively-parallel network architecture that could interpret speech signals from video recordings of human talkers. This report summarizes the project's results: (1) A corpus of video recordings from two human speakers was analyzed with image processing techniques and used as the data for this study; (2) We demonstrated that a feed forward network could be trained to categorize vowels from these talkers. The performance was comparable to that of the nearest neighbors techniques and to trained humans on the same data; (3) We developed a novel approach to sensory fusion by training a network to transform from facial images to short-time spectral amplitude envelopes. This information can be used to increase the signal-to-noise ratio and hence the performance of acoustic speech recognition systems in noisy environments; (4) We explored the use of recurrent networks to perform the same mapping for continuous speech. Results of this project demonstrate the feasibility of adding a visual speech recognition component to enhance existing speech recognition systems. Such a combined system could be used in noisy environments, such as cockpits, where improved communication is needed. This demonstration of presymbolic fusion of visual and acoustic speech signals is consistent with our current understanding of human speech perception.

  1. Ecological adaptation and species recognition drives vocal evolution in neotropical suboscine birds.

    PubMed

    Seddon, Nathalie

    2005-01-01

    Given that evolutionary divergence in mating signals leads to reproductive isolation in numerous animal taxa, understanding what drives signal divergence is fundamental to our understanding of speciation. Mating signals are thought to diverge via several processes, including (1) as a by-product of morphological adaptation, (2) through direct adaptation to the signaling environment, or (3) to facilitate species recognition. According to the first two hypotheses, birdsongs diversify in different foraging niches and habitats as a product of selection for optimal morphology and efficient sound transmission, respectively. According to the third hypothesis, they diversify as a result of selection against maladaptive hybridization. In this study I test all three hypotheses by examining the influence of morphology, acoustic environment, and the presence of closely related congeners on song structure in 163 species of antbird (Thamnophilidae). Unlike oscine passerines, these Neotropical suboscines make ideal subjects because they develop their songs without learning. In other words, patterns of vocal divergence are not complicated by cultural evolution. In support of the morphological adaptation hypothesis, body mass correlates with the acoustic frequency of songs, and bill size with temporal patterning. These relationships were robust, even when controlling for phylogenetic inertia using independent contrasts, suggesting that there has been correlated evolution between morphological and acoustic traits. The results also support the acoustic adaptation hypothesis: birds which habitually sing in the understory and canopy produce higher-pitched songs than those that sing in the midstory, suggesting that song structure is related to the sound transmission properties of different habitat strata. Finally, the songs of sympatric pairs of closely related species are more divergent than those of allopatric pairs, as predicted by the species recognition hypothesis. To my knowledge

  2. Ecological adaptation and species recognition drives vocal evolution in neotropical suboscine birds.

    PubMed

    Seddon, Nathalie

    2005-01-01

    Given that evolutionary divergence in mating signals leads to reproductive isolation in numerous animal taxa, understanding what drives signal divergence is fundamental to our understanding of speciation. Mating signals are thought to diverge via several processes, including (1) as a by-product of morphological adaptation, (2) through direct adaptation to the signaling environment, or (3) to facilitate species recognition. According to the first two hypotheses, birdsongs diversify in different foraging niches and habitats as a product of selection for optimal morphology and efficient sound transmission, respectively. According to the third hypothesis, they diversify as a result of selection against maladaptive hybridization. In this study I test all three hypotheses by examining the influence of morphology, acoustic environment, and the presence of closely related congeners on song structure in 163 species of antbird (Thamnophilidae). Unlike oscine passerines, these Neotropical suboscines make ideal subjects because they develop their songs without learning. In other words, patterns of vocal divergence are not complicated by cultural evolution. In support of the morphological adaptation hypothesis, body mass correlates with the acoustic frequency of songs, and bill size with temporal patterning. These relationships were robust, even when controlling for phylogenetic inertia using independent contrasts, suggesting that there has been correlated evolution between morphological and acoustic traits. The results also support the acoustic adaptation hypothesis: birds which habitually sing in the understory and canopy produce higher-pitched songs than those that sing in the midstory, suggesting that song structure is related to the sound transmission properties of different habitat strata. Finally, the songs of sympatric pairs of closely related species are more divergent than those of allopatric pairs, as predicted by the species recognition hypothesis. To my knowledge

  3. Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

    NASA Astrophysics Data System (ADS)

    Cui, Chen; Asari, Vijayan K.

    2014-03-01

    Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image

  4. Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

    PubMed

    Schädler, Marc René; Kollmeier, Birger

    2015-04-01

    To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance. PMID:25920855

  5. Automatic classification of sulcal regions of the human brain cortex using pattern recognition

    NASA Astrophysics Data System (ADS)

    Behnke, Kirsten J.; Rettmann, Maryam E.; Pham, Dzung L.; Shen, Dinggang; Resnick, Susan M.; Davatzikos, Christos; Prince, Jerry L.

    2003-05-01

    Parcellation of the cortex has received a great deal of attention in magnetic resonance (MR) image analysis, but its usefulness has been limited by time-consuming algorithms that require manual labeling. An automatic labeling scheme is necessary to accurately and consistently parcellate a large number of brains. The large variation of cortical folding patterns makes automatic labeling a challenging problem, which cannot be solved by deformable atlas registration alone. In this work, an automated classification scheme that consists of a mix of both atlas driven and data driven methods is proposed to label the sulcal regions, which are defined as the gray matter regions of the cortical surface surrounding each sulcus. The premise for this algorithm is that sulcal regions can be classified according to the pattern of anatomical features (e.g. supramarginal gyrus, cuneus, etc.) associated with each region. Using a nearest-neighbor approach, a sulcal region is classified as being in the same class as the sulcus from a set of training data which has the nearest pattern of anatomical features. Using just one subject as training data, the algorithm correctly labeled 83% of the regions that make up the main sulci of the cortex.

  6. Optical implementation of a feature-based neural network with application to automatic target recognition

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  7. Automatic target recognition using a feature-based optical neural network

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin

    1992-01-01

    An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. An automatic locally-adaptive method to estimate heavily-tailed breakthrough curves from particle distributions

    NASA Astrophysics Data System (ADS)

    Pedretti, Daniele; Fernàndez-Garcia, Daniel

    2013-09-01

    Particle tracking methods to simulate solute transport deal with the issue of having to reconstruct smooth concentrations from a limited number of particles. This is an error-prone process that typically leads to large fluctuations in the determined late-time behavior of breakthrough curves (BTCs). Kernel density estimators (KDE) can be used to automatically reconstruct smooth BTCs from a small number of particles. The kernel approach incorporates the uncertainty associated with subsampling a large population by equipping each particle with a probability density function. Two broad classes of KDE methods can be distinguished depending on the parametrization of this function: global and adaptive methods. This paper shows that each method is likely to estimate a specific portion of the BTCs. Although global methods offer a valid approach to estimate early-time behavior and peak of BTCs, they exhibit important fluctuations at the tails where fewer particles exist. In contrast, locally adaptive methods improve tail estimation while oversmoothing both early-time and peak concentrations. Therefore a new method is proposed combining the strength of both KDE approaches. The proposed approach is universal and only needs one parameter (α) which slightly depends on the shape of the BTCs. Results show that, for the tested cases, heavily-tailed BTCs are properly reconstructed with α ≈ 0.5 .

  10. Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.

    PubMed

    Cunefare, David; Cooper, Robert F; Higgins, Brian; Katz, David F; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina

    2016-05-01

    Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images.

  11. Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images

    PubMed Central

    Cunefare, David; Cooper, Robert F.; Higgins, Brian; Katz, David F.; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina

    2016-01-01

    Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice’s coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice’s coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images. PMID:27231641

  12. An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles.

    PubMed

    Johansson, A Torbjorn; White, Paul R

    2011-08-01

    This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances. PMID:21877804

  13. Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters

    PubMed Central

    2014-01-01

    Background Ontological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem. Results Three dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus. Over 1,000 parameter combinations are examined, and best-performing parameters for each system-ontology pair are presented. Conclusions Baselines for concept recognition by three systems on eight biomedical ontologies are established (F-measures range from 0.14–0.83). Out of the three systems we tested, ConceptMapper is generally the best-performing system; it produces the highest F-measure of seven out of eight ontologies. Default parameters are not ideal for most systems on most ontologies; by changing parameters F-measure can be increased by up to 0.4. Not only are best performing parameters presented, but suggestions for choosing the best parameters based on ontology characteristics are presented. PMID:24571547

  14. Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor.

    PubMed

    Lee, Jonguk; Jin, Long; Park, Daihee; Chung, Yongwha

    2016-01-01

    Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods. PMID:27144572

  15. Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor

    PubMed Central

    Lee, Jonguk; Jin, Long; Park, Daihee; Chung, Yongwha

    2016-01-01

    Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods. PMID:27144572

  16. Adaptive Recognition of Phonemes from Speaker - Connected-Speech Using Alisa.

    NASA Astrophysics Data System (ADS)

    Osella, Stephen Albert

    The purpose of this dissertation research is to investigate a novel approach to automatic speech recognition (ASR). The successes that have been achieved in ASR have relied heavily on the use of a language grammar, which significantly constrains the ASR process. By using grammar to provide most of the recognition ability, the ASR system does not have to be as accurate at the low-level recognition stage. The ALISA Phonetic Transcriber (APT) algorithm is proposed as a way to improve ASR by enhancing the lowest -level recognition stage. The objective of the APT algorithm is to classify speech frames (a short sequence of speech signal samples) into a small set of phoneme classes. The APT algorithm constructs the mapping from speech frames to phoneme labels through a multi-layer feedforward process. A design principle of APT is that final decisions are delayed as long as possible. Instead of attempting to optimize the decision making at each processing level individually, each level generates a list of candidate solutions that are passed on to the next level of processing. The later processing levels use these candidate solutions to resolve ambiguities. The scope of this dissertation is the design of the APT algorithm up to the speech-frame classification stage. In future research, the APT algorithm will be extended to the word recognition stage. In particular, the APT algorithm could serve as the front-end stage to a Hidden Markov Model (HMM) based word recognition system. In such a configuration, the APT algorithm would provide the HMM with the requisite phoneme state-probability estimates. To date, the APT algorithm has been tested with the TIMIT and NTIMIT speech databases. The APT algorithm has been trained and tested on the SX and SI sentence texts using both male and female speakers. Results indicate better performance than those results obtained using a neural network based speech-frame classifier. The performance of the APT algorithm has been evaluated for

  17. Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

    SciTech Connect

    Peroni, Marta; Ciardo, Delia; Spadea, Maria Francesca; Riboldi, Marco; Comi, Stefania; Alterio, Daniela; Baroni, Guido; Orecchia, Roberto

    2012-11-01

    Purpose: The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. Method: We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Results: Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. Conclusion: The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

  18. Scene segmentation from motion in multispectral imagery to aid automatic human gait recognition

    NASA Astrophysics Data System (ADS)

    Pearce, Daniel; Harvey, Christophe; Day, Simon; Goffredo, Michela

    2007-10-01

    Primarily focused at military and security environments where there is a need to identify humans covertly and remotely; this paper outlines how recovering human gait biometrics from a multi-spectral imaging system can overcome the failings of traditional biometrics to fulfil those needs. With the intention of aiding single camera human gait recognition, an algorithm was developed to accurately segment a walking human from multi-spectral imagery. 16-band imagery from the image replicating imaging spectrometer (IRIS) camera system is used to overcome some of the common problems associated with standard change detection techniques. Fusing the concepts of scene segmentation by spectral characterisation and background subtraction by image differencing gives a uniquely robust approach. This paper presents the results of real trials with human subjects and a prototype IRIS camera system, and compares performance to typical broadband camera systems.

  19. Automatic speech recognition and training for severely dysarthric users of assistive technology: the STARDUST project.

    PubMed

    Parker, Mark; Cunningham, Stuart; Enderby, Pam; Hawley, Mark; Green, Phil

    2006-01-01

    The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent proximity to "normal" articulatory patterns. Severe dysarthric output may also be characterized by a small mass of distinguishable phonetic tokens making the acoustic differentiation of target words difficult. Speaker dependent computer speech recognition using Hidden Markov Models was achieved by the identification of robust phonetic elements within the individual speaker output patterns. A new system of speech training using computer generated visual and auditory feedback reduced the inconsistent production of key phonetic tokens over time.

  20. Stereovision-based 3D field recognition for automatic guidance system of off-road vehicle

    NASA Astrophysics Data System (ADS)

    Zhang, Fangming; Ying, Yibin; Shen, Chuan; Jiang, Huanyu; Zhang, Qin

    2005-11-01

    A stereovision-based disparity evaluation algorithm was developed for rice crop field recognition. The gray level intensities and the correlation relation were integrated to produce the disparities of stereo-images. The surface of ground and rice were though as two rough planes, but their disparities waved in a narrow range. The cut/uncut edges of rice crops were first detected and track through the images. We used a step model to locate those edge positions. The points besides the edges were matched respectively to get disparity values using area correlation method. The 3D camera coordinates were computed based on those disparities. The vehicle coordinates were obtained by multiplying the 3D camera coordinates with a transform formula. It has been implemented on an agricultural robot and evaluated in rice crop field with straight rows. The results indicated that the developed stereovision navigation system is capable of reconstructing the field image.

  1. Automaticity of basic-level categorization accounts for labeling effects in visual recognition memory.

    PubMed

    Richler, Jennifer J; Gauthier, Isabel; Palmeri, Thomas J

    2011-11-01

    Are there consequences of calling objects by their names? Lupyan (2008) suggested that overtly labeling objects impairs subsequent recognition memory because labeling shifts stored memory representations of objects toward the category prototype (representational shift hypothesis). In Experiment 1, we show that processing objects at the basic category level versus exemplar level in the absence of any overt labeling produces the same qualitative pattern of results. Experiment 2 demonstrates that labeling does not always disrupt memory as predicted by the representational shift hypothesis: Differences in memory following labeling versus preference are more likely an effect of judging preference, not an effect of overt labeling. Labeling does not influence memory by shifting memory representations toward the category prototype. Rather, labeling objects at the basic level produces memory representations that are simply less robust than those produced by other kinds of study tasks.

  2. Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study.

    PubMed

    Skeppstedt, Maria; Kvist, Maria; Nilsson, Gunnar H; Dalianis, Hercules

    2014-06-01

    Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder+Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that

  3. Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study.

    PubMed

    Skeppstedt, Maria; Kvist, Maria; Nilsson, Gunnar H; Dalianis, Hercules

    2014-06-01

    Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder+Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that

  4. Self-adaptive grain recognition of diamond grinding wheel and its grains assessment

    NASA Astrophysics Data System (ADS)

    Cui, Changcai; Zhou, Lijun; Yu, Qing; Huang, Hui; Ye, Ruifang

    2013-10-01

    An improved Canny operator based on the method of Maximum Classes Square Error is adopted to get a self-adaptive threshold for grain recognition. First, a grinding wheel surface was measured by using a vertical scanning white light interferometric (WLI) system and reconstructed with an improved centroid algorithm; then the grains were extracted using the proposed method based on the fact that the peak intensity difference (ΔI) between maximum and minimum intensities on interferometric curve from diamond is larger than that from bond due to different reflective characteristics of different materials; third the grain protrusion parameters are investigated for grinding performance analysis. The experiments proved that the proposed grain recognition method is effective and assessment parameters are useful for understanding grinding performance.

  5. Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs.

    PubMed

    Li, Nan; Zarepisheh, Masoud; Uribe-Sanchez, Andres; Moore, Kevin; Tian, Zhen; Zhen, Xin; Graves, Yan Jiang; Gautier, Quentin; Mell, Loren; Zhou, Linghong; Jia, Xun; Jiang, Steve

    2013-12-21

    Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient and effective re-planning algorithm is an important step toward the clinical realization of ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is time-consuming and is usually considered unpractical, especially for online ART. It is desirable to automate this step to yield a plan of acceptable quality with minimal interventions. In ART, prior information in the original plan is available, such as dose-volume histogram (DVH), which can be employed to facilitate the automatic re-planning process. The goal of this work is to develop an automatic re-planning algorithm to generate a plan with similar, or possibly better, DVH curves compared with the clinically delivered original plan. Specifically, our algorithm iterates the following two loops. An inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed voxel weighting factors. In outer loop, the voxel weighting factors in the objective function are adjusted according to the deviation of the current DVH curves from those in the original plan. The process is repeated until the DVH curves are acceptable or maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. The feasibility of our algorithm has been demonstrated with three head-and-neck cancer IMRT cases, each having an initial planning CT scan and another treatment CT scan acquired in the middle of treatment course. Compared with the DVH curves in the original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are better for almost all structures. The re-optimization process takes about 30 s using

  6. Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs

    NASA Astrophysics Data System (ADS)

    Li, Nan; Zarepisheh, Masoud; Uribe-Sanchez, Andres; Moore, Kevin; Tian, Zhen; Zhen, Xin; Jiang Graves, Yan; Gautier, Quentin; Mell, Loren; Zhou, Linghong; Jia, Xun; Jiang, Steve

    2013-12-01

    Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient and effective re-planning algorithm is an important step toward the clinical realization of ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is time-consuming and is usually considered unpractical, especially for online ART. It is desirable to automate this step to yield a plan of acceptable quality with minimal interventions. In ART, prior information in the original plan is available, such as dose-volume histogram (DVH), which can be employed to facilitate the automatic re-planning process. The goal of this work is to develop an automatic re-planning algorithm to generate a plan with similar, or possibly better, DVH curves compared with the clinically delivered original plan. Specifically, our algorithm iterates the following two loops. An inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed voxel weighting factors. In outer loop, the voxel weighting factors in the objective function are adjusted according to the deviation of the current DVH curves from those in the original plan. The process is repeated until the DVH curves are acceptable or maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. The feasibility of our algorithm has been demonstrated with three head-and-neck cancer IMRT cases, each having an initial planning CT scan and another treatment CT scan acquired in the middle of treatment course. Compared with the DVH curves in the original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are better for almost all structures. The re-optimization process takes about 30 s using

  7. CRISPR Recognition Tool (CRT): a tool for automatic detection ofclustered regularly interspaced palindromic repeats

    SciTech Connect

    Bland, Charles; Ramsey, Teresa L.; Sabree, Fareedah; Lowe,Micheal; Brown, Kyndall; Kyrpides, Nikos C.; Hugenholtz, Philip

    2007-05-01

    Clustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes. CRT was compared to CRISPR detection tools, Patscan and Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT also demonstrated superior performance, especially for genomes containing large numbers of repeats. In this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, was shown to be a significant improvement over the current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(n) in space and O(nm/l) in time.

  8. Automatic Recognition of Type III Solar Radio Bursts in STEREO/WAVES Data

    NASA Astrophysics Data System (ADS)

    Lobzin, V. V.; Cairns, I. H.; Zaslavsky, A.

    2014-12-01

    Type III radio bursts are produced near the local electron plasma frequency and/or near its harmonic by fast electrons ejected from the solar active regions and moving through the corona and solar wind. These bursts have dynamic spectra with frequency rapidly falling with time. This paper presents two new methods developed to detect type III bursts automatically in the data from High Frequency Receiver (HFR) of the STEREO/WAVES radio instrument onboard the STEREO spacecraft. The first technique is applicable to the low-frequency band (HFR-1: 125 kHz to 1.975 MHz) only. This technique can possibly be implemented in onboard satellite software aimed at preliminary detection of bursts and identification of time intervals with relatively high solar activity. In the second technique the bursts are detected in both the low-frequency band and the high-frequency band (HFR-2: 2.025 MHz to 16.025 MHz), with the computational burden being higher by 1 order of magnitude as compared with that for the first technique. Preliminary tests of the method show that for the first technique the pobability to detect is quite high, Pd L = 72% ± 3%. The performance of the second technique is considerably higher, Pd L+H = 81%±1%, while the number of false alarms does not exceed 10% for one daily spectrum.

  9. Automatic recognition of Cenozoic calcareous nannofossils: a reliable tool for paleoceanographic studies

    NASA Astrophysics Data System (ADS)

    Barbarin, N.; Beaufort, L.; Gally, Y.; Buchet, N.; Ermini, M.

    2012-12-01

    Calcareous nannofossils are microscopic-scaled plates (mainly coccoliths) produced by marine phytoplankton. They are abundant in marine sediment, diversified through geologic time and sensitive to climatic/environmental changes. That's why they are often used in biostratigraphy and (paleo-)oceanographic/climatic reconstructions. We propose recent improvements on an automated system called SYRACO which is based essentially on artificial neural networks. Originally able to recognized around ten species from the Upper Pleistocene, this new system is now trained to recognize thousand of species from the Upper Eocene to actual species. It is now based on the use of several neural networks on a large database combined with a morphometric approach from statistical classifications. This new system is able to keep at least 90% of the nannofossils in a sample and filter out around 84% of the non-nannofossils. Moreover we added a special neural network coupled with macro for Florisphaera profunda, an important species in paleoceanography, which is difficult to indentify by neural network. As examples, we present applications nannofossil assemblage composition in core-tops of the Indian Ocean compared with human counts and in a core retrieved in the Gulf of Papua. These results show a very good correlation between human and automatic counts and a precise paleoceanogrphic dynamics.

  10. Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

    PubMed Central

    2013-01-01

    Dysarthria is a frequently occurring motor speech disorder which can be caused by neurological trauma, cerebral palsy, or degenerative neurological diseases. Because dysarthria affects phonation, articulation, and prosody, spoken communication of dysarthric speakers gets seriously restricted, affecting their quality of life and confidence. Assistive technology has led to the development of speech applications to improve the spoken communication of dysarthric speakers. In this field, this paper presents an approach to improve the accuracy of HMM-based speech recognition systems. Because phonatory dysfunction is a main characteristic of dysarthric speech, the phonemes of a dysarthric speaker are affected at different levels. Thus, the approach consists in finding the most suitable type of HMM topology (Bakis, Ergodic) for each phoneme in the speaker's phonetic repertoire. The topology is further refined with a suitable number of states and Gaussian mixture components for acoustic modelling. This represents a difference when compared with studies where a single topology is assumed for all phonemes. Finding the suitable parameters (topology and mixtures components) is performed with a Genetic Algorithm (GA). Experiments with a well-known dysarthric speech database showed statistically significant improvements of the proposed approach when compared with the single topology approach, even for speakers with severe dysarthria. PMID:24222784

  11. Estimation of phoneme-specific HMM topologies for the automatic recognition of dysarthric speech.

    PubMed

    Caballero-Morales, Santiago-Omar

    2013-01-01

    Dysarthria is a frequently occurring motor speech disorder which can be caused by neurological trauma, cerebral palsy, or degenerative neurological diseases. Because dysarthria affects phonation, articulation, and prosody, spoken communication of dysarthric speakers gets seriously restricted, affecting their quality of life and confidence. Assistive technology has led to the development of speech applications to improve the spoken communication of dysarthric speakers. In this field, this paper presents an approach to improve the accuracy of HMM-based speech recognition systems. Because phonatory dysfunction is a main characteristic of dysarthric speech, the phonemes of a dysarthric speaker are affected at different levels. Thus, the approach consists in finding the most suitable type of HMM topology (Bakis, Ergodic) for each phoneme in the speaker's phonetic repertoire. The topology is further refined with a suitable number of states and Gaussian mixture components for acoustic modelling. This represents a difference when compared with studies where a single topology is assumed for all phonemes. Finding the suitable parameters (topology and mixtures components) is performed with a Genetic Algorithm (GA). Experiments with a well-known dysarthric speech database showed statistically significant improvements of the proposed approach when compared with the single topology approach, even for speakers with severe dysarthria.

  12. Automatic recognition of anatomical regions in three-dimensional medical images.

    PubMed

    Tóth, Márton; Ruskó, László; Csébfalvi, Balázs

    2016-09-01

    This paper presents a method that detects anatomy regions in three-dimensional medical images. The method labels each axial slice of the image according to the anatomy region it belongs to. The detected regions are the head (and neck), the chest, the abdomen, the pelvis, and the legs. The proposed method consists of two main parts. The core of the algorithm is based on a two-dimensional feature extraction that is followed by a random forest classification. This recognition process achieves an overall accuracy of 91.5% in slice classification, but it cannot always provide fully consistent labeling. The subsequent post-processing step incorporates the expected sequence and size of the human anatomy regions in order to improve the accuracy of the labeling. In this part of the algorithm the detected anatomy regions (represented by Gaussian distributions) are fitted to the region probabilities provided by the random forest classifier. The proposed method was evaluated on a set of whole-body MR images. The results demonstrate that the accuracy of the labeling can be increased to 94.1% using the presented post-processing. In order to demonstrate the robustness of the proposed method it was applied to partial MRI scans of different sizes (cut from the whole-body examinations). According to the results the proposed method works reliably (91.3%) for partial body scans (having as little length as 35cm) as well. PMID:27433991

  13. FRankenstein becomes a cyborg: the automatic recombination and realignment of fold recognition models in CASP6.

    PubMed

    Kosinski, Jan; Gajda, Michal J; Cymerman, Iwona A; Kurowski, Michal A; Pawlowski, Marcin; Boniecki, Michal; Obarska, Agnieszka; Papaj, Grzegorz; Sroczynska-Obuchowicz, Paulina; Tkaczuk, Karolina L; Sniezynska, Paulina; Sasin, Joanna M; Augustyn, Anna; Bujnicki, Janusz M; Feder, Marcin

    2005-01-01

    In the course of CASP6, we generated models for all targets using a new version of the "FRankenstein's monster approach." Previously (in CASP5) we were able to build many very accurate full-atom models by selection and recombination of well-folded fragments obtained from crude fold recognition (FR) results, followed by optimization of the sequence-structure fit and assessment of alternative alignments on the structural level. This procedure was however very arduous, as most of the steps required extensive visual and manual input from the human modeler. Now, we have automated the most tedious steps, such as superposition of alternative models, extraction of best-scoring fragments, and construction of a hybrid "monster" structure, as well as generation of alternative alignments in the regions that remain poorly scored in the refined hybrid model. We have also included the ROSETTA method to construct those parts of the target for which no reasonable structures were generated by FR methods (such as long insertions and terminal extensions). The analysis of successes and failures of the current version of the FRankenstein approach in modeling of CASP6 targets reveals that the considerably streamlined and automated method performs almost as well as the initial, mostly manual version, which suggests that it may be a useful tool for accurate protein structure prediction even in the hands of nonexperts.

  14. New approach for automatic recognition of melanoma in profilometry: optimized feature selection using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Handels, Heinz; Ross, Th; Kreusch, J.; Wolff, H. H.; Poeppl, S. J.

    1998-06-01

    A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 X 4 mm2 at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micrometers . With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. In order to improve classification performance, a subsequent feature selection process is applied to determine the best possible subset of features. Genetic algorithms are optimized for the feature selection process, and results of different approaches are compared. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. In comparison to heuristic strategies and greedy algorithms, genetic algorithms show the best results for the feature selection problem. After feature selection, several architectures of feed forward neural networks with error back-propagation are evaluated. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result in all with an error rate of 2.3% was obtained with the nearest neighbor classifier.

  15. Adaptive optics system for fast automatic control of laser beam jitters in air

    NASA Astrophysics Data System (ADS)

    Grasso, Salvatore; Acernese, Fausto; Romano, Rocco; Barone, Fabrizio

    2010-04-01

    Adaptive Optics (AO) Systems can operate fast automatic control of laser beam jitters for several applications of basic research as well as for the improvement of industrial and medical devices. We here present our theoretical and experimental research showing the opportunity of suppressing laser beam geometrical fluctuations of higher order Hermite Gauss modes in interferometric Gravitational Waves (GW) antennas. This in turn allows to significantly reduce the noise that originates from the coupling of the laser source oscillations with the interferometer asymmetries and introduces the concrete possibility of overcoming the sensitivity limit of the GW antennas actually set at 10-23 1 Hz value. We have carried out the feasibility study of a novel AO System which performs effective laser jitters suppression in the 200 Hz bandwidth. It extracts the wavefront error signals in terms of Hermite Gauss (HG) coefficients and performs the wavefront correction using the Zernike polynomials. An experimental Prototype of the AO System has been implemented and tested in our laboratory at the University of Salerno and the results we have achieved fully confirm effectiveness and robustness of the control upon first and second order laser beam geometrical fluctuations, in good accordance with GW antennas requirements. Above all, we have measured 60 dB reduction of astigmatism and defocus modes at low frequency below 1 Hz and 20 dB reduction in the 200 Hz bandwidth.

  16. Automatic adaptive parameterization in local phase feature-based bone segmentation in ultrasound.

    PubMed

    Hacihaliloglu, Ilker; Abugharbieh, Rafeef; Hodgson, Antony J; Rohling, Robert N

    2011-10-01

    Intensity-invariant local phase features based on Log-Gabor filters have been recently shown to produce highly accurate localizations of bone surfaces from three-dimensional (3-D) ultrasound. A key challenge, however, remains in the proper selection of filter parameters, whose values have so far been chosen empirically and kept fixed for a given image. Since Log-Gabor filter responses widely change when varying the filter parameters, actual parameter selection can significantly affect the quality of extracted features. This article presents a novel method for contextual parameter selection that autonomously adapts to image content. Our technique automatically selects the scale, bandwidth and orientation parameters of Log-Gabor filters for optimizing local phase symmetry. The proposed approach incorporates principle curvature computed from the Hessian matrix and directional filter banks in a phase scale-space framework. Evaluations performed on carefully designed in vitro experiments demonstrate 35% improvement in accuracy of bone surface localization compared with empirically-set parameterization results. Results from a pilot in vivo study on human subjects, scanned in the operating room, show similar improvements.

  17. Automatic network-adaptive ultra-low-bit-rate video coding

    NASA Astrophysics Data System (ADS)

    Chien, Wei-Jung; Lam, Tuyet-Trang; Abousleman, Glen P.; Karam, Lina J.

    2006-05-01

    This paper presents a software-only, real-time video coder/decoder (codec) for use with low-bandwidth channels where the bandwidth is unknown or varies with time. The codec incorporates a modified JPEG2000 core and interframe predictive coding, and can operate with network bandwidths of less than 1 kbits/second. The encoder and decoder establish two virtual connections over a single IP-based communications link. The first connection is UDP/IP guaranteed throughput, which is used to transmit the compressed video stream in real time, while the second is TCP/IP guaranteed delivery, which is used for two-way control and compression parameter updating. The TCP/IP link serves as a virtual feedback channel and enables the decoder to instruct the encoder to throttle back the transmission bit rate in response to the measured packet loss ratio. It also enables either side to initiate on-the-fly parameter updates such as bit rate, frame rate, frame size, and correlation parameter, among others. The codec also incorporates frame-rate throttling whereby the number of frames decoded is adjusted based upon the available processing resources. Thus, the proposed codec is capable of automatically adjusting the transmission bit rate and decoding frame rate to adapt to any network scenario. Video coding results for a variety of network bandwidths and configurations are presented to illustrate the vast capabilities of the proposed video coding system.

  18. Individual differences in adaptive coding of face identity are linked to individual differences in face recognition ability.

    PubMed

    Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Hayward, William G; Ewing, Louise

    2014-06-01

    Despite their similarity as visual patterns, we can discriminate and recognize many thousands of faces. This expertise has been linked to 2 coding mechanisms: holistic integration of information across the face and adaptive coding of face identity using norms tuned by experience. Recently, individual differences in face recognition ability have been discovered and linked to differences in holistic coding. Here we show that they are also linked to individual differences in adaptive coding of face identity, measured using face identity aftereffects. Identity aftereffects correlated significantly with several measures of face-selective recognition ability. They also correlated marginally with own-race face recognition ability, suggesting a role for adaptive coding in the well-known other-race effect. More generally, these results highlight the important functional role of adaptive face-coding mechanisms in face expertise, taking us beyond the traditional focus on holistic coding mechanisms.

  19. Individual differences in adaptive coding of face identity are linked to individual differences in face recognition ability.

    PubMed

    Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Hayward, William G; Ewing, Louise

    2014-06-01

    Despite their similarity as visual patterns, we can discriminate and recognize many thousands of faces. This expertise has been linked to 2 coding mechanisms: holistic integration of information across the face and adaptive coding of face identity using norms tuned by experience. Recently, individual differences in face recognition ability have been discovered and linked to differences in holistic coding. Here we show that they are also linked to individual differences in adaptive coding of face identity, measured using face identity aftereffects. Identity aftereffects correlated significantly with several measures of face-selective recognition ability. They also correlated marginally with own-race face recognition ability, suggesting a role for adaptive coding in the well-known other-race effect. More generally, these results highlight the important functional role of adaptive face-coding mechanisms in face expertise, taking us beyond the traditional focus on holistic coding mechanisms. PMID:24684315

  20. Automatic recognition of fin and blue whale calls for real-time monitoring in the St. Lawrence.

    PubMed

    Mouy, Xavier; Bahoura, Mohammed; Simard, Yvan

    2009-12-01

    Monitoring blue and fin whales summering in the St. Lawrence Estuary with passive acoustics requires call recognition algorithms that can cope with the heavy shipping noise of the St. Lawrence Seaway and with multipath propagation characteristics that generate overlapping copies of the calls. In this paper, the performance of three time-frequency methods aiming at such automatic detection and classification is tested on more than 2000 calls and compared at several levels of signal-to-noise ratio using typical recordings collected in this area. For all methods, image processing techniques are used to reduce the noise in the spectrogram. The first approach consists in matching the spectrogram with binary time-frequency templates of the calls (coincidence of spectrograms). The second approach is based on the extraction of the frequency contours of the calls and their classification using dynamic time warping (DTW) and the vector quantization (VQ) algorithms. The coincidence of spectrograms was the fastest method and performed better for blue whale A and B calls. VQ detected more 20 Hz fin whale calls but with a higher false alarm rate. DTW and VQ outperformed for the more variable blue whale D calls. PMID:20000904

  1. Automatic recognition of fin and blue whale calls for real-time monitoring in the St. Lawrence.

    PubMed

    Mouy, Xavier; Bahoura, Mohammed; Simard, Yvan

    2009-12-01

    Monitoring blue and fin whales summering in the St. Lawrence Estuary with passive acoustics requires call recognition algorithms that can cope with the heavy shipping noise of the St. Lawrence Seaway and with multipath propagation characteristics that generate overlapping copies of the calls. In this paper, the performance of three time-frequency methods aiming at such automatic detection and classification is tested on more than 2000 calls and compared at several levels of signal-to-noise ratio using typical recordings collected in this area. For all methods, image processing techniques are used to reduce the noise in the spectrogram. The first approach consists in matching the spectrogram with binary time-frequency templates of the calls (coincidence of spectrograms). The second approach is based on the extraction of the frequency contours of the calls and their classification using dynamic time warping (DTW) and the vector quantization (VQ) algorithms. The coincidence of spectrograms was the fastest method and performed better for blue whale A and B calls. VQ detected more 20 Hz fin whale calls but with a higher false alarm rate. DTW and VQ outperformed for the more variable blue whale D calls.

  2. Nonlinear spectro-temporal features based on a cochlear model for automatic speech recognition in a noisy situation.

    PubMed

    Choi, Yong-Sun; Lee, Soo-Young

    2013-09-01

    A nonlinear speech feature extraction algorithm was developed by modeling human cochlear functions, and demonstrated as a noise-robust front-end for speech recognition systems. The algorithm was based on a model of the Organ of Corti in the human cochlea with such features as such as basilar membrane (BM), outer hair cells (OHCs), and inner hair cells (IHCs). Frequency-dependent nonlinear compression and amplification of OHCs were modeled by lateral inhibition to enhance spectral contrasts. In particular, the compression coefficients had frequency dependency based on the psychoacoustic evidence. Spectral subtraction and temporal adaptation were applied in the time-frame domain. With long-term and short-term adaptation characteristics, these factors remove stationary or slowly varying components and amplify the temporal changes such as onset or offset. The proposed features were evaluated with a noisy speech database and showed better performance than the baseline methods such as mel-frequency cepstral coefficients (MFCCs) and RASTA-PLP in unknown noisy conditions. PMID:23558292

  3. I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance.

    PubMed

    Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

    2016-01-01

    We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

  4. I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance.

    PubMed

    Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

    2016-01-01

    We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient.

  5. I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

    PubMed Central

    Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

    2016-01-01

    We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

  6. Comparison of different automatic adaptive threshold selection techniques for estimating discharge from river width

    NASA Astrophysics Data System (ADS)

    Elmi, Omid; Javad Tourian, Mohammad; Sneeuw, Nico

    2015-04-01

    The importance of river discharge monitoring is critical for e.g., water resource planning, climate change, hazard monitoring. River discharge has been measured at in situ gauges for more than a century. Despite various attempts, some basins are still ungauged. Moreover, a reduction in the number of worldwide gauging stations increases the interest to employ remote sensing data for river discharge monitoring. Finding an empirical relationship between simultaneous in situ measurements of discharge and river widths derived from satellite imagery has been introduced as a straightforward remote sensing alternative. Classifying water and land in an image is the primary task for defining the river width. Water appears dark in the near infrared and infrared bands in satellite images. As a result low values in the histogram usually represent the water content. In this way, applying a threshold on the image histogram and separating into two different classes is one of the most efficient techniques to build a water mask. Beside its simple definition, finding the appropriate threshold value in each image is the most critical issue. The threshold is variable due to changes in the water level, river extent, atmosphere, sunlight radiation, onboard calibration of the satellite over time. These complexities in water body classification are the main source of error in river width estimation. In this study, we are looking for the most efficient adaptive threshold algorithm to estimate the river discharge. To do this, all cloud free MODIS images coincident with the in situ measurement are collected. Next a number of automatic threshold selection techniques are employed to generate different dynamic water masks. Then, for each of them a separate empirical relationship between river widths and discharge measurements are determined. Through these empirical relationships, we estimate river discharge at the gauge and then validate our results against in situ measurements and also

  7. Recognition of Extracellular Bacteria by NLRs and Its Role in the Development of Adaptive Immunity

    PubMed Central

    Ferrand, Jonathan; Ferrero, Richard Louis

    2013-01-01

    Innate immune recognition of bacteria is the first requirement for mounting an effective immune response able to control infection. Over the previous decade, the general paradigm was that extracellular bacteria were only sensed by cell surface-expressed Toll-like receptors (TLRs), whereas cytoplasmic sensors, including members of the Nod-like receptor (NLR) family, were specific to pathogens capable of breaching the host cell membrane. It has become apparent, however, that intracellular innate immune molecules, such as the NLRs, play key roles in the sensing of not only intracellular, but also extracellular bacterial pathogens or their components. In this review, we will discuss the various mechanisms used by bacteria to activate NLR signaling in host cells. These mechanisms include bacterial secretion systems, pore-forming toxins, and outer membrane vesicles. We will then focus on the influence of NLR activation on the development of adaptive immune responses in different cell types. PMID:24155747

  8. Toward Design of an Environment-Aware Adaptive Locomotion-Mode-Recognition System

    PubMed Central

    Du, Lin; Zhang, Fan; Liu, Ming

    2013-01-01

    In this study, we aimed to improve the performance of a locomotion-mode-recognition system based on neuromuscular-mechanical fusion by introducing additional information about the walking environment. Linear-discriminant-analysis-based classifiers were first designed to identify a lower limb prosthesis user’s locomotion mode based on electromyographic signals recorded from residual leg muscles and ground reaction forces measured from the prosthetic pylon. Nine transfemoral amputees who wore a passive hydraulic knee or powered prosthetic knee participated in this study. Information about the walking terrain was simulated and modeled as prior probability based on the principle of maximum entropy and integrated into the discriminant functions of the classifier. When the correct prior knowledge of walking terrain was simulated, the classification accuracy for each locomotion mode significantly increased and no task transitions were missed. In addition, simulated incorrect prior knowledge did not significantly reduce system performance, indicating that our design is robust against noisy and imperfect prior information. Furthermore, these observations were independent of the type of prosthesis applied. The promising results in this study may assist the further development of an environment-aware adaptive system for locomotion-mode recognition for powered lower limb prostheses or orthoses. PMID:22996721

  9. Recognition of human emotion using sensor agent robot for interactive and adaptive living spaces

    NASA Astrophysics Data System (ADS)

    Murata, Sozo; Mita, Akira

    2011-04-01

    Safer, more comfortable and energy-efficient living spaces are always demanded. However, most buildings are designed based on prescribed scenarios so that they do not act on abrupt changes of environments. We propose "Biofication of Living Spaces" that has functions of learning occupants' lifestyles and taking actions based on collected information. By doing so, we can incorporate the high adaptability to the building. Our goal is to make living spaces more "comfortable". However, human beings have emotion that implies the meaning of "comfortable" depends on each individual. Therefore our study focuses on recognition of human emotion. We suggest using robots as sensor agents. By using robots equipped with various sensors, they can interact with occupants and environment. We use a sensor agent robot called "e-bio". In this research, we construct a human tracking system and identified emotions of residents using their walking information. We focus on the influences of illuminance and sound. We classified emotions by calculating the distance of the mapped points in comfortable and uncomfortable spaces with parametric eigen space method, in which parameters are determined by a mapping of tracks in the space. As a method of pattern recognition, a weighted k-nearest neighbor is used. Experiments considering illuminance and sound environments, illustrates good correlation between emotion and environments.

  10. Rapid Word Recognition as a Measure of Word-Level Automaticity and Its Relation to Other Measures of Reading

    ERIC Educational Resources Information Center

    Frye, Elizabeth M.; Gosky, Ross

    2012-01-01

    The present study investigated the relationship between rapid recognition of individual words (Word Recognition Test) and two measures of contextual reading: (1) grade-level Passage Reading Test (IRI passage) and (2) performance on standardized STAR Reading Test. To establish if time of presentation on the word recognition test was a factor in…

  11. Robust semi-automatic segmentation of single- and multichannel MRI volumes through adaptable class-specific representation

    NASA Astrophysics Data System (ADS)

    Nielsen, Casper F.; Passmore, Peter J.

    2002-05-01

    Segmentation of MRI volumes is complicated by noise, inhomogeneity and partial volume artefacts. Fully or semi-automatic methods often require time consuming or unintuitive initialization. Adaptable Class-Specific Representation (ACSR) is a semi-automatic segmentation framework implemented by the Path Growing Algorithm (PGA), which reduces artefacts near segment boundaries. The user visually defines the desired segment classes through the selection of class templates and the following segmentation process is fully automatic. Good results have previously been achieved with color cryo section segmentation and ACSR has been developed further for the MRI modality. In this paper we present two optimizations for robust ACSR segmentation of MRI volumes. Automatic template creation based on an initial segmentation step using Learning Vector Quantization is applied for higher robustness to noise. Inhomogeneity correction is added as a pre-processing step, comparing the EQ and N3 algorithms. Results based on simulated T1-weighed and multispectral (T1 and T2) MRI data from the BrainWeb database and real data from the Internet Brain Segmentation Repository are presented. We show that ACSR segmentation compares favorably to previously published results on the same volumes and discuss the pros and cons of using quantitative ground truth evaluation compared to qualitative visual assessment.

  12. Automatic online adaptive radiation therapy techniques for targets with significant shape change: a feasibility study.

    PubMed

    Court, Laurence E; Tishler, Roy B; Petit, Joshua; Cormack, Robert; Chin, Lee

    2006-05-21

    This work looks at the feasibility of an online adaptive radiation therapy concept that would detect the daily position and shape of the patient, and would then correct the daily treatment to account for any changes compared with planning position. In particular, it looks at the possibility of developing algorithms to correct for large complicated shape change. For co-planar beams, the dose in an axial plane is approximately associated with the positions of a single multi-leaf collimator (MLC) pair. We start with a primary plan, and automatically generate several secondary plans with gantry angles offset by regular increments. MLC sequences for each plan are calculated keeping monitor units (MUs) and number of segments constant for a given beam (fluences are different). Bulk registration (3D) of planning and daily CT images gives global shifts. Slice-by-slice (2D) registration gives local shifts and rotations about the longitudinal axis for each axial slice. The daily MLC sequence is then created for each axial slice/MLC leaf pair combination, by taking the MLC positions from the pre-calculated plan with the nearest rotation, and shifting using a beam's-eye-view calculation to account for local linear shifts. A planning study was carried out using two head and neck region MR images of a healthy volunteer which were contoured to simulate a base-of-tongue treatment: one with the head straight (used to simulate the planning image) and the other with the head tilted to the left (the daily image). Head and neck treatment was chosen to evaluate this technique because of its challenging nature, with varying internal and external contours, and multiple degrees of freedom. Shape change was significant: on a slice-by-slice basis, local rotations in the daily image varied from 2 to 31 degrees, and local shifts ranged from -0.2 to 0.5 cm and -0.4 to 0.0 cm in right-left and posterior-anterior directions, respectively. The adapted treatment gave reasonable target coverage (100

  13. The adaptation of GDL motion recognition system to sport and rehabilitation techniques analysis.

    PubMed

    Hachaj, Tomasz; Ogiela, Marek R

    2016-06-01

    The main novelty of this paper is presenting the adaptation of Gesture Description Language (GDL) methodology to sport and rehabilitation data analysis and classification. In this paper we showed that Lua language can be successfully used for adaptation of the GDL classifier to those tasks. The newly applied scripting language allows easily extension and integration of classifier with other software technologies and applications. The obtained execution speed allows using the methodology in the real-time motion capture data processing where capturing frequency differs from 100 Hz to even 500 Hz depending on number of features or classes to be calculated and recognized. Due to this fact the proposed methodology can be used to the high-end motion capture system. We anticipate that using novel, efficient and effective method will highly help both sport trainers and physiotherapist in they practice. The proposed approach can be directly applied to motion capture data kinematics analysis (evaluation of motion without regard to the forces that cause that motion). The ability to apply pattern recognition methods for GDL description can be utilized in virtual reality environment and used for sport training or rehabilitation treatment. PMID:27106581

  14. A semi-automatic 2D-to-3D video conversion with adaptive key-frame selection

    NASA Astrophysics Data System (ADS)

    Ju, Kuanyu; Xiong, Hongkai

    2014-11-01

    To compensate the deficit of 3D content, 2D to 3D video conversion (2D-to-3D) has recently attracted more attention from both industrial and academic communities. The semi-automatic 2D-to-3D conversion which estimates corresponding depth of non-key-frames through key-frames is more desirable owing to its advantage of balancing labor cost and 3D effects. The location of key-frames plays a role on quality of depth propagation. This paper proposes a semi-automatic 2D-to-3D scheme with adaptive key-frame selection to keep temporal continuity more reliable and reduce the depth propagation errors caused by occlusion. The potential key-frames would be localized in terms of clustered color variation and motion intensity. The distance of key-frame interval is also taken into account to keep the accumulated propagation errors under control and guarantee minimal user interaction. Once their depth maps are aligned with user interaction, the non-key-frames depth maps would be automatically propagated by shifted bilateral filtering. Considering that depth of objects may change due to the objects motion or camera zoom in/out effect, a bi-directional depth propagation scheme is adopted where a non-key frame is interpolated from two adjacent key frames. The experimental results show that the proposed scheme has better performance than existing 2D-to-3D scheme with fixed key-frame interval.

  15. The Role of Higher Level Adaptive Coding Mechanisms in the Development of Face Recognition

    ERIC Educational Resources Information Center

    Pimperton, Hannah; Pellicano, Elizabeth; Jeffery, Linda; Rhodes, Gillian

    2009-01-01

    DevDevelopmental improvements in face identity recognition ability are widely documented, but the source of children's immaturity in face recognition remains unclear. Differences in the way in which children and adults visually represent faces might underlie immaturities in face recognition. Recent evidence of a face identity aftereffect (FIAE),…

  16. Recognition of user's activity for adaptive cooperative assistance in robotic surgery.

    PubMed

    Nessi, Federico; Beretta, Elisa; Ferrigno, Giancarlo; De Momi, Elena

    2015-01-01

    During hands-on robotic surgery it is advisable to know how and when to provide the surgeon with different assistance levels with respect to the current performed activity. Gesteme-based on-line classification requires the definition of a complete set of primitives and the observation of large signal percentage. In this work an on-line, gesteme-free activity recognition method is addressed. The algorithm models the guidance forces and the resulting trajectory of the manipulator with 26 low-level components of a Gaussian Mixture Model (GMM). Temporal switching among the components is modeled with a Hidden Markov Model (HMM). Tests are performed in a simplified scenario over a pool of 5 non-surgeon users. Classification accuracy resulted higher than 89% after the observation of a 300 ms-long signal. Future work will address the use of the current detected activity to on-line trigger different strategies to control the manipulator and adapt the level of assistance. PMID:26737482

  17. The Neural Correlates of Everyday Recognition Memory

    ERIC Educational Resources Information Center

    Milton, F.; Muhlert, N.; Butler, C. R.; Benattayallah, A.; Zeman, A. Z.

    2011-01-01

    We used a novel automatic camera, SenseCam, to create a recognition memory test for real-life events. Adapting a "Remember/Know" paradigm, we asked healthy undergraduates, who wore SenseCam for 2 days, in their everyday environments, to classify images as strongly or weakly remembered, strongly or weakly familiar or novel, while brain activation…

  18. Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

    NASA Technical Reports Server (NTRS)

    Szu, Harold H.

    1990-01-01

    In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

  19. Automatic recognition of seismic intensity based on RS and GIS: a case study in Wenchuan Ms8.0 earthquake of China.

    PubMed

    Zhang, Qiuwen; Zhang, Yan; Yang, Xiaohong; Su, Bin

    2014-01-01

    In recent years, earthquakes have frequently occurred all over the world, which caused huge casualties and economic losses. It is very necessary and urgent to obtain the seismic intensity map timely so as to master the distribution of the disaster and provide supports for quick earthquake relief. Compared with traditional methods of drawing seismic intensity map, which require many investigations in the field of earthquake area or are too dependent on the empirical formulas, spatial information technologies such as Remote Sensing (RS) and Geographical Information System (GIS) can provide fast and economical way to automatically recognize the seismic intensity. With the integrated application of RS and GIS, this paper proposes a RS/GIS-based approach for automatic recognition of seismic intensity, in which RS is used to retrieve and extract the information on damages caused by earthquake, and GIS is applied to manage and display the data of seismic intensity. The case study in Wenchuan Ms8.0 earthquake in China shows that the information on seismic intensity can be automatically extracted from remotely sensed images as quickly as possible after earthquake occurrence, and the Digital Intensity Model (DIM) can be used to visually query and display the distribution of seismic intensity.

  20. Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

    NASA Astrophysics Data System (ADS)

    Keller, Brenton; Cunefare, David; Grewal, Dilraj S.; Mahmoud, Tamer H.; Izatt, Joseph A.; Farsiu, Sina

    2016-07-01

    We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed "adjusted mean arc length" (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra's shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.

  1. Using Virtualization and Automatic Evaluation: Adapting Network Services Management Courses to the EHEA

    ERIC Educational Resources Information Center

    Ros, S.; Robles-Gomez, A.; Hernandez, R.; Caminero, A. C.; Pastor, R.

    2012-01-01

    This paper outlines the adaptation of a course on the management of network services in operating systems, called NetServicesOS, to the context of the new European Higher Education Area (EHEA). NetServicesOS is a mandatory course in one of the official graduate programs in the Faculty of Computer Science at the Universidad Nacional de Educacion a…

  2. An Approach for Automatic Generation of Adaptive Hypermedia in Education with Multilingual Knowledge Discovery Techniques

    ERIC Educational Resources Information Center

    Alfonseca, Enrique; Rodriguez, Pilar; Perez, Diana

    2007-01-01

    This work describes a framework that combines techniques from Adaptive Hypermedia and Natural Language processing in order to create, in a fully automated way, on-line information systems from linear texts in electronic format, such as textbooks. The process is divided into two steps: an "off-line" processing step, which analyses the source text,…

  3. Automatic Domain Adaptation of Word Sense Disambiguation Based on Sublanguage Semantic Schemata Applied to Clinical Narrative

    ERIC Educational Resources Information Center

    Patterson, Olga

    2012-01-01

    Domain adaptation of natural language processing systems is challenging because it requires human expertise. While manual effort is effective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and confidentiality restrictions that hinder the…

  4. Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface

    PubMed Central

    Acqualagna, Laura; Botrel, Loic; Vidaurre, Carmen; Kübler, Andrea; Blankertz, Benjamin

    2016-01-01

    In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD) was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods. PMID:26891350

  5. Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface.

    PubMed

    Acqualagna, Laura; Botrel, Loic; Vidaurre, Carmen; Kübler, Andrea; Blankertz, Benjamin

    2016-01-01

    In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD) was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods. PMID:26891350

  6. Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.

    PubMed

    Ubeyli, Elif Derya

    2009-10-01

    This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261

  7. Effects of non-uniform windowing in a Rician-fading channel and simulation of adaptive automatic repeat request protocols

    NASA Astrophysics Data System (ADS)

    Kmiecik, Chris G.

    1990-06-01

    Two aspects of digital communication were investigated. In the first part, a Fast Fourier Transformation (FFT) based, M-ary frequency shift keying (FSK) receiver in a Rician-fading channel was analyzed to determine the benefits of non-uniform windowing of sampled received data. When a frequency offset occurs, non-uniform windowing provided better FFT magnitude separation. The improved dynamic range was balanced against a loss in detectability due to signal attenuation. With large frequency offset, the improved magnitude separation outweighed the loss in detectability. An analysis was carried out to determine what frequency deviation is necessary for non-uniform windowing to out-perform uniform windowing in a slow Rician-fading channel. Having established typical values of probability of bit errors, the second part of this thesis looked at improving throughput in a digital communications network by applying adaptive automatic repeat request (ARQ) protocols. The results of simulations of adaptive ARQ protocols with variable frame lengths is presented. By varying the frame length, improved throughput performance through all bit error rates was achieved.

  8. Adapting histogram for automatic noise data removal in building interior point cloud data

    NASA Astrophysics Data System (ADS)

    Shukor, S. A. Abdul; Rushforth, E. J.

    2015-05-01

    3D point cloud data is now preferred by researchers to generate 3D models. These models can be used throughout a variety of applications including 3D building interior models. The rise of Building Information Modeling (BIM) for Architectural, Engineering, Construction (AEC) applications has given 3D interior modelling more attention recently. To generate a 3D model representing the building interior, a laser scanner is used to collect the point cloud data. However, this data often comes with noise. This is due to several factors including the surrounding objects, lighting and specifications of the laser scanner. This paper highlights on the usage of the histogram to remove the noise data. Histograms, used in statistics and probability, are regularly being used in a number of applications like image processing, where a histogram can represent the total number of pixels in an image at each intensity level. Here, histograms represent the number of points recorded at range distance intervals in various projections. As unwanted noise data has a sparser cloud density compared to the required data and is usually situated at a notable distance from the required data, noise data will have lower frequencies in the histogram. By defining the acceptable range using the average frequency, points below this range can be removed. This research has shown that these histograms have the capabilities to remove unwanted data from 3D point cloud data representing building interiors automatically. This feature will aid the process of data preprocessing in producing an ideal 3D model from the point cloud data.

  9. Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

    NASA Astrophysics Data System (ADS)

    Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

    2015-11-01

    In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

  10. Automatic recognition of type III solar radio bursts: Automated Radio Burst Identification System method and first observations

    NASA Astrophysics Data System (ADS)

    Lobzin, Vasili V.; Cairns, Iver H.; Robinson, Peter A.; Steward, Graham; Patterson, Garth

    2009-04-01

    Because of the rapidly increasing role of technology, including complicated electronic systems, spacecraft, etc., modern society has become more vulnerable to a set of extraterrestrial influences (space weather) and requires continuous observation and forecasts of space weather. The major space weather events like solar flares and coronal mass ejections are usually accompanied by solar radio bursts, which can be used for a real-time space weather forecast. Coronal type III radio bursts are produced near the local electron plasma frequency and near its harmonic by fast electrons ejected from the solar active regions and moving through the corona and solar wind. These bursts have dynamic spectra with frequency rapidly falling with time, the typical duration of the coronal burst being about 1-3 s. This paper presents a new method developed to detect coronal type III bursts automatically and its implementation in a new Automated Radio Burst Identification System. The central idea of the implementation is to use the Radon transform for more objective detection of the bursts as approximately straight lines in dynamic spectra. Preliminary tests of the method with the use of the spectra obtained during 13 days show that the performance of the current implementation is quite high, ˜84%, while no false positives are observed and 23 events not listed previously are found. Prospects for improvements are discussed. The first automatically detected coronal type III radio bursts are presented.

  11. Automatic Mesh Adaptivity for Hybrid Monte Carlo/Deterministic Neutronics Modeling of Fusion Energy Systems

    SciTech Connect

    Ibrahim, Ahmad M; Wilson, P.; Sawan, M.; Mosher, Scott W; Peplow, Douglas E.; Grove, Robert E

    2013-01-01

    Three mesh adaptivity algorithms were developed to facilitate and expedite the use of the CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques in accurate full-scale neutronics simulations of fusion energy systems with immense sizes and complicated geometries. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as much geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility and resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation. Additionally, because of the significant increase in the efficiency of FW-CADIS simulations, the three algorithms enabled this difficult calculation to be accurately solved on a regular computer cluster, eliminating the need for a world-class super computer.

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

    NASA Astrophysics Data System (ADS)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

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

  13. Automatic mesh adaptivity for CADIS and FW-CADIS neutronics modeling of difficult shielding problems

    SciTech Connect

    Ibrahim, A. M.; Peplow, D. E.; Mosher, S. W.; Wagner, J. C.; Evans, T. M.; Wilson, P. P.; Sawan, M. E.

    2013-07-01

    The CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques dramatically increase the efficiency of neutronics modeling, but their use in the accurate design analysis of very large and geometrically complex nuclear systems has been limited by the large number of processors and memory requirements for their preliminary deterministic calculations and final Monte Carlo calculation. Three mesh adaptivity algorithms were developed to reduce the memory requirements of CADIS and FW-CADIS without sacrificing their efficiency improvement. First, a macro-material approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as much geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm de-couples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility. Using these algorithms resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation and, additionally, increased the efficiency of the Monte Carlo simulation by a factor of at least 3.4. The three algorithms enabled this difficult calculation to be accurately solved using an FW-CADIS simulation on a regular computer cluster, obviating the need for a world-class super computer. (authors)

  14. Automatic mesh adaptivity for hybrid Monte Carlo/deterministic neutronics modeling of difficult shielding problems

    DOE PAGES

    Ibrahim, Ahmad M.; Wilson, Paul P.H.; Sawan, Mohamed E.; Mosher, Scott W.; Peplow, Douglas E.; Wagner, John C.; Evans, Thomas M.; Grove, Robert E.

    2015-06-30

    The CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques dramatically increase the efficiency of neutronics modeling, but their use in the accurate design analysis of very large and geometrically complex nuclear systems has been limited by the large number of processors and memory requirements for their preliminary deterministic calculations and final Monte Carlo calculation. Three mesh adaptivity algorithms were developed to reduce the memory requirements of CADIS and FW-CADIS without sacrificing their efficiency improvement. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as muchmore » geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility. Using these algorithms resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation and, additionally, increased the efficiency of the Monte Carlo simulation by a factor of at least 3.4. The three algorithms enabled this difficult calculation to be accurately solved using an FW-CADIS simulation on a regular computer cluster, eliminating the need for a world-class super computer.« less

  15. Automatic mesh adaptivity for hybrid Monte Carlo/deterministic neutronics modeling of difficult shielding problems

    SciTech Connect

    Ibrahim, Ahmad M.; Wilson, Paul P.H.; Sawan, Mohamed E.; Mosher, Scott W.; Peplow, Douglas E.; Wagner, John C.; Evans, Thomas M.; Grove, Robert E.

    2015-06-30

    The CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques dramatically increase the efficiency of neutronics modeling, but their use in the accurate design analysis of very large and geometrically complex nuclear systems has been limited by the large number of processors and memory requirements for their preliminary deterministic calculations and final Monte Carlo calculation. Three mesh adaptivity algorithms were developed to reduce the memory requirements of CADIS and FW-CADIS without sacrificing their efficiency improvement. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as much geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility. Using these algorithms resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation and, additionally, increased the efficiency of the Monte Carlo simulation by a factor of at least 3.4. The three algorithms enabled this difficult calculation to be accurately solved using an FW-CADIS simulation on a regular computer cluster, eliminating the need for a world-class super computer.

  16. Speech recognition in reverberant and noisy environments employing multiple feature extractors and i-vector speaker adaptation

    NASA Astrophysics Data System (ADS)

    Alam, Md Jahangir; Gupta, Vishwa; Kenny, Patrick; Dumouchel, Pierre

    2015-12-01

    The REVERB challenge provides a common framework for the evaluation of feature extraction techniques in the presence of both reverberation and additive background noise. State-of-the-art speech recognition systems perform well in controlled environments, but their performance degrades in realistic acoustical conditions, especially in real as well as simulated reverberant environments. In this contribution, we utilize multiple feature extractors including the conventional mel-filterbank, multi-taper spectrum estimation-based mel-filterbank, robust mel and compressive gammachirp filterbank, iterative deconvolution-based dereverberated mel-filterbank, and maximum likelihood inverse filtering-based dereverberated mel-frequency cepstral coefficient features for speech recognition with multi-condition training data. In order to improve speech recognition performance, we combine their results using ROVER (Recognizer Output Voting Error Reduction). For two- and eight-channel tasks, to get benefited from the multi-channel data, we also use ROVER, instead of the multi-microphone signal processing method, to reduce word error rate by selecting the best scoring word at each channel. As in a previous work, we also apply i-vector-based speaker adaptation which was found effective. In speech recognition task, speaker adaptation tries to reduce mismatch between the training and test speakers. Speech recognition experiments are conducted on the REVERB challenge 2014 corpora using the Kaldi recognizer. In our experiments, we use both utterance-based batch processing and full batch processing. In the single-channel task, full batch processing reduced word error rate (WER) from 10.0 to 9.3 % on SimData as compared to utterance-based batch processing. Using full batch processing, we obtained an average WER of 9.0 and 23.4 % on the SimData and RealData, respectively, for the two-channel task, whereas for the eight-channel task on the SimData and RealData, the average WERs found were 8

  17. User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

    NASA Astrophysics Data System (ADS)

    He, Jiayuan; Zhang, Dingguo; Jiang, Ning; Sheng, Xinjun; Farina, Dario; Zhu, Xiangyang

    2015-08-01

    Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

  18. Structural interplay between germline and adaptive recognition determines TCR-peptide-MHC cross-reactivity

    PubMed Central

    Adams, Jarrett J.; Narayanan, Samanthi; Birnbaum, Michael E.; Sidhu, Sachdev S.; Blevins, Sydney J.; Gee, Marvin H.; Sibener, Leah V.; Baker, Brian M.; Kranz, David M.; Garcia, K. Christopher

    2015-01-01

    The T cell receptor - peptide-MHC interface is comprised of conserved and diverse regions, yet the relative contributions of each in shaping T cell recognition remain unclear. We isolated cross-reactive peptides with limited homology, allowing us to compare the structural properties of nine peptides for a single TCR-MHC pair. The TCR’s cross-reactivity is rooted in highly similar recognition of an apical ‘hotspot’ position in the peptide, while tolerating significant sequence variation at ancillary positions. Furthermore, we find a striking structural convergence onto a germline-mediated interaction between TCR CDR1α and the MHC α2 helix of twelve TCR-pMHC complexes. Our studies suggest that TCR-MHC germline-mediated constraints, together with a focus on a small peptide hotspot, may place limits on peptide antigen cross-reactivity. PMID:26523866

  19. Hearing-aid automatic gain control adapting to two sound sources in the environment, using three time constants

    NASA Astrophysics Data System (ADS)

    Nordqvist, Peter; Leijon, Arne

    2004-11-01

    A hearing aid AGC algorithm is presented that uses a richer representation of the sound environment than previous algorithms. The proposed algorithm is designed to (1) adapt slowly (in approximately 10 s) between different listening environments, e.g., when the user leaves a single talker lecture for a multi-babble coffee-break; (2) switch rapidly (about 100 ms) between different dominant sound sources within one listening situation, such as the change from the user's own voice to a distant speaker's voice in a quiet conference room; (3) instantly reduce gain for strong transient sounds and then quickly return to the previous gain setting; and (4) not change the gain in silent pauses but instead keep the gain setting of the previous sound source. An acoustic evaluation showed that the algorithm worked as intended. The algorithm was evaluated together with a reference algorithm in a pilot field test. When evaluated by nine users in a set of speech recognition tests, the algorithm showed similar results to the reference algorithm. .

  20. Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition

    PubMed Central

    Vajda, Szilárd; Rangoni, Yves; Cecotti, Hubert

    2015-01-01

    For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. PMID:25870463

  1. A new approach to automatic radiation spectrum analysis

    SciTech Connect

    Olmos, P.; Diaz, J.C.; Perez, J.M.; Aguayo, P.; Bru, A.; Garcia-Belmonte, G.; de Pablos, J.L. ); Gomez, P.; Rodellar, V. )

    1991-08-01

    In this paper the application of adaptive methods to the solution of the automatic radioisotope identification problem using the energy spectrum is described. The identification is carried out by means of neural networks, which allow the use of relatively reduced computational structures, while keeping high pattern recognition capability. In this context, it has been found that one of these simple structures, once adequately trained, is quite suitable to identify a given isotope present in a mixture of elements as well as the relative proportions of each identified substance. Preliminary results are good enough to consider these adaptive structures as powerful and simple tools in the automatic spectrum analysis.

  2. The contribution of discrete-trial naming and visual recognition to rapid automatized naming deficits of dyslexic children with and without a history of language delay

    PubMed Central

    Gasperini, Filippo; Brizzolara, Daniela; Cristofani, Paola; Casalini, Claudia; Chilosi, Anna Maria

    2014-01-01

    Children with Developmental Dyslexia (DD) are impaired in Rapid Automatized Naming (RAN) tasks, where subjects are asked to name arrays of high frequency items as quickly as possible. However the reasons why RAN speed discriminates DD from typical readers are not yet fully understood. Our study was aimed to identify some of the cognitive mechanisms underlying RAN-reading relationship by comparing one group of 32 children with DD with an age-matched control group of typical readers on a naming and a visual recognition task both using a discrete-trial methodology, in addition to a serial RAN task, all using the same stimuli (digits and colors). Results showed a significant slowness of DD children in both serial and discrete-trial naming (DN) tasks regardless of type of stimulus, but no difference between the two groups on the discrete-trial recognition task. Significant differences between DD and control participants in the RAN task disappeared when performance in the DN task was partialled out by covariance analysis for colors, but not for digits. The same pattern held in a subgroup of DD subjects with a history of early language delay (LD). By contrast, in a subsample of DD children without LD the RAN deficit was specific for digits and disappeared after slowness in DN was partialled out. Slowness in DN was more evident for LD than for noLD DD children. Overall, our results confirm previous evidence indicating a name-retrieval deficit as a cognitive impairment underlying RAN slowness in DD children. This deficit seems to be more marked in DD children with previous LD. Moreover, additional cognitive deficits specifically associated with serial RAN tasks have to be taken into account when explaining deficient RAN speed of these latter children. We suggest that partially different cognitive dysfunctions underpin superficially similar RAN impairments in different subgroups of DD subjects. PMID:25237301

  3. Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

    PubMed

    Kazemi, Fatemeh; Najafabadi, Tooraj Abbasian; Araabi, Babak Nadjar

    2016-01-01

    Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

  4. Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine

    PubMed Central

    Kazemi, Fatemeh; Najafabadi, Tooraj Abbasian; Araabi, Babak Nadjar

    2016-01-01

    Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2–M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists. PMID:27563575

  5. Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

    PubMed

    Kazemi, Fatemeh; Najafabadi, Tooraj Abbasian; Araabi, Babak Nadjar

    2016-01-01

    Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists. PMID:27563575

  6. Speech recognition and understanding

    SciTech Connect

    Vintsyuk, T.K.

    1983-05-01

    This article discusses the automatic processing of speech signals with the aim of finding a sequence of works (speech recognition) or a concept (speech understanding) being transmitted by the speech signal. The goal of the research is to develop an automatic typewriter that will automatically edit and type text under voice control. A dynamic programming method is proposed in which all possible class signals are stored, after which the presented signal is compared to all the stored signals during the recognition phase. Topics considered include element-by-element recognition of words of speech, learning speech recognition, phoneme-by-phoneme speech recognition, the recognition of connected speech, understanding connected speech, and prospects for designing speech recognition and understanding systems. An application of the composition dynamic programming method for the solution of basic problems in the recognition and understanding of speech is presented.

  7. Real-Time Very Large-Scale Integration Recognition System with an On-Chip Adaptive K-Means Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Hou, Zuoxun; Ma, Yitao; Zhu, Hongbo; Zheng, Nanning; Shibata, Tadashi

    2013-04-01

    A very large-scale integration (VLSI) recognition system equipped with an on-chip learning capability has been developed for real-time processing applications. This system can work in two functional modes of operation: adaptive K-means learning mode and recognition mode. In the adaptive K-means learning mode, the variance ratio criterion (VRC) has been employed to evaluate the quality of K-means classification results, and the evaluation algorithm has been implemented on the chip. As a result, it has become possible for the system to autonomously determine the optimum number of clusters (K). In the recognition mode, the nearest-neighbor search algorithm is very efficiently carried out by the fully parallel architecture employed in the chip. In both modes of operation, many hardware resources are shared and the functionality is flexibly altered by the system controller designed as a finite-state machine (FSM). The chip is implemented on Altera Cyclone II FPGA with 46K logic cells. Its operating clock is 25 MHz and the processing times for adaptive learning and recognition with 256 64-dimension feature vectors are about 0.42 ms and 4 µs, respectively. Both adaptive K-means learning and recognition functions have been verified by experiments using the image data from the COIL-100 (Columbia University Object Image Library) database.

  8. Semantic Network Adaptation Based on QoS Pattern Recognition for Multimedia Streams

    NASA Astrophysics Data System (ADS)

    Exposito, Ernesto; Gineste, Mathieu; Lamolle, Myriam; Gomez, Jorge

    This article proposes an ontology based pattern recognition methodology to compute and represent common QoS properties of the Application Data Units (ADU) of multimedia streams. The use of this ontology by mechanisms located at different layers of the communication architecture will allow implementing fine per-packet self-optimization of communication services regarding the actual application requirements. A case study showing how this methodology is used by error control mechanisms in the context of wireless networks is presented in order to demonstrate the feasibility and advantages of this approach.

  9. Function and evolution of ubiquitous bacterial signaling adapter phosphopeptide recognition domain FHA.

    PubMed

    Weiling, Hong; Xiaowen, Yu; Chunmei, Li; Jianping, Xie

    2013-03-01

    Forkhead-associated domain (FHA) is a phosphopeptide recognition domain embedded in some regulatory proteins. With similar fold type to important eukaryotic signaling molecules such as Smad2 and IRF3, the role of bacterial FHA domain is intensively pursued. Reported bacterial FHA domain roles include: regulation of glutamate and lipids production, regulation of cell shape, type III secretion, ethambutol resistance, sporulation, signal transduction, carbohydrate storage and transport, and pathogenic and symbiotic host-bacterium interactions. To provide basis for the studies of other bacterial FHA domain containing proteins, the status of bacterial FHA functionality and evolution were summarized.

  10. Combining Automatic Tube Current Modulation with Adaptive Statistical Iterative Reconstruction for Low-Dose Chest CT Screening

    PubMed Central

    Chen, Jiang-Hong; Jin, Er-Hu; He, Wen; Zhao, Li-Qin

    2014-01-01

    Objective To reduce radiation dose while maintaining image quality in low-dose chest computed tomography (CT) by combining adaptive statistical iterative reconstruction (ASIR) and automatic tube current modulation (ATCM). Methods Patients undergoing cancer screening (n = 200) were subjected to 64-slice multidetector chest CT scanning with ASIR and ATCM. Patients were divided into groups 1, 2, 3, and 4 (n = 50 each), with a noise index (NI) of 15, 20, 30, and 40, respectively. Each image set was reconstructed with 4 ASIR levels (0% ASIR, 30% ASIR, 50% ASIR, and 80% ASIR) in each group. Two radiologists assessed subjective image noise, image artifacts, and visibility of the anatomical structures. Objective image noise and signal-to-noise ratio (SNR) were measured, and effective dose (ED) was recorded. Results Increased NI was associated with increased subjective and objective image noise results (P<0.001), and SNR decreased with increasing NI (P<0.001). These values improved with increased ASIR levels (P<0.001). Images from all 4 groups were clinically diagnosable. Images with NI = 30 and 50% ASIR had average subjective image noise scores and nearly average anatomical structure visibility scores, with a mean objective image noise of 23.42 HU. The EDs for groups 1, 2, 3 and 4 were 2.79±1.17, 1.69±0.59, 0.74±0.29, and 0.37±0.22 mSv, respectively. Compared to group 1 (NI = 15), the ED reductions were 39.43%, 73.48%, and 86.74% for groups 2, 3, and 4, respectively. Conclusions Using NI = 30 with 50% ASIR in the chest CT protocol, we obtained average or above-average image quality but a reduced ED. PMID:24691208

  11. Development of an Adaptive Multi-Method Algorithm for Automatic Picking of First Arrival Times: Application to Near Surface Seismic Data

    NASA Astrophysics Data System (ADS)

    Khalaf, A.; Camerlynck, C. M.; Schneider, A. C.; Florsch, N.

    2015-12-01

    Accurate picking of first arrival times plays an important role in many seismic studies, particularly in seismic tomography and reservoirs or aquifers monitoring. Many techniques have been developed for picking first arrivals automatically or semi-automatically, but most of them were developed for seismological purposes which does not attain the accuracy objectives due to the complexity of near surface structures, and to usual low signal-to-noise ratio. We propose a new adaptive algorithm for near surface data based on three picking methods, combining multi-nested windows (MNW), Higher Order Statistics (HOS), and Akaike Information Criterion (AIC). They exploit the benefits of integrating many properties, which reveal the presence of first arrivals, to provide an efficient and robust first arrivals picking. This strategy mimics the human first-break picking, where at the beginning the global trend is defined. Then the exact first-breaks are searched in the vicinity of the now defined trend. In a multistage algorithm, three successive phases are launched, where each of them characterize a specific signal property. Within each phase, the potential picks and their error range are automatically estimated, and then used sequentially as leader in the following phase picking. The accuracy and robustness of the implemented algorithm are successfully validated on synthetic and real data which have special challenges for automatic pickers. The comparison of resulting P-wave arrival times with those picked manually, and other algorithms of automatic picking, demonstrated the reliable performance of the new scheme under different noisy conditions. All parameters of our multi-method algorithm are auto-adaptive thanks to the integration in series of each sub-algorithm results in the flow. Hence, it is nearly a parameter-free algorithm, which is straightforward to implement and demands low computational resources.

  12. Autistic traits are linked to reduced adaptive coding of face identity and selectively poorer face recognition in men but not women.

    PubMed

    Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Ewing, Louise

    2013-11-01

    Our ability to discriminate and recognize thousands of faces despite their similarity as visual patterns relies on adaptive, norm-based, coding mechanisms that are continuously updated by experience. Reduced adaptive coding of face identity has been proposed as a neurocognitive endophenotype for autism, because it is found in autism and in relatives of individuals with autism. Autistic traits can also extend continuously into the general population, raising the possibility that reduced adaptive coding of face identity may be more generally associated with autistic traits. In the present study, we investigated whether adaptive coding of face identity decreases as autistic traits increase in an undergraduate population. Adaptive coding was measured using face identity aftereffects, and autistic traits were measured using the Autism-Spectrum Quotient (AQ) and its subscales. We also measured face and car recognition ability to determine whether autistic traits are selectively related to face recognition difficulties. We found that men who scored higher on levels of autistic traits related to social interaction had reduced adaptive coding of face identity. This result is consistent with the idea that atypical adaptive face-coding mechanisms are an endophenotype for autism. Autistic traits were also linked with face-selective recognition difficulties in men. However, there were some unexpected sex differences. In women, autistic traits were linked positively, rather than negatively, with adaptive coding of identity, and were unrelated to face-selective recognition difficulties. These sex differences indicate that autistic traits can have different neurocognitive correlates in men and women and raise the intriguing possibility that endophenotypes of autism can differ in males and females.

  13. The soluble pattern recognition receptor PTX3 links humoral innate and adaptive immune responses by helping marginal zone B cells.

    PubMed

    Chorny, Alejo; Casas-Recasens, Sandra; Sintes, Jordi; Shan, Meimei; Polentarutti, Nadia; García-Escudero, Ramón; Walland, A Cooper; Yeiser, John R; Cassis, Linda; Carrillo, Jorge; Puga, Irene; Cunha, Cristina; Bastos, Hélder; Rodrigues, Fernando; Lacerda, João F; Morais, António; Dieguez-Gonzalez, Rebeca; Heeger, Peter S; Salvatori, Giovanni; Carvalho, Agostinho; Garcia-Sastre, Adolfo; Blander, J Magarian; Mantovani, Alberto; Garlanda, Cecilia; Cerutti, Andrea

    2016-09-19

    Pentraxin 3 (PTX3) is a fluid-phase pattern recognition receptor of the humoral innate immune system with ancestral antibody-like properties but unknown antibody-inducing function. In this study, we found binding of PTX3 to splenic marginal zone (MZ) B cells, an innate-like subset of antibody-producing lymphocytes strategically positioned at the interface between the circulation and the adaptive immune system. PTX3 was released by a subset of neutrophils that surrounded the splenic MZ and expressed an immune activation-related gene signature distinct from that of circulating neutrophils. Binding of PTX3 promoted homeostatic production of IgM and class-switched IgG antibodies to microbial capsular polysaccharides, which decreased in PTX3-deficient mice and humans. In addition, PTX3 increased IgM and IgG production after infection with blood-borne encapsulated bacteria or immunization with bacterial carbohydrates. This immunogenic effect stemmed from the activation of MZ B cells through a neutrophil-regulated pathway that elicited class switching and plasmablast expansion via a combination of T cell-independent and T cell-dependent signals. Thus, PTX3 may bridge the humoral arms of the innate and adaptive immune systems by serving as an endogenous adjuvant for MZ B cells. This property could be harnessed to develop more effective vaccines against encapsulated pathogens. PMID:27621420

  14. Reptile Toll-like receptor 5 unveils adaptive evolution of bacterial flagellin recognition.

    PubMed

    Voogdt, Carlos G P; Bouwman, Lieneke I; Kik, Marja J L; Wagenaar, Jaap A; van Putten, Jos P M

    2016-01-01

    Toll-like receptors (TLR) are ancient innate immune receptors crucial for immune homeostasis and protection against infection. TLRs are present in mammals, birds, amphibians and fish but have not been functionally characterized in reptiles despite the central position of this animal class in vertebrate evolution. Here we report the cloning, characterization, and function of TLR5 of the reptile Anolis carolinensis (Green Anole lizard). The receptor (acTLR5) displays the typical TLR protein architecture with 22 extracellular leucine rich repeats flanked by a N- and C-terminal leucine rich repeat domain, a membrane-spanning region, and an intracellular TIR domain. The receptor is phylogenetically most similar to TLR5 of birds and most distant to fish TLR5. Transcript analysis revealed acTLR5 expression in multiple lizard tissues. Stimulation of acTLR5 with TLR ligands demonstrated unique responsiveness towards bacterial flagellin in both reptile and human cells. Comparison of acTLR5 and human TLR5 using purified flagellins revealed differential sensitivity to Pseudomonas but not Salmonella flagellin, indicating development of species-specific flagellin recognition during the divergent evolution of mammals and reptiles. Our discovery of reptile TLR5 fills the evolutionary gap regarding TLR conservation across vertebrates and provides novel insights in functional evolution of host-microbe interactions.

  15. Reptile Toll-like receptor 5 unveils adaptive evolution of bacterial flagellin recognition

    PubMed Central

    Voogdt, Carlos G. P.; Bouwman, Lieneke I.; Kik, Marja J. L.; Wagenaar, Jaap A.; van Putten, Jos P. M.

    2016-01-01

    Toll-like receptors (TLR) are ancient innate immune receptors crucial for immune homeostasis and protection against infection. TLRs are present in mammals, birds, amphibians and fish but have not been functionally characterized in reptiles despite the central position of this animal class in vertebrate evolution. Here we report the cloning, characterization, and function of TLR5 of the reptile Anolis carolinensis (Green Anole lizard). The receptor (acTLR5) displays the typical TLR protein architecture with 22 extracellular leucine rich repeats flanked by a N- and C-terminal leucine rich repeat domain, a membrane-spanning region, and an intracellular TIR domain. The receptor is phylogenetically most similar to TLR5 of birds and most distant to fish TLR5. Transcript analysis revealed acTLR5 expression in multiple lizard tissues. Stimulation of acTLR5 with TLR ligands demonstrated unique responsiveness towards bacterial flagellin in both reptile and human cells. Comparison of acTLR5 and human TLR5 using purified flagellins revealed differential sensitivity to Pseudomonas but not Salmonella flagellin, indicating development of species-specific flagellin recognition during the divergent evolution of mammals and reptiles. Our discovery of reptile TLR5 fills the evolutionary gap regarding TLR conservation across vertebrates and provides novel insights in functional evolution of host-microbe interactions. PMID:26738735

  16. Toward the ultimate synthesis/recognition system.

    PubMed Central

    Furui, S

    1995-01-01

    This paper predicts speech synthesis, speech recognition, and speaker recognition technology for the year 2001, and it describes the most important research problems to be solved in order to arrive at these ultimate synthesis and recognition systems. The problems for speech synthesis include natural and intelligible voice production, prosody control based on meaning, capability of controlling synthesized voice quality and choosing individual speaking style, multilingual and multidialectal synthesis, choice of application-oriented speaking styles, capability of adding emotion, and synthesis from concepts. The problems for speech recognition include robust recognition against speech variations, adaptation/normalization to variations due to environmental conditions and speakers, automatic knowledge acquisition for acoustic and linguistic modeling, spontaneous speech recognition, naturalness and ease of human-machine interaction, and recognition of emotion. The problems for speaker recognition are similar to those for speech recognition. The research topics related to all these techniques include the use of articulatory and perceptual constraints and evaluation methods for measuring the quality of technology and systems. Images Fig. 3 PMID:7479723

  17. Adaptive fuzzy leader clustering of complex data sets in pattern recognition

    NASA Technical Reports Server (NTRS)

    Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

    1992-01-01

    A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

  18. Long-term adaptation of the influenza A virus by escaping cytotoxic T-cell recognition

    PubMed Central

    Woolthuis, Rutger G.; van Dorp, Christiaan H.; Keşmir, Can; de Boer, Rob J.; van Boven, Michiel

    2016-01-01

    The evolutionary adaptation of the influenza A virus (IAV) to human antibodies is well characterised. Much less is known about the long-term evolution of cytotoxic T lymphocyte (CTL) epitopes, which are important antigens for clearance of infection. We construct an antigenic map of IAVs of all human subtypes using a compendium of 142 confirmed CTL epitopes, and show that IAV evolved gradually in the period 1932–2015, with infrequent antigenic jumps in the H3N2 subtype. Intriguingly, the number of CTL epitopes per virus decreases with more than one epitope per three years in the H3N2 subtype (from 84 epitopes per virus in 1968 to 64 in 2015), mostly attributed to the loss of HLA-B epitopes. We confirm these observations with epitope predictions. Our findings indicate that selection pressures imposed by CTL immunity shape the long-term evolution of IAV. PMID:27629812

  19. Long-term adaptation of the influenza A virus by escaping cytotoxic T-cell recognition.

    PubMed

    Woolthuis, Rutger G; van Dorp, Christiaan H; Keşmir, Can; de Boer, Rob J; van Boven, Michiel

    2016-01-01

    The evolutionary adaptation of the influenza A virus (IAV) to human antibodies is well characterised. Much less is known about the long-term evolution of cytotoxic T lymphocyte (CTL) epitopes, which are important antigens for clearance of infection. We construct an antigenic map of IAVs of all human subtypes using a compendium of 142 confirmed CTL epitopes, and show that IAV evolved gradually in the period 1932-2015, with infrequent antigenic jumps in the H3N2 subtype. Intriguingly, the number of CTL epitopes per virus decreases with more than one epitope per three years in the H3N2 subtype (from 84 epitopes per virus in 1968 to 64 in 2015), mostly attributed to the loss of HLA-B epitopes. We confirm these observations with epitope predictions. Our findings indicate that selection pressures imposed by CTL immunity shape the long-term evolution of IAV. PMID:27629812

  20. Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: Proposal for an adaptable algorithm.

    PubMed

    Lamer, Antoine; Jeanne, Mathieu; Marcilly, Romaric; Kipnis, Eric; Schiro, Jessica; Logier, Régis; Tavernier, Benoît

    2016-06-01

    Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution. PMID:26817405

  1. A new method based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling.

    PubMed

    Avci, Derya; Leblebicioglu, Mehmet Kemal; Poyraz, Mustafa; Dogantekin, Esin

    2014-02-01

    So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the Artificial Neural Network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58% recognition succes was obtained.

  2. Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model.

    PubMed

    Khayati, Rasoul; Vafadust, Mansur; Towhidkhah, Farzad; Nabavi, Massood

    2008-03-01

    In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.

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

    ERIC Educational Resources Information Center

    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…

  4. A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion

    PubMed Central

    Sun, Wei; Zhang, Xiaorui; Peeta, Srinivas; He, Xiaozheng; Li, Yongfu; Zhu, Senlai

    2015-01-01

    To improve the effectiveness and robustness of fatigue driving recognition, a self-adaptive dynamic recognition model is proposed that incorporates information from multiple sources and involves two sequential levels of fusion, constructed at the feature level and the decision level. Compared with existing models, the proposed model introduces a dynamic basic probability assignment (BPA) to the decision-level fusion such that the weight of each feature source can change dynamically with the real-time fatigue feature measurements. Further, the proposed model can combine the fatigue state at the previous time step in the decision-level fusion to improve the robustness of the fatigue driving recognition. An improved correction strategy of the BPA is also proposed to accommodate the decision conflict caused by external disturbances. Results from field experiments demonstrate that the effectiveness and robustness of the proposed model are better than those of models based on a single fatigue feature and/or single-source information fusion, especially when the most effective fatigue features are used in the proposed model. PMID:26393615

  5. A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion.

    PubMed

    Sun, Wei; Zhang, Xiaorui; Peeta, Srinivas; He, Xiaozheng; Li, Yongfu; Zhu, Senlai

    2015-01-01

    To improve the effectiveness and robustness of fatigue driving recognition, a self-adaptive dynamic recognition model is proposed that incorporates information from multiple sources and involves two sequential levels of fusion, constructed at the feature level and the decision level. Compared with existing models, the proposed model introduces a dynamic basic probability assignment (BPA) to the decision-level fusion such that the weight of each feature source can change dynamically with the real-time fatigue feature measurements. Further, the proposed model can combine the fatigue state at the previous time step in the decision-level fusion to improve the robustness of the fatigue driving recognition. An improved correction strategy of the BPA is also proposed to accommodate the decision conflict caused by external disturbances. Results from field experiments demonstrate that the effectiveness and robustness of the proposed model are better than those of models based on a single fatigue feature and/or single-source information fusion, especially when the most effective fatigue features are used in the proposed model. PMID:26393615

  6. Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

    NASA Astrophysics Data System (ADS)

    Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

    2012-04-01

    Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

  7. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  8. Automatic recognition of T and teleseismic P waves by statistical analysis of their spectra: An application to continuous records of moored hydrophones

    NASA Astrophysics Data System (ADS)

    Sukhovich, Alexey; Irisson, Jean-Olivier; Perrot, Julie; Nolet, Guust

    2014-08-01

    A network of moored hydrophones is an effective way of monitoring seismicity of oceanic ridges since it allows detection and localization of underwater events by recording generated T waves. The high cost of ship time necessitates long periods (normally a year) of autonomous functioning of the hydrophones, which results in very large data sets. The preliminary but indispensable part of the data analysis consists of identifying all T wave signals. This process is extremely time consuming if it is done by a human operator who visually examines the entire database. We propose a new method for automatic signal discrimination based on the Gradient Boosted Decision Trees technique that uses the distribution of signal spectral power among different frequency bands as the discriminating characteristic. We have applied this method to automatically identify the types of acoustic signals in data collected by two moored hydrophones in the North Atlantic. We show that the method is capable of efficiently resolving the signals of seismic origin with a small percentage of wrong identifications and missed events: 1.2% and 0.5% for T waves and 14.5% and 2.8% for teleseismic P waves, respectively. In addition, good identification rates for signals of other types (iceberg and ship generated) are obtained. Our results indicate that the method can be successfully applied to automate the analysis of other (not necessarily acoustic) databases provided that enough information is available to describe statistical properties of the signals to be identified.

  9. Automatic recognizing of vocal fold disorders from glottis images.

    PubMed

    Huang, Chang-Chiun; Leu, Yi-Shing; Kuo, Chung-Feng Jeffrey; Chu, Wen-Lin; Chu, Yueng-Hsiang; Wu, Han-Cheng

    2014-09-01

    The laryngeal video stroboscope is an important instrument to test glottal diseases and read vocal fold images and voice quality for physician clinical diagnosis. This study is aimed to develop a medical system with functionality of automatic intelligent recognition of dynamic images. The static images of glottis opening to the largest extent and closing to the smallest extent were screened automatically using color space transformation and image preprocessing. The glottal area was also quantized. As the tongue base movements affected the position of laryngoscope and saliva would result in unclear images, this study used the gray scale adaptive entropy value to set the threshold in order to establish an elimination system. The proposed system can improve the effect of automatically captured images of glottis and achieve an accuracy rate of 96%. In addition, the glottal area and area segmentation threshold were calculated effectively. The glottis area segmentation was corrected, and the glottal area waveform pattern was drawn automatically to assist in vocal fold diagnosis. When developing the intelligent recognition system for vocal fold disorders, this study analyzed the characteristic values of four vocal fold patterns, namely, normal vocal fold, vocal fold paralysis, vocal fold polyp, and vocal fold cyst. It also used the support vector machine classifier to identify vocal fold disorders and achieved an identification accuracy rate of 98.75%. The results can serve as a very valuable reference for diagnosis.

  10. Automatic recognizing of vocal fold disorders from glottis images.

    PubMed

    Huang, Chang-Chiun; Leu, Yi-Shing; Kuo, Chung-Feng Jeffrey; Chu, Wen-Lin; Chu, Yueng-Hsiang; Wu, Han-Cheng

    2014-09-01

    The laryngeal video stroboscope is an important instrument to test glottal diseases and read vocal fold images and voice quality for physician clinical diagnosis. This study is aimed to develop a medical system with functionality of automatic intelligent recognition of dynamic images. The static images of glottis opening to the largest extent and closing to the smallest extent were screened automatically using color space transformation and image preprocessing. The glottal area was also quantized. As the tongue base movements affected the position of laryngoscope and saliva would result in unclear images, this study used the gray scale adaptive entropy value to set the threshold in order to establish an elimination system. The proposed system can improve the effect of automatically captured images of glottis and achieve an accuracy rate of 96%. In addition, the glottal area and area segmentation threshold were calculated effectively. The glottis area segmentation was corrected, and the glottal area waveform pattern was drawn automatically to assist in vocal fold diagnosis. When developing the intelligent recognition system for vocal fold disorders, this study analyzed the characteristic values of four vocal fold patterns, namely, normal vocal fold, vocal fold paralysis, vocal fold polyp, and vocal fold cyst. It also used the support vector machine classifier to identify vocal fold disorders and achieved an identification accuracy rate of 98.75%. The results can serve as a very valuable reference for diagnosis. PMID:25313026

  11. Multimodal eye recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Zhi; Du, Yingzi; Thomas, N. L.; Delp, Edward J., III

    2010-04-01

    Multimodal biometrics use more than one means of biometric identification to achieve higher recognition accuracy, since sometimes a unimodal biometric is not good enough used to do identification and classification. In this paper, we proposed a multimodal eye recognition system, which can obtain both iris and sclera patterns from one color eye image. Gabor filter and 1-D Log-Gabor filter algorithms have been applied as the iris recognition algorithms. In sclera recognition, we introduced automatic sclera segmentation, sclera pattern enhancement, sclera pattern template generation, and sclera pattern matching. We applied kernelbased matching score fusion to improve the performance of the eye recognition system. The experimental results show that the proposed eye recognition method can achieve better performance compared to unimodal biometric identification, and the accuracy of our proposed kernel-based matching score fusion method is higher than two classic linear matching score fusion methods: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

  12. Method and System for Object Recognition Search

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A. (Inventor); Duong, Vu A. (Inventor); Stubberud, Allen R. (Inventor)

    2012-01-01

    A method for object recognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable object recognition.

  13. Automatic Imitation

    ERIC Educational Resources Information Center

    Heyes, Cecilia

    2011-01-01

    "Automatic imitation" is a type of stimulus-response compatibility effect in which the topographical features of task-irrelevant action stimuli facilitate similar, and interfere with dissimilar, responses. This article reviews behavioral, neurophysiological, and neuroimaging research on automatic imitation, asking in what sense it is "automatic"…

  14. Xa21D encodes a receptor-like molecule with a leucine-rich repeat domain that determines race-specific recognition and is subject to adaptive evolution.

    PubMed Central

    Wang, G L; Ruan, D L; Song, W Y; Sideris, S; Chen, L; Pi, L Y; Zhang, S; Zhang, Z; Fauquet, C; Gaut, B S; Whalen, M C; Ronald, P C

    1998-01-01

    The rice Xa21 gene confers resistance to Xanthomonas oryzae pv oryzae in a race-specific manner. Analysis of the inheritance patterns and resistance spectra of transgenic plants carrying six Xa21 gene family members indicated that one member, designated Xa21D, displayed a resistance spectrum identical to that observed for Xa21 but conferred only partial resistance. Xa21D encodes a receptor-like protein carrying leucine-rich repeat (LRR) motifs in the presumed extracellular domain. The Xa21D transcript terminates shortly after the stop codon introduced by the retrotransposon Retrofit. Comparison of nucleotide substitutions in the LRR coding regions of Xa21 and Xa21D provided evidence of adaptive selection. Both functional and evolutionary evidence indicates that the Xa21D LRR domain controls race-specific pathogen recognition. PMID:9596635

  15. Guinea pig-adapted foot-and-mouth disease virus with altered receptor recognition can productively infect a natural host.

    PubMed

    Núñez, José I; Molina, Nicolas; Baranowski, Eric; Domingo, Esteban; Clark, Stuart; Burman, Alison; Berryman, Stephen; Jackson, Terry; Sobrino, Francisco

    2007-08-01

    We report that adaptation to infect the guinea pig did not modify the capacity of foot-and-mouth disease virus (FMDV) to kill suckling mice and to cause an acute and transmissible disease in the pig, an important natural host for this pathogen. Adaptive amino acid replacements (I(248)-->T in 2C, Q(44)-->R in 3A, and L(147)-->P in VP1), selected upon serial passages of a type C FMDV isolated from swine (biological clone C-S8c1) in the guinea pig, were maintained after virus multiplication in swine and suckling mice. However, the adaptive replacement L(147)-->P, next to the integrin-binding RGD motif at the GH loop in VP1, abolished growth of the virus in different established cell lines and modified its antigenicity. In contrast, primary bovine thyroid cell cultures could be productively infected by viruses with replacement L(147)-->P, and this infection was inhibited by antibodies to alphavbeta6 and by an FMDV-derived RGD-containing peptide, suggesting that integrin alphavbeta6 may be used as a receptor for these mutants in the animal (porcine, guinea pig, and suckling mice) host. Substitution T(248)-->N in 2C was not detectable in C-S8c1 but was present in a low proportion of the guinea pig-adapted virus. This substitution became rapidly dominant in the viral population after the reintroduction of the guinea pig-adapted virus into pigs. These observations illustrate how the appearance of minority variant viruses in an unnatural host can result in the dominance of these viruses on reinfection of the original host species. PMID:17522230

  16. Automatic parameter adjustment of difference of Gaussian (DoG) filter to improve OT-MACH filter performance for target recognition applications

    NASA Astrophysics Data System (ADS)

    Alkandri, Ahmad; Gardezi, Akber; Bangalore, Nagachetan; Birch, Philip; Young, Rupert; Chatwin, Chris

    2011-11-01

    A wavelet-modified frequency domain Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter has been trained using 3D CAD models and tested on real target images acquired from a Forward Looking Infra Red (FLIR) sensor. The OT-MACH filter can be used to detect and discriminate predefined targets from a cluttered background. The FLIR sensor extends the filter's ability by increasing the range of detection by exploiting the heat signature differences between the target and the background. A Difference of Gaussians (DoG) based wavelet filter has been use to improve the OT-MACH filter discrimination ability and distortion tolerance. Choosing the right standard deviation values of the two Gaussians comprising the filter is critical. In this paper we present a new technique for auto adjustment of the DoG filter parameters driven by the expected target size. Tests were carried on images acquired by the Apache AH-64 helicopter mounted FLIR sensor, results showing an overall improvement in the recognition of target objects present within the IR images.

  17. PCA facial expression recognition

    NASA Astrophysics Data System (ADS)

    El-Hori, Inas H.; El-Momen, Zahraa K.; Ganoun, Ali

    2013-12-01

    This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. The comparative study of Facial Expression Recognition (FER) techniques namely Principal Component's analysis (PCA) and PCA with Gabor filters (GF) is done. The objective of this research is to show that PCA with Gabor filters is superior to the first technique in terms of recognition rate. To test and evaluates their performance, experiments are performed using real database by both techniques. The universally accepted five principal emotions to be recognized are: Happy, Sad, Disgust and Angry along with Neutral. The recognition rates are obtained on all the facial expressions.

  18. Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2008-04-01

    Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

  19. Evaluation of a Blood Glucose Monitoring System with Automatic High- and Low-Pattern Recognition Software in Insulin-Using Patients: Pattern Detection and Patient-Reported Insights

    PubMed Central

    Grady, Mike; Campbell, Denise; MacLeod, Kirsty; Srinivasan, Aparna

    2013-01-01

    Background This study aimed to evaluate the performance of a glucose pattern recognition tool incorporated in a blood glucose monitoring system (BGMS) and its association with clinical measures, and to assess user perception and understanding of the pattern messages they receive. Methods Participants had type 1 or type 2 diabetes mellitus and were self-adjusting insulin doses for ≥1 year. During a 4-week home testing period, participants performed ≥6 daily self-tests, adjusted their insulin regimen based on BGMS results, and recorded pattern messages in the logbook. Participants reflected on usability of the pattern tool in a questionnaire. Results Study participants (n = 101) received a mean ± standard deviation of 4.5 ± 1.9 pattern messages per week (3.6 ± 1.8 high glucose patterns and 0.9 ± 1.3 low glucose patterns). Most received ≥1 high (96.5%) and/or ≥1 low (46.0%) pattern message per week. The average number of high- and low-pattern messages per week was associated with higher and lower, respectively, baseline hemoglobin A1c (p < .01) and fasting plasma glucose (p < .05). Participants found high- and low-pattern messages clear and easy to understand (84.2% and 83.2%, respectively) and considered the frequency of low (82.0%) and high (63.4%) pattern messages about right. Overall, 71.3% of participants indicated they preferred to use a meter with pattern messages. Conclusions The on-device Pattern tool identified meaningful blood glucose patterns, highlighting potential opportunities for improving glycemic control in patients who self-adjust their insulin. PMID:23911178

  20. WE-A-17A-06: Evaluation of An Automatic Interstitial Catheter Digitization Algorithm That Reduces Treatment Planning Time and Provide Means for Adaptive Re-Planning in HDR Brachytherapy of Gynecologic Cancers

    SciTech Connect

    Dise, J; Liang, X; Lin, L; Teo, B

    2014-06-15

    Purpose: To evaluate an automatic interstitial catheter digitization algorithm that reduces treatment planning time and provide means for adaptive re-planning in HDR Brachytherapy of Gynecologic Cancers. Methods: The semi-automatic catheter digitization tool utilizes a region growing algorithm in conjunction with a spline model of the catheters. The CT images were first pre-processed to enhance the contrast between the catheters and soft tissue. Several seed locations were selected in each catheter for the region growing algorithm. The spline model of the catheters assisted in the region growing by preventing inter-catheter cross-over caused by air or metal artifacts. Source dwell positions from day one CT scans were applied to subsequent CTs and forward calculated using the automatically digitized catheter positions. This method was applied to 10 patients who had received HDR interstitial brachytherapy on an IRB approved image-guided radiation therapy protocol. The prescribed dose was 18.75 or 20 Gy delivered in 5 fractions, twice daily, over 3 consecutive days. Dosimetric comparisons were made between automatic and manual digitization on day two CTs. Results: The region growing algorithm, assisted by the spline model of the catheters, was able to digitize all catheters. The difference between automatic and manually digitized positions was 0.8±0.3 mm. The digitization time ranged from 34 minutes to 43 minutes with a mean digitization time of 37 minutes. The bulk of the time was spent on manual selection of initial seed positions and spline parameter adjustments. There was no significance difference in dosimetric parameters between the automatic and manually digitized plans. D90% to the CTV was 91.5±4.4% for the manual digitization versus 91.4±4.4% for the automatic digitization (p=0.56). Conclusion: A region growing algorithm was developed to semi-automatically digitize interstitial catheters in HDR brachytherapy using the Syed-Neblett template. This automatic

  1. Catalytic adaptive recognition of thiol (SH) and selenol (SeH) groups toward synthesis of functionalized vinyl monomers.

    PubMed

    Ananikov, Valentine P; Orlov, Nikolay V; Zalesskiy, Sergey S; Beletskaya, Irina P; Khrustalev, Victor N; Morokuma, Keiji; Musaev, Djamaladdin G

    2012-04-18

    An unprecedented sustainable procedure was developed to produce functionalized vinyl monomers H(2)C═C(R)(FG) starting from a mixture of sulfur and selenium compounds as a functional group donor (FG = S or Se). The reaction serves as a model for efficient utilization of natural resources of sulfur feedstock in oil and technological sources of sulfur/selenium. The catalytic system is reported with amazing ability to recognize SH/SeH groups in the mixture and selectively incorporate them into valuable organic products via wastes-free atom-economic reaction with alkynes (HC≡CR). Formation of catalyst active site and the mechanism of the catalytic reaction were revealed by joint experimental and theoretical study. The difference in reactivity of μ(1)- and μ(2)-type chalcogen atoms attached to the metal was established and was shown to play the key role in the action of palladium catalyst. An approach to solve a challenging problem of dynamically changed reaction mixture was demonstrated using adaptive tuning of the catalyst. The origins of the adaptive tuning effect were investigated at molecular level and were found to be governed by the nature of metal-chalcogen bond.

  2. Individual vocal signatures in barn owl nestlings: does individual recognition have an adaptive role in sibling vocal competition?

    PubMed

    Dreiss, A N; Ruppli, C A; Roulin, A

    2014-01-01

    To compete over limited parental resources, young animals communicate with their parents and siblings by producing honest vocal signals of need. Components of begging calls that are sensitive to food deprivation may honestly signal need, whereas other components may be associated with individual-specific attributes that do not change with time such as identity, sex, absolute age and hierarchy. In a sib-sib communication system where barn owl (Tyto alba) nestlings vocally negotiate priority access to food resources, we show that calls have individual signatures that are used by nestlings to recognize which siblings are motivated to compete, even if most vocalization features vary with hunger level. Nestlings were more identifiable when food-deprived than food-satiated, suggesting that vocal identity is emphasized when the benefit of winning a vocal contest is higher. In broods where siblings interact iteratively, we speculate that individual-specific signature permits siblings to verify that the most vocal individual in the absence of parents is the one that indeed perceived the food brought by parents. Individual recognition may also allow nestlings to associate identity with individual-specific characteristics such as position in the within-brood dominance hierarchy. Calls indeed revealed age hierarchy and to a lower extent sex and absolute age. Using a cross-fostering experimental design, we show that most acoustic features were related to the nest of origin (but not the nest of rearing), suggesting a genetic or an early developmental effect on the ontogeny of vocal signatures. To conclude, our study suggests that sibling competition has promoted the evolution of vocal behaviours that signal not only hunger level but also intrinsic individual characteristics such as identity, family, sex and age. PMID:24266879

  3. Deformable image registration based automatic CT-to-CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting

    SciTech Connect

    Kumarasiri, Akila Siddiqui, Farzan; Liu, Chang; Yechieli, Raphael; Shah, Mira; Pradhan, Deepak; Zhong, Hualiang; Chetty, Indrin J.; Kim, Jinkoo

    2014-12-15

    Purpose: To evaluate the clinical potential of deformable image registration (DIR)-based automatic propagation of physician-drawn contours from a planning CT to midtreatment CT images for head and neck (H and N) adaptive radiotherapy. Methods: Ten H and N patients, each with a planning CT (CT1) and a subsequent CT (CT2) taken approximately 3–4 week into treatment, were considered retrospectively. Clinically relevant organs and targets were manually delineated by a radiation oncologist on both sets of images. Four commercial DIR algorithms, two B-spline-based and two Demons-based, were used to deform CT1 and the relevant contour sets onto corresponding CT2 images. Agreement of the propagated contours with manually drawn contours on CT2 was visually rated by four radiation oncologists in a scale from 1 to 5, the volume overlap was quantified using Dice coefficients, and a distance analysis was done using center of mass (CoM) displacements and Hausdorff distances (HDs). Performance of these four commercial algorithms was validated using a parameter-optimized Elastix DIR algorithm. Results: All algorithms attained Dice coefficients of >0.85 for organs with clear boundaries and those with volumes >9 cm{sup 3}. Organs with volumes <3 cm{sup 3} and/or those with poorly defined boundaries showed Dice coefficients of ∼0.5–0.6. For the propagation of small organs (<3 cm{sup 3}), the B-spline-based algorithms showed higher mean Dice values (Dice = 0.60) than the Demons-based algorithms (Dice = 0.54). For the gross and planning target volumes, the respective mean Dice coefficients were 0.8 and 0.9. There was no statistically significant difference in the Dice coefficients, CoM, or HD among investigated DIR algorithms. The mean radiation oncologist visual scores of the four algorithms ranged from 3.2 to 3.8, which indicated that the quality of transferred contours was “clinically acceptable with minor modification or major modification in a small number of contours

  4. Image Quality and Radiation Dose of CT Coronary Angiography with Automatic Tube Current Modulation and Strong Adaptive Iterative Dose Reduction Three-Dimensional (AIDR3D)

    PubMed Central

    Shen, Hesong; Dai, Guochao; Luo, Mingyue; Duan, Chaijie; Cai, Wenli; Liang, Dan; Wang, Xinhua; Zhu, Dongyun; Li, Wenru; Qiu, Jianping

    2015-01-01

    Purpose To investigate image quality and radiation dose of CT coronary angiography (CTCA) scanned using automatic tube current modulation (ATCM) and reconstructed by strong adaptive iterative dose reduction three-dimensional (AIDR3D). Methods Eighty-four consecutive CTCA patients were collected for the study. All patients were scanned using ATCM and reconstructed with strong AIDR3D, standard AIDR3D and filtered back-projection (FBP) respectively. Two radiologists who were blinded to the patients' clinical data and reconstruction methods evaluated image quality. Quantitative image quality evaluation included image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). To evaluate image quality qualitatively, coronary artery is classified into 15 segments based on the modified guidelines of the American Heart Association. Qualitative image quality was evaluated using a 4-point scale. Radiation dose was calculated based on dose-length product. Results Compared with standard AIDR3D, strong AIDR3D had lower image noise, higher SNR and CNR, their differences were all statistically significant (P<0.05); compared with FBP, strong AIDR3D decreased image noise by 46.1%, increased SNR by 84.7%, and improved CNR by 82.2%, their differences were all statistically significant (P<0.05 or 0.001). Segments with diagnostic image quality for strong AIDR3D were 336 (100.0%), 486 (96.4%), and 394 (93.8%) in proximal, middle, and distal part respectively; whereas those for standard AIDR3D were 332 (98.8%), 472 (93.7%), 378 (90.0%), respectively; those for FBP were 217 (64.6%), 173 (34.3%), 114 (27.1%), respectively; total segments with diagnostic image quality in strong AIDR3D (1216, 96.5%) were higher than those of standard AIDR3D (1182, 93.8%) and FBP (504, 40.0%); the differences between strong AIDR3D and standard AIDR3D, strong AIDR3D and FBP were all statistically significant (P<0.05 or 0.001). The mean effective radiation dose was (2.55±1.21) mSv. Conclusion

  5. Range-adaptive standoff recognition of explosive fingerprints on solid surfaces using a supervised learning method and laser-induced breakdown spectroscopy.

    PubMed

    Gaona, Inmaculada; Serrano, Jorge; Moros, Javier; Laserna, J Javier

    2014-05-20

    The distance between the sensor and the target is a particularly critical factor for an issue as crucial as explosive residues recognition when a laser-assisted spectroscopic technique operates in a standoff configuration. Particularly for laser ablation, variations in operational range influence the induced plasmas as well as the sensitivity of their ensuing optical emissions, thereby confining the attributes used in sorting methods. Though efficient classification models based on optical emissions gathered under specific conditions have been developed, their successful performance on any variable information is limited. Hence, to test new information by a designed model, data must be acquired under operational conditions totally matching those used during modeling. Otherwise, the new expected scenario needs to be previously modeled. To facing both this restriction and this time-consuming mission, a novel strategy is proposed in this work. On the basis of machine learning methods, the strategy stems from a decision boundary function designed for a defined set of experimental conditions. Next, particular semisupervised models to the envisaged conditions are obtained adaptively on the basis of changes in laser fluence and light emission with variation of the sensor-to-target distance. Hence, the strategy requires only a little prior information, therefore ruling out the tedious and time-consuming process of modeling all the expected distant scenes. Residues of ordinary materials (olive oil, fuel oil, motor oils, gasoline, car wax and hand cream) hardly cause confusion in alerting the presence of an explosive (DNT, TNT, RDX, or PETN) when tested within a range from 30 to 50 m with varying laser irradiance between 8.2 and 1.3 GW cm(-2). With error rates of around 5%, the experimental assessments confirm that this semisupervised model suitably addresses the recognition of organic residues on aluminum surfaces under different operational conditions. PMID:24773280

  6. Neural networks: Alternatives to conventional techniques for automatic docking

    NASA Technical Reports Server (NTRS)

    Vinz, Bradley L.

    1994-01-01

    Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels. Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes. Neural networks can reduce the computational time required by existing image processing and control software. In addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, enabling enhanced performance, redundancy, and fault tolerance. Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.

  7. Natural and adaptive IgM antibodies in the recognition of tumor-associated antigens of breast cancer (Review)

    PubMed Central

    DÍAZ-ZARAGOZA, MARIANA; HERNÁNDEZ-ÁVILA, RICARDO; VIEDMA-RODRÍGUEZ, RUBÍ; ARENAS-ARANDA, DIEGO; OSTOA-SALOMA, PEDRO

    2015-01-01

    For early detection of cancer, education and screening are important, but the most critical factor is the development of early diagnostic tools. Methods that recognize the warning signs of cancer and take prompt action lead to an early diagnosis; simple tests can identify individuals in a healthy population who have the disease but have not developed symptoms. Early detection of cancer is significant and is one of the most promising approaches by which to reduce the growing cancer burden and guide curative treatment. The early diagnosis of patients with breast cancer is challenging, since it is the most common cancer in women worldwide. Despite the advent of mammography in screening for breast cancer, low-resource, low-cost alternative tools must be implemented to complement mammography findings. IgM is part of the first line of defense of an organism and is responsible for recognizing and eliminating infectious particles and removing transformed cells. Most studies on breast cancer have focused on the development of IgG-like molecules as biomarkers or as a treatment for the advanced stages of cancer, but autoantibodies (IgM) and tumor-associated antigens (proteins or carbohydrates with aberrant structures) have not been examined as early diagnostic tools for breast cancer. The present review summarizes the function of natural and adaptive IgM in eliminating cancer cells in the early stages of pathology and their value as early diagnostic tools. IgM, as a component of the immune system, is being used to identify tumor-associated antigens and tumor-associated carbohydrate antigens. PMID:26133558

  8. Phage anti-immunocomplex assay for clomazone: two-site recognition increasing assay specificity and facilitating adaptation into an on-site format.

    PubMed

    Rossotti, M A; Carlomagno, M; González-Techera, A; Hammock, B D; Last, J; González-Sapienza, G

    2010-11-01

    The impact of the use of herbicides in agriculture can be minimized by compliance with good management practices that reduce the amount used and their release into the environment. Simple tests that provide real time on-site information about these chemicals are a major aid for these programs. In this work, we show that phage anti-immunocomplex assay (PHAIA), a method that uses phage-borne peptides to detect the formation of antibody-analyte immunocomplexes, is an advantageous technology to produce such field tests. A monoclonal antibody to the herbicide clomazone was raised and used in the development of conventional competitive and noncompetitive PHAIA immunoassays. The sensitivity attained with the PHAIA format was over 10 times higher than that of the competitive format. The cross-reactivity of the two methods was also compared using structurally related compounds, and we observed that the two-site binding of PHAIA "double-checks" the recognition of the analyte, thereby increasing the assay specificity. The positive readout of the noncompetitive PHAIA method allowed adaptation of the assay into a rapid and simple format where as little as 0.4 ng/mL clomazone (more than 10-fold lower than the proposed standard) in water samples from a rice field could be easily detected by simple visual inspection.

  9. Phage Anti-Immunocomplex Assay (PHAIA) for clomazone: Two-site recognition increases assay specificity and facilitates adaptation into a rapid on-site format

    PubMed Central

    Rossotti, M.A.; Carlomagno, M.; González-Techera, A.; Hammock, B.D.; Last, J.; González-Sapienza, G.

    2010-01-01

    The impact of the use of herbicides in agriculture can be minimized by compliance with good management practices that reduce the amount used and their release into the environment. Simple tests that provide real time on-site information about these chemicals are a major aid for these programs. In this work we show that PHAIA, a method that uses phage-borne peptides to detect the formation of antibody-analyte immunocomplexes, is an advantageous technology to produce such field tests. A monoclonal antibody to the herbicide clomazone was raised and used in the development of conventional competitive and noncompetitive PHAIA immunoassays. The sensitivity attained with the PHAIA format was over ten times higher than that of the competitive format. The cross-reactivity of the two methods was also compared by using structurally related compounds, and we observed that the two-site binding of PHAIA “double-checks” the recognition of the analyte, thereby increasing the assay specificity. The positive readout of the noncompetitive PHAIA method allowed adaptation of the assay into a rapid and simple format where as little as 0.4 ng/ml of clomazone (more than 10-fold lower than the proposed standard) in water samples from a rice field could be easily detected by simple visual inspection. PMID:20886819

  10. Automatic recognition of emotions from facial expressions

    NASA Astrophysics Data System (ADS)

    Xue, Henry; Gertner, Izidor

    2014-06-01

    In the human-computer interaction (HCI) process it is desirable to have an artificial intelligent (AI) system that can identify and categorize human emotions from facial expressions. Such systems can be used in security, in entertainment industries, and also to study visual perception, social interactions and disorders (e.g. schizophrenia and autism). In this work we survey and compare the performance of different feature extraction algorithms and classification schemes. We introduce a faster feature extraction method that resizes and applies a set of filters to the data images without sacrificing the accuracy. In addition, we have enhanced SVM to multiple dimensions while retaining the high accuracy rate of SVM. The algorithms were tested using the Japanese Female Facial Expression (JAFFE) Database and the Database of Faces (AT&T Faces).

  11. Automatic speech recognition in severe environments

    NASA Astrophysics Data System (ADS)

    Human-machine interaction by voice was analyzed. The potential for improving the safety and effectiveness of its forces by making electronic and electromechanical devices directly responsive to the human voice and able to respond by voice is recognized. The benefits of this capability are noteworthy in situations where the individual is engaged in such hands/eyes-busy tasks as flying an airplane or operating a tank. Voice control of navigational displays, information files, and weapons systems could relieve the information loads on visual and manual channels.

  12. A new method of automatic processing of seismic waves: waveform modeling by using Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Kodera, Y.; Sakai, S.

    2012-12-01

    Development of a method of automatic processing of seismic waves is needed since there are limitations to manually picking out earthquake events from seismograms. However, there is no practical method to automatically detect arrival times of P and S waves in seismograms. One typical example of previously proposed methods is automatic detection by using AR model (e.g. Kitagawa et al., 2004). This method seems not to be effective for seismograms contaminated with spike noise, because it cannot distinguish non-stationary signals generated by earthquakes from those generated by noise. The difficulty of distinguishing the signals is caused by the fact that the automatic detection system has a lack of information on time series variation of seismic waves. We expect that an automatic detection system that includes the information on seismic waves is more effective for seismograms contaminated with noise. So we try to adapt Hidden Markov Model (HMM) to construct seismic wave models and establish a new automatic detection method. HMM has been widely used in many fields such as voice recognition (e.g. Bishop, 2006). With the use of HMM, P- or S-waveform models that include envelops can be constructed directly and semi-automatically from lots of observed waveform data of P or S waves. These waveform models are expected to become more robust if the quantity of observation data increases. We have constructed seismic wave models based on HMM from seismograms observed in Ashio, Japan. By using these models, we have tried automatic detection of arrival times of earthquake events in Ashio. Results show that automatic detection based on HMM is more effective for seismograms contaminated with noise than that based on AR model.

  13. Automatic Stabilization

    NASA Technical Reports Server (NTRS)

    Haus, FR

    1936-01-01

    This report lays more stress on the principles underlying automatic piloting than on the means of applications. Mechanical details of servomotors and the mechanical release device necessary to assure instantaneous return of the controls to the pilot in case of malfunction are not included. Descriptions are provided of various commercial systems.

  14. Automatic violence detection in digital movies

    NASA Astrophysics Data System (ADS)

    Fischer, Stephan

    1996-11-01

    Research on computer-based recognition of violence is scant. We are working on the automatic recognition of violence in digital movies, a first step towards the goal of a computer- assisted system capable of protecting children against TV programs containing a great deal of violence. In the video domain a collision detection and a model-mapping to locate human figures are run, while the creation and comparison of fingerprints to find certain events are run int he audio domain. This article centers on the recognition of fist- fights in the video domain and on the recognition of shots, explosions and cries in the audio domain.

  15. Automatic translation among spoken languages

    NASA Astrophysics Data System (ADS)

    Walter, Sharon M.; Costigan, Kelly

    1994-02-01

    The Machine Aided Voice Translation (MAVT) system was developed in response to the shortage of experienced military field interrogators with both foreign language proficiency and interrogation skills. Combining speech recognition, machine translation, and speech generation technologies, the MAVT accepts an interrogator's spoken English question and translates it into spoken Spanish. The spoken Spanish response of the potential informant can then be translated into spoken English. Potential military and civilian applications for automatic spoken language translation technology are discussed in this paper.

  16. Automatic translation among spoken languages

    NASA Technical Reports Server (NTRS)

    Walter, Sharon M.; Costigan, Kelly

    1994-01-01

    The Machine Aided Voice Translation (MAVT) system was developed in response to the shortage of experienced military field interrogators with both foreign language proficiency and interrogation skills. Combining speech recognition, machine translation, and speech generation technologies, the MAVT accepts an interrogator's spoken English question and translates it into spoken Spanish. The spoken Spanish response of the potential informant can then be translated into spoken English. Potential military and civilian applications for automatic spoken language translation technology are discussed in this paper.

  17. Adaptive detection of missed text areas in OCR outputs: application to the automatic assessment of OCR quality in mass digitization projects

    NASA Astrophysics Data System (ADS)

    Ben Salah, Ahmed; Ragot, Nicolas; Paquet, Thierry

    2013-01-01

    The French National Library (BnF*) has launched many mass digitization projects in order to give access to its collection. The indexation of digital documents on Gallica (digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition softwares (OCR). OCR softwares have become increasingly complex systems composed of several subsystems dedicated to the analysis and the recognition of the elements in a page. However, the reliability of these systems is always an issue at stake. Indeed, in some cases, we can find errors in OCR outputs that occur because of an accumulation of several errors at different levels in the OCR process. One of the frequent errors in OCR outputs is the missed text components. The presence of such errors may lead to severe defects in digital libraries. In this paper, we investigate the detection of missed text components to control the OCR results from the collections of the French National Library. Our verification approach uses local information inside the pages based on Radon transform descriptors and Local Binary Patterns descriptors (LBP) coupled with OCR results to control their consistency. The experimental results show that our method detects 84.15% of the missed textual components, by comparing the OCR ALTO files outputs (produced by the service providers) to the images of the document.

  18. AUTOMATIC COUNTER

    DOEpatents

    Robinson, H.P.

    1960-06-01

    An automatic counter of alpha particle tracks recorded by a sensitive emulsion of a photographic plate is described. The counter includes a source of mcdulated dark-field illumination for developing light flashes from the recorded particle tracks as the photographic plate is automatically scanned in narrow strips. Photoelectric means convert the light flashes to proportional current pulses for application to an electronic counting circuit. Photoelectric means are further provided for developing a phase reference signal from the photographic plate in such a manner that signals arising from particle tracks not parallel to the edge of the plate are out of phase with the reference signal. The counting circuit includes provision for rejecting the out-of-phase signals resulting from unoriented tracks as well as signals resulting from spurious marks on the plate such as scratches, dust or grain clumpings, etc. The output of the circuit is hence indicative only of the tracks that would be counted by a human operator.

  19. Automatic water inventory, collecting, and dispensing unit

    NASA Technical Reports Server (NTRS)

    Hall, J. B., Jr.; Williams, E. F.

    1972-01-01

    Two cylindrical tanks with piston bladders and associated components for automatic filling and emptying use liquid inventory readout devices in control of water flow. Unit provides for adaptive water collection, storage, and dispensation in weightlessness environment.

  20. Image quality and radiation reduction of 320-row area detector CT coronary angiography with optimal tube voltage selection and an automatic exposure control system: comparison with body mass index-adapted protocol.

    PubMed

    Lim, Jiyeon; Park, Eun-Ah; Lee, Whal; Shim, Hackjoon; Chung, Jin Wook

    2015-06-01

    To assess the image quality and radiation exposure of 320-row area detector computed tomography (320-ADCT) coronary angiography with optimal tube voltage selection with the guidance of an automatic exposure control system in comparison with a body mass index (BMI)-adapted protocol. Twenty-two patients (study group) underwent 320-ADCT coronary angiography using an automatic exposure control system with the target standard deviation value of 33 as the image quality index and the lowest possible tube voltage. For comparison, a sex- and BMI-matched group (control group, n = 22) using a BMI-adapted protocol was established. Images of both groups were reconstructed by an iterative reconstruction algorithm. For objective evaluation of the image quality, image noise, vessel density, signal to noise ratio (SNR), and contrast to noise ratio (CNR) were measured. Two blinded readers then subjectively graded the image quality using a four-point scale (1: nondiagnostic to 4: excellent). Radiation exposure was also measured. Although the study group tended to show higher image noise (14.1 ± 3.6 vs. 9.3 ± 2.2 HU, P = 0.111) and higher vessel density (665.5 ± 161 vs. 498 ± 143 HU, P = 0.430) than the control group, the differences were not significant. There was no significant difference between the two groups for SNR (52.5 ± 19.2 vs. 60.6 ± 21.8, P = 0.729), CNR (57.0 ± 19.8 vs. 67.8 ± 23.3, P = 0.531), or subjective image quality scores (3.47 ± 0.55 vs. 3.59 ± 0.56, P = 0.960). However, radiation exposure was significantly reduced by 42 % in the study group (1.9 ± 0.8 vs. 3.6 ± 0.4 mSv, P = 0.003). Optimal tube voltage selection with the guidance of an automatic exposure control system in 320-ADCT coronary angiography allows substantial radiation reduction without significant impairment of image quality, compared to the results obtained using a BMI-based protocol. PMID:25604967

  1. Image quality and radiation reduction of 320-row area detector CT coronary angiography with optimal tube voltage selection and an automatic exposure control system: comparison with body mass index-adapted protocol.

    PubMed

    Lim, Jiyeon; Park, Eun-Ah; Lee, Whal; Shim, Hackjoon; Chung, Jin Wook

    2015-06-01

    To assess the image quality and radiation exposure of 320-row area detector computed tomography (320-ADCT) coronary angiography with optimal tube voltage selection with the guidance of an automatic exposure control system in comparison with a body mass index (BMI)-adapted protocol. Twenty-two patients (study group) underwent 320-ADCT coronary angiography using an automatic exposure control system with the target standard deviation value of 33 as the image quality index and the lowest possible tube voltage. For comparison, a sex- and BMI-matched group (control group, n = 22) using a BMI-adapted protocol was established. Images of both groups were reconstructed by an iterative reconstruction algorithm. For objective evaluation of the image quality, image noise, vessel density, signal to noise ratio (SNR), and contrast to noise ratio (CNR) were measured. Two blinded readers then subjectively graded the image quality using a four-point scale (1: nondiagnostic to 4: excellent). Radiation exposure was also measured. Although the study group tended to show higher image noise (14.1 ± 3.6 vs. 9.3 ± 2.2 HU, P = 0.111) and higher vessel density (665.5 ± 161 vs. 498 ± 143 HU, P = 0.430) than the control group, the differences were not significant. There was no significant difference between the two groups for SNR (52.5 ± 19.2 vs. 60.6 ± 21.8, P = 0.729), CNR (57.0 ± 19.8 vs. 67.8 ± 23.3, P = 0.531), or subjective image quality scores (3.47 ± 0.55 vs. 3.59 ± 0.56, P = 0.960). However, radiation exposure was significantly reduced by 42 % in the study group (1.9 ± 0.8 vs. 3.6 ± 0.4 mSv, P = 0.003). Optimal tube voltage selection with the guidance of an automatic exposure control system in 320-ADCT coronary angiography allows substantial radiation reduction without significant impairment of image quality, compared to the results obtained using a BMI-based protocol.

  2. Self-adaptive iris image acquisition system

    NASA Astrophysics Data System (ADS)

    Dong, Wenbo; Sun, Zhenan; Tan, Tieniu; Qiu, Xianchao

    2008-03-01

    Iris image acquisition is the fundamental step of the iris recognition, but capturing high-resolution iris images in real-time is very difficult. The most common systems have small capture volume and demand users to fully cooperate with machines, which has become the bottleneck of iris recognition's application. In this paper, we aim at building an active iris image acquiring system which is self-adaptive to users. Two low resolution cameras are co-located in a pan-tilt-unit (PTU), for face and iris image acquisition respectively. Once the face camera detects face region in real-time video, the system controls the PTU to move towards the eye region and automatically zooms, until the iris camera captures an clear iris image for recognition. Compared with other similar works, our contribution is that we use low-resolution cameras, which can transmit image data much faster and are much cheaper than the high-resolution cameras. In the system, we use Haar-like cascaded feature to detect faces and eyes, linear transformation to predict the iris camera's position, and simple heuristic PTU control method to track eyes. A prototype device has been established, and experiments show that our system can automatically capture high-quality iris image in the range of 0.6m×0.4m×0.4m in average 3 to 5 seconds.

  3. Automatic transmission

    SciTech Connect

    Ohkubo, M.

    1988-02-16

    An automatic transmission is described combining a stator reversing type torque converter and speed changer having first and second sun gears comprising: (a) a planetary gear train composed of first and second planetary gears sharing one planetary carrier in common; (b) a clutch and requisite brakes to control the planetary gear train; and (c) a speed-increasing or speed-decreasing mechanism is installed both in between a turbine shaft coupled to a turbine of the stator reversing type torque converter and the first sun gear of the speed changer, and in between a stator shaft coupled to a reversing stator and the second sun gear of the speed changer.

  4. Automatic stabilization

    NASA Technical Reports Server (NTRS)

    Haus, FR

    1936-01-01

    This report concerns the study of automatic stabilizers and extends it to include the control of the three-control system of the airplane instead of just altitude control. Some of the topics discussed include lateral disturbed motion, static stability, the mathematical theory of lateral motion, and large angles of incidence. Various mechanisms and stabilizers are also discussed. The feeding of Diesel engines by injection pumps actuated by engine compression, achieves the required high speeds of injection readily and permits rigorous control of the combustible charge introduced into each cylinder and of the peak pressure in the resultant cycle.

  5. Automatic transmission

    SciTech Connect

    Miki, N.

    1988-10-11

    This patent describes an automatic transmission including a fluid torque converter, a first gear unit having three forward-speed gears and a single reverse gear, a second gear unit having a low-speed gear and a high-speed gear, and a hydraulic control system, the hydraulic control system comprising: a source of pressurized fluid; a first shift valve for controlling the shifting between the first-speed gear and the second-speed gear of the first gear unit; a second shift valve for controlling the shifting between the second-speed gear and the third-speed gear of the first gear unit; a third shift valve equipped with a spool having two positions for controlling the shifting between the low-speed gear and the high-speed gear of the second gear unit; a manual selector valve having a plurality of shift positions for distributing the pressurized fluid supply from the source of pressurized fluid to the first, second and third shift valves respectively; first, second and third solenoid valves corresponding to the first, second and third shift valves, respectively for independently controlling the operation of the respective shift valves, thereby establishing a six forward-speed automatic transmission by combining the low-speed gear and the high-speed gear of the second gear unit with each of the first-speed gear, the second speed gear and the third-speed gear of the first gear unit; and means to fixedly position the spool of the third shift valve at one of the two positions by supplying the pressurized fluid to the third shift valve when the manual selector valve is shifted to a particular shift position, thereby locking the second gear unit in one of low-speed gear and the high-speed gear, whereby the six forward-speed automatic transmission is converted to a three forward-speed automatic transmission when the manual selector valve is shifted to the particular shift position.

  6. Adaptive fingerprint image enhancement with emphasis on preprocessing of data.

    PubMed

    Bartůnek, Josef Ström; Nilsson, Mikael; Sällberg, Benny; Claesson, Ingvar

    2013-02-01

    This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases.

  7. Sensor agnostic object recognition using a map seeking circuit

    NASA Astrophysics Data System (ADS)

    Overman, Timothy L.; Hart, Michael

    2012-05-01

    Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

  8. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

    PubMed Central

    Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

    2016-01-01

    Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

  9. Automatic transmission

    SciTech Connect

    Aoki, H.

    1989-03-21

    An automatic transmission is described, comprising: a torque converter including an impeller having a connected member, a turbine having an input member and a reactor; and an automatic transmission mechanism having first to third clutches and plural gear units including a single planetary gear unit with a ring gear and a dual planetary gear unit with a ring gear. The single and dual planetary gear units have respective carriers integrally coupled with each other and respective sun gears integrally coupled with each other, the input member of the turbine being coupled with the ring gear of the single planetary gear unit through the first clutch, and being coupled with the sun gear through the second clutch. The connected member of the impeller is coupled with the ring gear of the dual planetary gear of the dual planetary gear unit is made to be and ring gear of the dual planetary gear unit is made to be restrained as required, and the carrier is coupled with an output member.

  10. Body-wide anatomy recognition in PET/CT images

    NASA Astrophysics Data System (ADS)

    Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Zhao, Liming; Torigian, Drew A.

    2015-03-01

    With the rapid growth of positron emission tomography/computed tomography (PET/CT)-based medical applications, body-wide anatomy recognition on whole-body PET/CT images becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem and seldom studied due to unclear anatomy reference frame and low spatial resolution of PET images as well as low contrast and spatial resolution of the associated low-dose CT images. We previously developed an automatic anatomy recognition (AAR) system [15] whose applicability was demonstrated on diagnostic computed tomography (CT) and magnetic resonance (MR) images in different body regions on 35 objects. The aim of the present work is to investigate strategies for adapting the previous AAR system to low-dose CT and PET images toward automated body-wide disease quantification. Our adaptation of the previous AAR methodology to PET/CT images in this paper focuses on 16 objects in three body regions - thorax, abdomen, and pelvis - and consists of the following steps: collecting whole-body PET/CT images from existing patient image databases, delineating all objects in these images, modifying the previous hierarchical models built from diagnostic CT images to account for differences in appearance in low-dose CT and PET images, automatically locating objects in these images following object hierarchy, and evaluating performance. Our preliminary evaluations indicate that the performance of the AAR approach on low-dose CT images achieves object localization accuracy within about 2 voxels, which is comparable to the accuracies achieved on diagnostic contrast-enhanced CT images. Object recognition on low-dose CT images from PET/CT examinations without requiring diagnostic contrast-enhanced CT seems feasible.

  11. Generalization of Hindi OCR Using Adaptive Segmentation and Font Files

    NASA Astrophysics Data System (ADS)

    Agrawal, Mudit; Ma, Huanfeng; Doermann, David

    In this chapter, we describe an adaptive Indic OCR system implemented as part of a rapidly retargetable language tool effort and extend work found in [20, 2]. The system includes script identification, character segmentation, training sample creation, and character recognition. For script identification, Hindi words are identified in bilingual or multilingual document images using features of the Devanagari script and support vector machine (SVM). Identified words are then segmented into individual characters, using a font-model-based intelligent character segmentation and recognition system. Using characteristics of structurally similar TrueType fonts, our system automatically builds a model to be used for the segmentation and recognition of the new script, independent of glyph composition. The key is a reliance on known font attributes. In our recognition system three feature extraction methods are used to demonstrate the importance of appropriate features for classification. The methods are tested on both Latin and non-Latin scripts. Results show that the character-level recognition accuracy exceeds 92% for non-Latin and 96% for Latin text on degraded documents. This work is a step toward the recognition of scripts of low-density languages which typically do not warrant the development of commercial OCR, yet often have complete TrueType font descriptions.

  12. Understanding Cognitive Development: Automaticity and the Early Years Child

    ERIC Educational Resources Information Center

    Gray, Colette

    2004-01-01

    In recent years a growing body of evidence has implicated deficits in the automaticity of fundamental facts such as word and number recognition in a range of disorders: including attention deficit hyperactivity disorder, dyslexia, apraxia and autism. Variously described as habits, fluency, chunking and over learning, automatic processes are best…

  13. [A novel automatic manufacture device for tissue micro-array].

    PubMed

    Wang, Chaohui; Chen, Chao; Zhang, Qunming; Jiang, Zhuangde; Wang, Teng; Meng, Tao

    2007-10-01

    A novel automatic manufacture device for tissue micro-array is introduced in this paper. Based on the analyses of task and process, the new device prototype is researched and developed. The device consists of a paraffin positioning module and a three-manipulator module. The control system is composed of accurate navigation sub-system, digital image recognition sub-system and punching-filling operating sub-system. The results of experiment demonstrate that the device can accomplish the operations such as image automatic recognition, accurate position, auto-punching and filling. It fulfills the requirements to automatic manufacture of tissue micro-array. PMID:18027675

  14. Adaptive Batch Mode Active Learning.

    PubMed

    Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

    2015-08-01

    Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

  15. Adaptive interface for spoken dialog

    NASA Astrophysics Data System (ADS)

    Dusan, Sorin; Flanagan, James

    2002-05-01

    Speech has become increasingly important in human-computer interaction. Spoken dialog interfaces rely on automatic speech recognition, speech synthesis, language understanding, and dialog management. A main issue in dialog systems is that they typically are limited to pre-programmed vocabularies and sets of sentences. The research reported here focuses on developing an adaptive spoken dialog interface capable of acquiring new linguistic units and their corresponding semantics during the human-computer interaction. The adaptive interface identifies unknown words and phrases in the users utterances and asks the user for the corresponding semantics. The user can provide the meaning or the semantic representation of the new linguistic units through multiple modalities, including speaking, typing, pointing, touching, or showing. The interface then stores the new linguistic units in a semantic grammar and creates new objects defining the corresponding semantic representation. This process takes place during natural interaction between user and computer and, thus, the interface does not have to be rewritten and compiled to incorporate the newly acquired language. Users can personalize the adaptive spoken interface for different domain applications, or according to their personal preferences. [Work supported by NSF.

  16. Automatic transmission

    SciTech Connect

    Miura, M.; Inuzuka, T.

    1986-08-26

    1. An automatic transmission with four forward speeds and one reverse position, is described which consists of: an input shaft; an output member; first and second planetary gear sets each having a sun gear, a ring gear and a carrier supporting a pinion in mesh with the sun gear and ring gear; the carrier of the first gear set, the ring gear of the second gear set and the output member all being connected; the ring gear of the first gear set connected to the carrier of the second gear set; a first clutch means for selectively connecting the input shaft to the sun gear of the first gear set, including friction elements, a piston selectively engaging the friction elements and a fluid servo in which hydraulic fluid is selectively supplied to the piston; a second clutch means for selectively connecting the input shaft to the sun gear of the second gear set a third clutch means for selectively connecting the input shaft to the carrier of the second gear set including friction elements, a piston selectively engaging the friction elements and a fluid servo in which hydraulic fluid is selectively supplied to the piston; a first drive-establishing means for selectively preventing rotation of the ring gear of the first gear set and the carrier of the second gear set in only one direction and, alternatively, in any direction; a second drive-establishing means for selectively preventing rotation of the sun gear of the second gear set; and a drum being open to the first planetary gear set, with a cylindrical intermediate wall, an inner peripheral wall and outer peripheral wall and forming the hydraulic servos of the first and third clutch means between the intermediate wall and the inner peripheral wall and between the intermediate wall and the outer peripheral wall respectively.

  17. Retina vascular network recognition

    NASA Astrophysics Data System (ADS)

    Tascini, Guido; Passerini, Giorgio; Puliti, Paolo; Zingaretti, Primo

    1993-09-01

    The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.

  18. Automatic inspection of leather surfaces

    NASA Astrophysics Data System (ADS)

    Poelzleitner, Wolfgang; Niel, Albert

    1994-10-01

    This paper describes the key elements of a system for detecting quality defects on leather surfaces. The inspection task must treat defects like scars, mite nests, warts, open fissures, healed scars, holes, pin holes, and fat folds. The industrial detection of these defects is difficult because of the large dimensions of the leather hides (2 m X 3 m), and the small dimensions of the defects (150 micrometers X 150 micrometers ). Pattern recognition approaches suffer from the fact that defects are hidden on an irregularly textured background, and can be hardly seen visually by human graders. We describe the methods tested for automatic classification using image processing, which include preprocessing, local feature description of texture elements, and final segmentation and grading of defects. We conclude with a statistical evaluation of the recognition error rate, and an outlook on the expected industrial performance.

  19. Toward automatic finite element analysis

    NASA Technical Reports Server (NTRS)

    Kela, Ajay; Perucchio, Renato; Voelcker, Herbert

    1987-01-01

    Two problems must be solved if the finite element method is to become a reliable and affordable blackbox engineering tool. Finite element meshes must be generated automatically from computer aided design databases and mesh analysis must be made self-adaptive. The experimental system described solves both problems in 2-D through spatial and analytical substructuring techniques that are now being extended into 3-D.

  20. A reinforcement learning approach in rotated image recognition and its convergence analysis

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan M.; Li, Yaqin

    2006-08-01

    One of the major problems in automatic target recognition (ATR) involves the recognition of images in different orientations. In a classical training-testing setup for an ATR for rotated targets using neural networks, the recognition is inherently static, and the performance is largely dependent on the range of orientation angles in the training process. To alleviate this problem, we propose a reinforcement learning (RL) approach for the ATR of rotated images. The RL is implemented in an adaptive critic design (ACD) framework wherein the ACD is mainly composed of the neuro-dynamic programming of an action network and a critic network. The proposed RL provides an adaptive learning and object recognition ability without a priori training. Numerical simulations demonstrate that the proposed ACD-based ATR system can effectively recognize rotated target with the whole range of 180° rotation. Analytic characterization of the learning algorithm provides a sufficient condition for its asymptotic convergence. Finally, we obtain an upper bound for the estimation error of the cost-to-go function under the asymptotic convergence condition.

  1. Automatic program debugging for intelligent tutoring systems

    SciTech Connect

    Murray, W.R.

    1986-01-01

    This thesis explores the process by which student programs can be automatically debugged in order to increase the instructional capabilities of these systems. This research presents a methodology and implementation for the diagnosis and correction of nontrivial recursive programs. In this approach, recursive programs are debugged by repairing induction proofs in the Boyer-Moore Logic. The potential of a program debugger to automatically debug widely varying novice programs in a nontrivial domain is proportional to its capabilities to reason about computational semantics. By increasing these reasoning capabilities a more powerful and robust system can result. This thesis supports these claims by examining related work in automated program debugging and by discussing the design, implementation, and evaluation of Talus, an automatic degugger for LISP programs. Talus relies on its abilities to reason about computational semantics to perform algorithm recognition, infer code teleology, and to automatically detect and correct nonsyntactic errors in student programs written in a restricted, but nontrivial, subset of LISP.

  2. Liver recognition based on statistical shape model in CT images

    NASA Astrophysics Data System (ADS)

    Xiang, Dehui; Jiang, Xueqing; Shi, Fei; Zhu, Weifang; Chen, Xinjian

    2016-03-01

    In this paper, an automatic method is proposed to recognize the liver on clinical 3D CT images. The proposed method effectively use statistical shape model of the liver. Our approach consist of three main parts: (1) model training, in which shape variability is detected using principal component analysis from the manual annotation; (2) model localization, in which a fast Euclidean distance transformation based method is able to localize the liver in CT images; (3) liver recognition, the initial mesh is locally and iteratively adapted to the liver boundary, which is constrained with the trained shape model. We validate our algorithm on a dataset which consists of 20 3D CT images obtained from different patients. The average ARVD was 8.99%, the average ASSD was 2.69mm, the average RMSD was 4.92mm, the average MSD was 28.841mm, and the average MSD was 13.31%.

  3. Automatic identification of species with neural networks.

    PubMed

    Hernández-Serna, Andrés; Jiménez-Segura, Luz Fernanda

    2014-01-01

    A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

  4. Automatic reading of hybridization filter images.

    PubMed

    Audic, S; Zanetti, G

    1995-10-01

    We describe a new technique for the automatic detection and characterization, in terms of location, size and intensity, of positive hybridization spots on hybridization filter images. We will also discuss how this information can be used to reconstruct the spot deposition pattern on the filter and thus identify the spots. The part of the algorithm dedicated to the detection of spots is derived from a technique used in astronomy for the automatic recognition of galaxies on photographic plates. This technique has been modified so that it becomes able to cope with the strongly varying background typical of hybridization filter images. The technique does not require any human intervention.

  5. Automatic identification of species with neural networks

    PubMed Central

    Jiménez-Segura, Luz Fernanda

    2014-01-01

    A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification. PMID:25392749

  6. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

    PubMed Central

    Hosseini, Monireh Sheikh; Zekri, Maryam

    2012-01-01

    Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

  7. Visual adaptation dominates bimodal visual-motor action adaptation

    PubMed Central

    de la Rosa, Stephan; Ferstl, Ylva; Bülthoff, Heinrich H.

    2016-01-01

    A long standing debate revolves around the question whether visual action recognition primarily relies on visual or motor action information. Previous studies mainly examined the contribution of either visual or motor information to action recognition. Yet, the interaction of visual and motor action information is particularly important for understanding action recognition in social interactions, where humans often observe and execute actions at the same time. Here, we behaviourally examined the interaction of visual and motor action recognition processes when participants simultaneously observe and execute actions. We took advantage of behavioural action adaptation effects to investigate behavioural correlates of neural action recognition mechanisms. In line with previous results, we find that prolonged visual exposure (visual adaptation) and prolonged execution of the same action with closed eyes (non-visual motor adaptation) influence action recognition. However, when participants simultaneously adapted visually and motorically – akin to simultaneous execution and observation of actions in social interactions - adaptation effects were only modulated by visual but not motor adaptation. Action recognition, therefore, relies primarily on vision-based action recognition mechanisms in situations that require simultaneous action observation and execution, such as social interactions. The results suggest caution when associating social behaviour in social interactions with motor based information. PMID:27029781

  8. The neuroecology of competitor recognition.

    PubMed

    Grether, Gregory F

    2011-11-01

    Territorial animals can be expected to distinguish among the types of competitors and noncompetitors that they encounter on a regular basis, including prospective mates and rivals of their own species, but they may not correctly classify individuals of other species. Closely related species often have similar phenotypes and this can cause confusion when formerly allopatric populations first come into contact. Errors in recognizing competitors can have important ecological and evolutionary effects. I review what is known about the mechanisms of competitor recognition in animals generally, focusing on cases in which the targets of recognition include other species. Case studies include damselflies, ants, skinks, salamanders, reef fishes, and birds. In general, recognition systems consist of a phenotypic cue (e.g., chemical, color, song), a neural template against which cues are compared, a motor response (e.g., aggression), and sensory integration circuits for context dependency of the response (if any). Little is known about how competitor recognition systems work at the neural level, but inferences about specificity of cues and about sensory integration can be drawn from the responses of territory residents to simulated intruders. Competitor recognition often involves multiple cues in the same, or different, sensory modalities. The same cues and templates are often, but not always, used for intraspecific and interspecific recognition. Experiments have shown that imprinting on local cues is common, which may enable templates to track evolved changes in cues automatically. The dependence of aggression and tolerance on context is important even in the simplest systems. Species in which mechanisms of competitor recognition are best known offer untapped opportunities to examine how competitor-recognition systems evolve (e.g., by comparing allopatric and sympatric populations). Cues that are gene products (peptides, proteins) may provide insights into rates of evolution

  9. Face Recognition System with Holographic Memory and Stereovision Technology

    NASA Astrophysics Data System (ADS)

    Honma, Satoshi; Yagisawa, Yasuaki; Momose, Hidetomo; Sekiguchi, Toru

    2011-09-01

    We have proposed a face recognition system with holographic memory and stereovision technology (FARSHAS). In this system, facial three-dimensional data is captured by stereovision technology and then the facial images at a position in front of the virtual camera is reconstructed automatically. Using the corrected facial images, we estimated theoretically the error rate of the facial recognition system.

  10. Voice reaction times with recognition for Commodore computers

    NASA Technical Reports Server (NTRS)

    Washburn, David A.; Putney, R. Thompson

    1990-01-01

    Hardware and software modifications are presented that allow for collection and recognition by a Commodore computer of spoken responses. Responses are timed with millisecond accuracy and automatically analyzed and scored. Accuracy data for this device from several experiments are presented. Potential applications and suggestions for improving recognition accuracy are also discussed.

  11. [Wearable Automatic External Defibrillators].

    PubMed

    Luo, Huajie; Luo, Zhangyuan; Jin, Xun; Zhang, Leilei; Wang, Changjin; Zhang, Wenzan; Tu, Quan

    2015-11-01

    Defibrillation is the most effective method of treating ventricular fibrillation(VF), this paper introduces wearable automatic external defibrillators based on embedded system which includes EGG measurements, bioelectrical impedance measurement, discharge defibrillation module, which can automatic identify VF signal, biphasic exponential waveform defibrillation discharge. After verified by animal tests, the device can realize EGG acquisition and automatic identification. After identifying the ventricular fibrillation signal, it can automatic defibrillate to abort ventricular fibrillation and to realize the cardiac electrical cardioversion.

  12. Features classification using support vector machine for a facial expression recognition system

    NASA Astrophysics Data System (ADS)

    Patil, Rajesh A.; Sahula, Vineet; Mandal, Atanendu S.

    2012-10-01

    A methodology for automatic facial expression recognition in image sequences is proposed, which makes use of the Candide wire frame model and an active appearance algorithm for tracking, and support vector machine (SVM) for classification. A face is detected automatically from the given image sequence and by adapting the Candide wire frame model properly on the first frame of face image sequence, facial features in the subsequent frames are tracked using an active appearance algorithm. The algorithm adapts the Candide wire frame model to the face in each of the frames and then automatically tracks the grid in consecutive video frames over time. We require that first frame of the image sequence corresponds to the neutral facial expression, while the last frame of the image sequence corresponds to greatest intensity of facial expression. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the SVM, which classify the facial expression into one of the classes viz happy, surprise, sadness, anger, disgust, and fear.

  13. Structure of the foot-and-mouth disease virus leader protease: a papain-like fold adapted for self-processing and eIF4G recognition.

    PubMed Central

    Guarné, A; Tormo, J; Kirchweger, R; Pfistermueller, D; Fita, I; Skern, T

    1998-01-01

    The leader protease of foot-and-mouth disease virus, as well as cleaving itself from the nascent viral polyprotein, disables host cell protein synthesis by specific proteolysis of a cellular protein: the eukaryotic initiation factor 4G (eIF4G). The crystal structure of the leader protease presented here comprises a globular catalytic domain reminiscent of that of cysteine proteases of the papain superfamily, and a flexible C-terminal extension found intruding into the substrate-binding site of an adjacent molecule. Nevertheless, the relative disposition of this extension and the globular domain to each other supports intramolecular self-processing. The different sequences of the two substrates cleaved during viral replication, the viral polyprotein (at LysLeuLys/GlyAlaGly) and eIF4G (at AsnLeuGly/ArgThrThr), appear to be recognized by distinct features in a narrow, negatively charged groove traversing the active centre. The structure illustrates how the prototype papain fold has been adapted to the requirements of an RNA virus. Thus, the protein scaffold has been reduced to a minimum core domain, with the active site being modified to increase specificity. Furthermore, surface features have been developed which enable C-terminal self-processing from the viral polyprotein. PMID:9857201

  14. Automatic discrimination of emotion from spoken Finnish.

    PubMed

    Toivanen, Juhani; Väyrynen, Eero; Seppänen, Tapio

    2004-01-01

    In this paper, experiments on the automatic discrimination of basic emotions from spoken Finnish are described. For the purpose of the study, a large emotional speech corpus of Finnish was collected; 14 professional actors acted as speakers, and simulated four primary emotions when reading out a semantically neutral text. More than 40 prosodic features were derived and automatically computed from the speech samples. Two application scenarios were tested: the first scenario was speaker-independent for a small domain of speakers while the second scenario was completely speaker-independent. Human listening experiments were conducted to assess the perceptual adequacy of the emotional speech samples. Statistical classification experiments indicated that, with the optimal combination of prosodic feature vectors, automatic emotion discrimination performance close to human emotion recognition ability was achievable. PMID:16038449

  15. Complex Event Recognition Architecture

    NASA Technical Reports Server (NTRS)

    Fitzgerald, William A.; Firby, R. James

    2009-01-01

    Complex Event Recognition Architecture (CERA) is the name of a computational architecture, and software that implements the architecture, for recognizing complex event patterns that may be spread across multiple streams of input data. One of the main components of CERA is an intuitive event pattern language that simplifies what would otherwise be the complex, difficult tasks of creating logical descriptions of combinations of temporal events and defining rules for combining information from different sources over time. In this language, recognition patterns are defined in simple, declarative statements that combine point events from given input streams with those from other streams, using conjunction, disjunction, and negation. Patterns can be built on one another recursively to describe very rich, temporally extended combinations of events. Thereafter, a run-time matching algorithm in CERA efficiently matches these patterns against input data and signals when patterns are recognized. CERA can be used to monitor complex systems and to signal operators or initiate corrective actions when anomalous conditions are recognized. CERA can be run as a stand-alone monitoring system, or it can be integrated into a larger system to automatically trigger responses to changing environments or problematic situations.

  16. Automatic Welding System

    NASA Technical Reports Server (NTRS)

    1982-01-01

    Robotic welding has been of interest to industrial firms because it offers higher productivity at lower cost than manual welding. There are some systems with automated arc guidance available, but they have disadvantages, such as limitations on types of materials or types of seams that can be welded; susceptibility to stray electrical signals; restricted field of view; or tendency to contaminate the weld seam. Wanting to overcome these disadvantages, Marshall Space Flight Center, aided by Hayes International Corporation, developed system that uses closed-circuit TV signals for automatic guidance of the welding torch. NASA granted license to Combined Technologies, Inc. for commercial application of the technology. They developed a refined and improved arc guidance system. CTI in turn, licensed the Merrick Corporation, also of Nashville, for marketing and manufacturing of the new system, called the CT2 Optical Trucker. CT2 is a non-contracting system that offers adaptability to broader range of welding jobs and provides greater reliability in high speed operation. It is extremely accurate and can travel at high speed of up to 150 inches per minute.

  17. Automatic transmission system

    SciTech Connect

    Ha, J.S.

    1989-04-25

    An automatic transmission system is described for use in vehicles, which comprises: a clutch wheel containing a plurality of concentric rings of decreasing diameter, the clutch wheel being attached to an engine of the vehicle; a plurality of clutch gears corresponding in size to the concentric rings, the clutch gears being adapted to selectively and frictionally engage with the concentric rings of the clutch wheel; an accelerator pedal and a gear selector, the accelerator pedals being connected to one end of a substantially U-shaped frame member, the other end of the substantially U-shaped frame member selectively engaging with one end of one of wires received in a pair of apertures of the gear selector; a plurality of drive gear controllers and a reverse gear controller; means operatively connected with the gear selector and the plurality of drive gear controllers and reverse gear controller for selectively engaging one of the drive and reverse gear controllers depending upon the position of the gear selector; and means for individually connecting the drive and reverse gear controllers with the corresponding clutch gears whereby upon the selection of the gear selector, friction engagement is achieved between the clutch gear and the clutch wheels for rotating the wheel in the forward or reverse direction.

  18. Automatic goals and conscious regulation in social cognitive affective neuroscience.

    PubMed

    Sripada, Chandra; Swain, John D; Ho, S Shaun; Swain, James E

    2014-04-01

    The Selfish Goal model challenges traditional agentic models that place conscious systems at the helm of motivation. We highlight the need for ongoing supervision and intervention of automatic goals by higher-order conscious systems with examples from social cognitive affective neuroscience. We contend that interplay between automatic and supervisory systems is required for adaptive human behavior. PMID:24775144

  19. Automated inspection of micro-defect recognition system for color filter

    NASA Astrophysics Data System (ADS)

    Jeffrey Kuo, Chung-Feng; Peng, Kai-Ching; Wu, Han-Cheng; Wang, Ching-Chin

    2015-07-01

    This study focused on micro-defect recognition and classification in color filters. First, six types of defects were examined, namely grain, black matrix hole (BMH), indium tin oxide (ITO) defect, missing edge and shape (MES), highlights, and particle. Orthogonal projection was applied to locate each pixel in a test image. Then, an image comparison was performed to mark similar blocks on the test image. The block that best resembled the template was chosen as the new template (or matching adaptive template). Afterwards, image subtraction was applied to subtract the pixels at the same location in each block of the test image from the matching adaptive template. The control limit law employed logic operation to separate the defect from the background region. The complete defect structure was obtained by the morphology method. Next, feature values, including defect gray value, red, green, and blue (RGB) color components, and aspect ratio were obtained as the classifier input. The experimental results showed that defect recognition could be completed as fast as 0.154 s using the proposed recognition system and software. In micro-defect classification, back-propagation neural network (BPNN) and minimum distance classifier (MDC) served as the defect classification decision theories for the five acquired feature values. To validate the proposed system, this study used 41 defects as training samples, and treated the feature values of 307 test samples as the BPNN classifier inputs. The total recognition rate was 93.7%. When an MDC was used, the total recognition rate was 96.8%, indicating that the MDC method is feasible in applying automatic optical inspection technology to classify micro-defects of color filters. The proposed system is proven to successfully improve the production yield and lower costs.

  20. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  1. 10 CFR Appendix J to Subpart B of... - Uniform Test Method for Measuring the Energy Consumption of Automatic and Semi-Automatic Clothes...

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... with an adaptive control system. Therefore, pursuant to 10 CFR 430.27, a waiver must be obtained to... adaptive control systems, must submit a petition for waiver pursuant to 10 CFR 430.27 to establish an... of Automatic and Semi-Automatic Clothes Washers J Appendix J to Subpart B of Part 430...

  2. Automatic Command Sequence Generation

    NASA Technical Reports Server (NTRS)

    Fisher, Forest; Gladded, Roy; Khanampompan, Teerapat

    2007-01-01

    Automatic Sequence Generator (Autogen) Version 3.0 software automatically generates command sequences for the Mars Reconnaissance Orbiter (MRO) and several other JPL spacecraft operated by the multi-mission support team. Autogen uses standard JPL sequencing tools like APGEN, ASP, SEQGEN, and the DOM database to automate the generation of uplink command products, Spacecraft Command Message Format (SCMF) files, and the corresponding ground command products, DSN Keywords Files (DKF). Autogen supports all the major multi-mission mission phases including the cruise, aerobraking, mapping/science, and relay mission phases. Autogen is a Perl script, which functions within the mission operations UNIX environment. It consists of two parts: a set of model files and the autogen Perl script. Autogen encodes the behaviors of the system into a model and encodes algorithms for context sensitive customizations of the modeled behaviors. The model includes knowledge of different mission phases and how the resultant command products must differ for these phases. The executable software portion of Autogen, automates the setup and use of APGEN for constructing a spacecraft activity sequence file (SASF). The setup includes file retrieval through the DOM (Distributed Object Manager), an object database used to store project files. This step retrieves all the needed input files for generating the command products. Depending on the mission phase, Autogen also uses the ASP (Automated Sequence Processor) and SEQGEN to generate the command product sent to the spacecraft. Autogen also provides the means for customizing sequences through the use of configuration files. By automating the majority of the sequencing generation process, Autogen eliminates many sequence generation errors commonly introduced by manually constructing spacecraft command sequences. Through the layering of commands into the sequence by a series of scheduling algorithms, users are able to rapidly and reliably construct the

  3. Image feature meaning for automatic key-frame extraction

    NASA Astrophysics Data System (ADS)

    Di Lecce, Vincenzo; Guerriero, Andrea

    2003-12-01

    Video abstraction and summarization, being request in several applications, has address a number of researches to automatic video analysis techniques. The processes for automatic video analysis are based on the recognition of short sequences of contiguous frames that describe the same scene, shots, and key frames representing the salient content of the shot. Since effective shot boundary detection techniques exist in the literature, in this paper we will focus our attention on key frames extraction techniques to identify the low level visual features of the frames that better represent the shot content. To evaluate the features performance, key frame automatically extracted using these features, are compared to human operator video annotations.

  4. Automatism and hypoglycaemia.

    PubMed

    Beaumont, Guy

    2007-02-01

    A case of a detained person (DP) suffering from insulin-dependent diabetes, who subsequently used the disorder in his defence as a reason to claim automatism, is discussed. The legal and medical history of automatism is outlined along with the present day situation. Forensic physicians should be aware when examining any diabetic that automatism may subsequently be claimed. With this in mind, the importance of relevant history taking specifically relating to diabetic control and symptoms is discussed.

  5. An anatomy of automatism.

    PubMed

    Mackay, R D

    2015-07-01

    The automatism defence has been described as a quagmire of law and as presenting an intractable problem. Why is this so? This paper will analyse and explore the current legal position on automatism. In so doing, it will identify the problems which the case law has created, including the distinction between sane and insane automatism and the status of the 'external factor doctrine', and comment briefly on recent reform proposals.

  6. An anatomy of automatism.

    PubMed

    Mackay, R D

    2015-07-01

    The automatism defence has been described as a quagmire of law and as presenting an intractable problem. Why is this so? This paper will analyse and explore the current legal position on automatism. In so doing, it will identify the problems which the case law has created, including the distinction between sane and insane automatism and the status of the 'external factor doctrine', and comment briefly on recent reform proposals. PMID:26378105

  7. Automatic crack propagation tracking

    NASA Technical Reports Server (NTRS)

    Shephard, M. S.; Weidner, T. J.; Yehia, N. A. B.; Burd, G. S.

    1985-01-01

    A finite element based approach to fully automatic crack propagation tracking is presented. The procedure presented combines fully automatic mesh generation with linear fracture mechanics techniques in a geometrically based finite element code capable of automatically tracking cracks in two-dimensional domains. The automatic mesh generator employs the modified-quadtree technique. Crack propagation increment and direction are predicted using a modified maximum dilatational strain energy density criterion employing the numerical results obtained by meshes of quadratic displacement and singular crack tip finite elements. Example problems are included to demonstrate the procedure.

  8. Automatic differentiation bibliography

    SciTech Connect

    Corliss, G.F.

    1992-07-01

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

  9. Low-resolution gait recognition.

    PubMed

    Zhang, Junping; Pu, Jian; Chen, Changyou; Fleischer, Rudolf

    2010-08-01

    Unlike other biometric authentication methods, gait recognition is noninvasive and effective from a distance. However, the performance of gait recognition will suffer in the low-resolution (LR) case. Furthermore, when gait sequences are projected onto a nonoptimal low-dimensional subspace to reduce the data complexity, the performance of gait recognition will also decline. To deal with these two issues, we propose a new algorithm called superresolution with manifold sampling and backprojection (SRMS), which learns the high-resolution (HR) counterparts of LR test images from a collection of HR/LR training gait image patch pairs. Then, we incorporate SRMS into a new algorithm called multilinear tensor-based learning without tuning parameters (MTP) for LR gait recognition. Our contributions include the following: 1) With manifold sampling, the redundancy of gait image patches is remarkably decreased; thus, the superresolution procedure is more efficient and reasonable. 2) Backprojection guarantees that the learned HR gait images and the corresponding LR gait images can be more consistent. 3) The optimal subspace dimension for dimension reduction is automatically determined without introducing extra parameters. 4) Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF human gait database and the CASIA gait database show the increased efficiency of the proposed algorithm, compared with previous algorithms. PMID:20199936

  10. Learning and Domain Adaptation

    NASA Astrophysics Data System (ADS)

    Mansour, Yishay

    Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, including NLP tasks (document classification, sentiment analysis, etc.), speech recognition (speakers and noise or environment adaptation) and face recognition (different lighting conditions, different population composition).

  11. Automatic Differentiation Package

    SciTech Connect

    Gay, David M.; Phipps, Eric; Bratlett, Roscoe

    2007-03-01

    Sacado is an automatic differentiation package for C++ codes using operator overloading and C++ templating. Sacado provide forward, reverse, and Taylor polynomial automatic differentiation classes and utilities for incorporating these classes into C++ codes. Users can compute derivatives of computations arising in engineering and scientific applications, including nonlinear equation solving, time integration, sensitivity analysis, stability analysis, optimization and uncertainity quantification.

  12. Automatic Versus Manual Indexing

    ERIC Educational Resources Information Center

    Vander Meulen, W. A.; Janssen, P. J. F. C.

    1977-01-01

    A comparative evaluation of results in terms of recall and precision from queries submitted to systems with automatic and manual subject indexing. Differences were attributed to query formulation. The effectiveness of automatic indexing was found equivalent to manual indexing. (Author/KP)

  13. Recognition Tunneling

    PubMed Central

    Lindsay, Stuart; He, Jin; Sankey, Otto; Hapala, Prokop; Jelinek, Pavel; Zhang, Peiming; Chang, Shuai; Huang, Shuo

    2010-01-01

    Single molecules in a tunnel junction can now be interrogated reliably using chemically-functionalized electrodes. Monitoring stochastic bonding fluctuations between a ligand bound to one electrode and its target bound to a second electrode (“tethered molecule-pair” configuration) gives insight into the nature of the intermolecular bonding at a single molecule-pair level, and defines the requirements for reproducible tunneling data. Simulations show that there is an instability in the tunnel gap at large currents, and this results in a multiplicity of contacts with a corresponding spread in the measured currents. At small currents (i.e. large gaps) the gap is stable, and functionalizing a pair of electrodes with recognition reagents (the “free analyte” configuration) can generate a distinct tunneling signal when an analyte molecule is trapped in the gap. This opens up a new interface between chemistry and electronics with immediate implications for rapid sequencing of single DNA molecules. PMID:20522930

  14. Automatic Dryers--Components and Operations; Appliance Repair--Intermediate: 9025.01.

    ERIC Educational Resources Information Center

    Dade County Public Schools, Miami, FL.

    Designed to familiarize the student with the components and operations of automatic gas and electric dryers, this course outlines the principles of drying and how they relate to the automatic dryer. Instruction centers upon the functions and operations of dryer components and the recognition and identification of various component malfunctions,…

  15. Prosody's Contribution to Fluency: An Examination of the Theory of Automatic Information Processing

    ERIC Educational Resources Information Center

    Schrauben, Julie E.

    2010-01-01

    LaBerge and Samuels' (1974) theory of automatic information processing in reading offers a model that explains how and where the processing of information occurs and the degree to which processing of information occurs. These processes are dependent upon two criteria: accurate word decoding and automatic word recognition. However, LaBerge and…

  16. Investigating Prompt Difficulty in an Automatically Scored Speaking Performance Assessment

    ERIC Educational Resources Information Center

    Cox, Troy L.

    2013-01-01

    Speaking assessments for second language learners have traditionally been expensive to administer because of the cost of rating the speech samples. To reduce the cost, many researchers are investigating the potential of using automatic speech recognition (ASR) as a means to score examinee responses to open-ended prompts. This study examined the…

  17. On the use of MKL for cooking action recognition

    NASA Astrophysics Data System (ADS)

    Bianco, Simone; Ciocca, Gianluigi; Napoletano, Paolo

    2014-03-01

    Automatic action recognition in videos is a challenging computer vision task that has become an active research area in recent years. Existing strategies usually use kernel-based learning algorithms that considers a simple combination of different features completely disregarding how such features should be integrated to fit the given problem. Since a given feature is most suitable to describe a given image/video property, the adaptive weighting of such features can improve the performance of the learning algorithm. In this paper, we investigated the use of the Multiple Kernel Learning (MKL) algorithm to adaptive search for the best linear relation among the considered features. MKL is an extension of the support vector machines (SVMs) to work with a weighted linear combination of several single kernels. This approach allows to simultaneously estimate the weights for the multiple kernels combination as well as the underlying SVM parameters. In order to prove the validity of the MKL approach, we considered a descriptor composed of multiple features aligned with dense trajectories. We experimented our approach on a database containing 36 cooking actions. Results confirm that the use of MKL improves the classification performance.

  18. Convolution neural networks for ship type recognition

    NASA Astrophysics Data System (ADS)

    Rainey, Katie; Reeder, John D.; Corelli, Alexander G.

    2016-05-01

    Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.

  19. Implementation of perceptual aspects in a face recognition algorithm

    NASA Astrophysics Data System (ADS)

    Crenna, F.; Zappa, E.; Bovio, L.; Testa, R.; Gasparetto, M.; Rossi, G. B.

    2013-09-01

    Automatic face recognition is a biometric technique particularly appreciated in security applications. In fact face recognition presents the opportunity to operate at a low invasive level without the collaboration of the subjects under tests, with face images gathered either from surveillance systems or from specific cameras located in strategic points. The automatic recognition algorithms perform a measurement, on the face images, of a set of specific characteristics of the subject and provide a recognition decision based on the measurement results. Unfortunately several quantities may influence the measurement of the face geometry such as its orientation, the lighting conditions, the expression and so on, affecting the recognition rate. On the other hand human recognition of face is a very robust process far less influenced by the surrounding conditions. For this reason it may be interesting to insert perceptual aspects in an automatic facial-based recognition algorithm to improve its robustness. This paper presents a first study in this direction investigating the correlation between the results of a perception experiment and the facial geometry, estimated by means of the position of a set of repere points.

  20. A new automatic baseline correction method based on iterative method

    NASA Astrophysics Data System (ADS)

    Bao, Qingjia; Feng, Jiwen; Chen, Fang; Mao, Wenping; Liu, Zao; Liu, Kewen; Liu, Chaoyang

    2012-05-01

    A new automatic baseline correction method for Nuclear Magnetic Resonance (NMR) spectra is presented. It is based on an improved baseline recognition method and a new iterative baseline modeling method. The presented baseline recognition method takes advantages of three baseline recognition algorithms in order to recognize all signals in spectra. While in the iterative baseline modeling method, besides the well-recognized baseline points in signal-free regions, the 'quasi-baseline points' in the signal-crowded regions are also identified and then utilized to improve robustness by preventing the negative regions. The experimental results on both simulated data and real metabolomics spectra with over-crowded peaks show the efficiency of this automatic method.

  1. Automatic and Flexible

    PubMed Central

    Hassin, Ran R.; Bargh, John A.; Zimerman, Shira

    2008-01-01

    Arguing from the nature of goal pursuit and from the economy of mental resources this paper suggests that automatic goal pursuit, much like its controlled counterpart, may be flexible. Two studies that employ goal priming procedures examine this hypothesis using the Wisconsin Card Sorting Test (Study 1) and a variation of the Iowa Gambling Task (Study 2). Implications of the results for our understanding of the dichotomy between automatic and controlled processes in general, and for our conception of automatic goal pursuit in particular, are discussed. PMID:19325712

  2. Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

    PubMed Central

    Kolluru, BalaKrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

    2011-01-01

    Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR. PMID:21633495

  3. Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.

    PubMed

    Liu, Li; Shao, Ling; Li, Xuelong; Lu, Ke

    2016-01-01

    Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.

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

    PubMed

    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.

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

    PubMed

    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. PMID:24693243

  6. Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

    PubMed Central

    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. PMID:24693243

  7. Automatic amino acid analyzer

    NASA Technical Reports Server (NTRS)

    Berdahl, B. J.; Carle, G. C.; Oyama, V. I.

    1971-01-01

    Analyzer operates unattended or up to 15 hours. It has an automatic sample injection system and can be programmed. All fluid-flow valve switching is accomplished pneumatically from miniature three-way solenoid pilot valves.

  8. AUTOMATIC MASS SPECTROMETER

    DOEpatents

    Hanson, M.L.; Tabor, C.D. Jr.

    1961-12-01

    A mass spectrometer for analyzing the components of a gas is designed which is capable of continuous automatic operation such as analysis of samples of process gas from a continuous production system where the gas content may be changing. (AEC)

  9. Automatic Payroll Deposit System.

    ERIC Educational Resources Information Center

    Davidson, D. B.

    1979-01-01

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

  10. Securing iris recognition systems against masquerade attacks

    NASA Astrophysics Data System (ADS)

    Galbally, Javier; Gomez-Barrero, Marta; Ross, Arun; Fierrez, Julian; Ortega-Garcia, Javier

    2013-05-01

    A novel two-stage protection scheme for automatic iris recognition systems against masquerade attacks carried out with synthetically reconstructed iris images is presented. The method uses different characteristics of real iris images to differentiate them from the synthetic ones, thereby addressing important security flaws detected in state-of-the-art commercial systems. Experiments are carried out on the publicly available Biosecure Database and demonstrate the efficacy of the proposed security enhancing approach.

  11. Automatic switching matrix

    DOEpatents

    Schlecht, Martin F.; Kassakian, John G.; Caloggero, Anthony J.; Rhodes, Bruce; Otten, David; Rasmussen, Neil

    1982-01-01

    An automatic switching matrix that includes an apertured matrix board containing a matrix of wires that can be interconnected at each aperture. Each aperture has associated therewith a conductive pin which, when fully inserted into the associated aperture, effects electrical connection between the wires within that particular aperture. Means is provided for automatically inserting the pins in a determined pattern and for removing all the pins to permit other interconnecting patterns.

  12. Automatic recognition and scoring of olympic rhythmic gymnastic movements.

    PubMed

    Díaz-Pereira, M Pino; Gómez-Conde, Iván; Escalona, Merly; Olivieri, David N

    2014-04-01

    We describe a conceptually simple algorithm for assigning judgement scores to rhythmic gymnastic movements, which could improve scoring objectivity and reduce judgemental bias during competitions. Our method, implemented as a real-time computer vision software, takes a video shot or a live performance video stream as input and extracts detailed velocity field information from body movements, transforming them into specialized spatio-temporal image templates. The collection of such images over time, when projected into a velocity covariance eigenspace, trace out unique but similar trajectories for a particular gymnastic movement type. By comparing separate executions of the same atomic gymnastic routine, our method assigns a quality judgement score that is related to the distance between the respective spatio-temporal trajectories. For several standard gymnastic movements, the method accurately assigns scores that are comparable to those assigned by expert judges. We also describe our rhythmic gymnastic video shot database, which we have made freely available to the human movement research community. The database can be obtained at http://www.milegroup.net/apps/gymdb/. PMID:24502991

  13. Automatic Recognition of Breathing Route During Sleep Using Snoring Sounds

    NASA Astrophysics Data System (ADS)

    Mikami, Tsuyoshi; Kojima, Yohichiro

    This letter classifies snoring sounds into three breathing routes (oral, nasal, and oronasal) with discriminant analysis of the power spectra and k-nearest neighbor method. It is necessary to recognize breathing route during snoring, because oral snoring is a typical symptom of sleep apnea but we cannot know our own breathing and snoring condition during sleep. As a result, about 98.8% classification rate is obtained by using leave-one-out test for performance evaluation.

  14. Automatic solar panel recognition and defect detection using infrared imaging

    NASA Astrophysics Data System (ADS)

    Gao, Xiang; Munson, Eric; Abousleman, Glen P.; Si, Jennie

    2015-05-01

    Failure-free operation of solar panels is of fundamental importance for modern commercial solar power plants. To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation method is required. By using thermal infrared imaging, anomalies can be detected without having to incorporate expensive electrical detection circuitry. In this paper, we propose a solar panel defect detection system, which automates the inspection process and mitigates the need for manual panel inspection in a large solar farm. Infrared video sequences of each array of solar panels are first collected by an infrared camera mounted to a moving cart, which is driven from array to array in a solar farm. The image processing algorithm segments the solar panels from the background in real time, with only the height of the array (specified as the number of rows of panels in the array) being given as prior information to aid in the segmentation process. In order to "count" the number the panels within any given array, frame-to frame panel association is established using optical flow. Local anomalies in a single panel such as hotspots and cracks will be immediately detected and labeled as soon as the panel is recognized in the field of view. After the data from an entire array is collected, hot panels are detected using DBSCAN clustering. On real-world test data containing over 12,000 solar panels, over 98% of all panels are recognized and correctly counted, with 92% of all types of defects being identified by the system.

  15. Sensitivity based segmentation and identification in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Absher, R.

    1984-03-01

    This research program continued an investigation of sensitivity analysis, and its use in the segmentation and identification of the phonetic units of speech, that was initiated during the 1982 Summer Faculty Research Program. The elements of the sensitivity matrix, which express the relative change in each pole of the speech model to a relative change in each coefficient of the characteristic equation, were evaluated for an expanded set of data which consisted of six vowels contained in single words spoken in a simple carrier phrase by five males with differing dialects. The objectives were to evaluate the sensitivity matrix, interpret its changes during the production of the vowels, and to evaluate inter-speaker variations. It was determined that the sensitivity analysis (1) serves to segment the vowel interval, (2) provides a measure of when a vowel is on target, and (3) should provide sufficient information to identify each particular vowel. Based on the results presented, sensitivity analysis should result in more accurate segmentation and identification of phonemes and should provide a practicable framework for incorporation of acoustic-phonetic variance as well as time and talker normalization.

  16. Capillary-driven automatic packaging.

    PubMed

    Ding, Yuzhe; Hong, Lingfei; Nie, Baoqing; Lam, Kit S; Pan, Tingrui

    2011-04-21

    Packaging continues to be one of the most challenging steps in micro-nanofabrication, as many emerging techniques (e.g., soft lithography) are incompatible with the standard high-precision alignment and bonding equipment. In this paper, we present a simple-to-operate, easy-to-adapt packaging strategy, referred to as Capillary-driven Automatic Packaging (CAP), to achieve automatic packaging process, including the desired features of spontaneous alignment and bonding, wide applicability to various materials, potential scalability, and direct incorporation in the layout. Specifically, self-alignment and self-engagement of the CAP process induced by the interfacial capillary interactions between a liquid capillary bridge and the top and bottom substrates have been experimentally characterized and theoretically analyzed with scalable implications. High-precision alignment (of less than 10 µm) and outstanding bonding performance (up to 300 kPa) has been reliably obtained. In addition, a 3D microfluidic network, aligned and bonded by the CAP technique, has been devised to demonstrate the applicability of this facile yet robust packaging technique for emerging microfluidic and bioengineering applications.

  17. Interactive training for handwriting recognition in historical document collections

    NASA Astrophysics Data System (ADS)

    Kennard, Douglas J.; Barrett, William A.

    2007-01-01

    We present a method of interactive training for handwriting recognition in collections of documents. As the user transcribes (labels) the words in the training set, words are automatically skipped if they appear to match words that are already transcribed. By reducing the amount of redundant training, better coverage of the data is achieved, resulting in more accurate recognition. Using word-level features for training and recognition in a collection of George Washington's manuscripts, the recognition ratio is approximately 2%-8% higher after training with our interactive method than after training the same number of words sequentially. Using our approach, less training is required to achieve an equivalent recognition ratio. A slight improvement in recognition ratio is also observed when using our method on a second data set, which consists of several pages from a diary written by Jennie Leavitt Smith.

  18. A road sign detection and recognition system for mobile devices

    NASA Astrophysics Data System (ADS)

    Xiong, Bo; Izmirli, Ozgur

    2012-01-01

    We present an automatic road sign detection and recognition service system for mobile devices. The system is based on a client-server architecture which allows mobile users to take pictures of road signs and request detection and recognition service from a centralized server for processing. The preprocessing, detection and recognition take place at the server end and consequently, the result is sent back to the mobile device. For road sign detection, we use particular color features calculated from the input image. Recognition is implemented using a neural network based on normalized color histogram features. We report on the effects of various parameters on recognition accuracy. Our results demonstrate that the system can provide an efficient framework for locale-dependent road sign recognition with multilingual support.

  19. Automatic Parametric Testing Of Integrated Circuits

    NASA Technical Reports Server (NTRS)

    Jennings, Glenn A.; Pina, Cesar A.

    1989-01-01

    Computer program for parametric testing saves time and effort in research and development of integrated circuits. Software system automatically assembles various types of test structures and lays them out on silicon chip, generates sequency of test instructions, and interprets test data. Employs self-programming software; needs minimum of human intervention. Adapted to needs of different laboratories and readily accommodates new test structures. Program codes designed to be adaptable to most computers and test equipment now in use. Written in high-level languages to enhance transportability.

  20. Phonematic recognition by linear prediction: Experiment

    NASA Astrophysics Data System (ADS)

    Miclet, L.; Grenier, Y.; Leroux, J.

    The recognition of speech signals analyzed by linear prediction is introduced. The principle of the channel adapted vocoder (CAV) is outlined. The learning of each channel model and adaptation to the speaker are discussed. A method stemming from the canonical analysis of correlations is given. This allows, starting with the CAV of one speaker, the calculation of that of another. The projection function is learned from a series of key words pronounced by both speakers. The reconstruction of phonemes can be explained by recognition factors arising from the vocoder. Automata associated with the channels are used for local smoothing and series of segments are treated in order to produce a phonemic lattice.

  1. [Study on the automatic parameters identification of water pipe network model].

    PubMed

    Jia, Hai-Feng; Zhao, Qi-Feng

    2010-01-01

    Based on the problems analysis on development and application of water pipe network model, the model parameters automatic identification is regarded as a kernel bottleneck of model's application in water supply enterprise. The methodology of water pipe network model parameters automatic identification based on GIS and SCADA database is proposed. Then the kernel algorithm of model parameters automatic identification is studied, RSA (Regionalized Sensitivity Analysis) is used for automatic recognition of sensitive parameters, and MCS (Monte-Carlo Sampling) is used for automatic identification of parameters, the detail technical route based on RSA and MCS is presented. The module of water pipe network model parameters automatic identification is developed. At last, selected a typical water pipe network as a case, the case study on water pipe network model parameters automatic identification is conducted and the satisfied results are achieved. PMID:20329520

  2. Computer-assisted counting of retinal cells by automatic segmentation after TV denoising

    PubMed Central

    2013-01-01

    Background Quantitative evaluation of mosaics of photoreceptors and neurons is essential in studies on development, aging and degeneration of the retina. Manual counting of samples is a time consuming procedure while attempts to automatization are subject to various restrictions from biological and preparation variability leading to both over- and underestimation of cell numbers. Here we present an adaptive algorithm to overcome many of these problems. Digital micrographs were obtained from cone photoreceptor mosaics visualized by anti-opsin immuno-cytochemistry in retinal wholemounts from a variety of mammalian species including primates. Segmentation of photoreceptors (from background, debris, blood vessels, other cell types) was performed by a procedure based on Rudin-Osher-Fatemi total variation (TV) denoising. Once 3 parameters are manually adjusted based on a sample, similarly structured images can be batch processed. The module is implemented in MATLAB and fully documented online. Results The object recognition procedure was tested on samples with a typical range of signal and background variations. We obtained results with error ratios of less than 10% in 16 of 18 samples and a mean error of less than 6% compared to manual counts. Conclusions The presented method provides a traceable module for automated acquisition of retinal cell density data. Remaining errors, including addition of background items, splitting or merging of objects might be further reduced by introduction of additional parameters. The module may be integrated into extended environments with features such as 3D-acquisition and recognition. PMID:24138794

  3. License Plate Recognition System for Indian Vehicles

    NASA Astrophysics Data System (ADS)

    Sanap, P. R.; Narote, S. P.

    2010-11-01

    We consider the task of recognition of Indian vehicle number plates (also called license plates or registration plates in other countries). A system for Indian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. Also, vehicle owners may place the plates inside glass covered frames or use plates made of nonstandard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Indian vehicle number plates in digital images. Commercial application of the system is envisaged.

  4. Automatic repair in active-matrix liquid crystal display (AMLCD)

    NASA Astrophysics Data System (ADS)

    Qiu, Hongjie; Sheng, King C.; Lam, Joseph K.; Knuth, Tim; Miller, Mike; Addiego, Ginetto

    1994-04-01

    This paper presents an automatic AMLCD repair system utilizing real-time video, image processing and analysis, pattern recognition, and artificial intelligence. The system fundamentally includes automatic optical focus, automatic alignment, defect detection, defect analysis and identification, repair point and path definition, and automatic metal removal and addition (cutting, ablating, and metal deposition). Automatic alignment includes mark alignment as well AMLCD pixel alignment. The features (area, centroid, slope, perimeter, length, width and relative location between objects of interest) are measured for defect analysis. A least cost criterion is employed for defect detection and classification. The choice of repair process is determined by two defect types, either `Open' or `Short'. The repair point and path definition is made from the material structure type such as Data line, Gate line, and ITO area, defect position, and repair rules. The rules are generated from the global and local knowledge. In the automatic repair process, the system automatically performs optical focus, mark and pixel alignment, defect detection and classification, and laser writing or cutting.

  5. Evaluation of an automatic markup system

    NASA Astrophysics Data System (ADS)

    Taghva, Kazem; Condit, Allen; Borsack, Julie

    1995-03-01

    One predominant application of OCR is the recognition of full text documents for information retrieval. Modern retrieval systems exploit both the textual content of the document as well as its structure. The relationship between textual content and character accuracy have been the focus of recent studies. It has been shown that due to the redundancies in text, average precision and recall is not heavily affected by OCR character errors. What is not fully known is to what extent OCR devices can provide reliable information that can be used to capture the structure of the document. In this paper, we present a preliminary report on the design and evaluation of a system to automatically markup technical documents, based on information provided by an OCR device. The device we use differs from traditional OCR devices in that it not only performs optical character recognition, but also provides detailed information about page layout, word geometry, and font usage. Our automatic markup program, which we call Autotag, uses this information, combined with dictionary lookup and content analysis, to identify structural components of the text. These include the document title, author information, abstract, sections, section titles, paragraphs, sentences, and de-hyphenated words. A visual examination of the hardcopy is compared to the output of our markup system to determine its correctness.

  6. An evaluation of an automatic markup system

    SciTech Connect

    Taghva, K.; Condit, A.; Borsack, J.

    1995-04-01

    One predominant application of OCR is the recognition of full text documents for information retrieval. Modern retrieval systems exploit both the textual content of the document as well as its structure. The relationship between textual content and character accuracy have been the focus of recent studies. It has been shown that due to the redundancies in text, average precision and recall is not heavily affected by OCR character errors. What is not fully known is to what extent OCR devices can provide reliable information that can be used to capture the structure of the document. In this paper, the authors present a preliminary report on the design and evaluation of a system to automatically markup technical documents, based on information provided by an OCR device. The device the authors use differs from traditional OCR devices in that it not only performs optical character recognition, but also provides detailed information about page layout, word geometry, and font usage. Their automatic markup program, which they call Autotag, uses this information, combined with dictionary, lookup and content analysis, to identify structural components of the text. These include the document title, author information, abstract, sections, section titles, paragraphs, sentences, and de-hyphenated words. A visual examination of the hardcopy will be compared to the output of their markup system to determine its correctness.

  7. Automatic analysis of a skull fracture based on image content

    NASA Astrophysics Data System (ADS)

    Shao, Hong; Zhao, Hong

    2003-09-01

    Automatic analysis based on image content is a hotspot with bright future of medical image diagnosis technology research. Analysis of the fracture of skull can help doctors diagnose. In this paper, a new approach is proposed to automatically detect the fracture of skull based on CT image content. First region growing method, whose seeds and growing rules are chosen by k-means clustering dynamically, is applied for image automatic segmentation. The segmented region boundary is found by boundary tracing. Then the shape of the boundary is analyzed, and the circularity measure is taken as description parameter. At last the rules for computer automatic diagnosis of the fracture of the skull are reasoned by entropy function. This method is used to analyze the images from the third ventricles below layer to cerebral cortex top layer. Experimental result shows that the recognition rate is 100% for the 100 images, which are chosen from medical image database randomly and are not included in the training examples. This method integrates color and shape feature, and isn't affected by image size and position. This research achieves high recognition rate and sets a basis for automatic analysis of brain image.

  8. A new automatic synchronizer

    SciTech Connect

    Malm, C.F.

    1995-12-31

    A phase lock loop automatic synchronizer, PLLS, matches generator speed starting from dead stop to bus frequency, and then locks the phase difference at zero, thereby maintaining zero slip frequency while the generator breaker is being closed to the bus. The significant difference between the PLLS and a conventional automatic synchronizer is that there is no slip frequency difference between generator and bus. The PLL synchronizer is most advantageous when the penstock pressure fluctuates the grid frequency fluctuates, or both. The PLL synchronizer is relatively inexpensive. Hydroplants with multiple units can economically be equipped with a synchronizer for each unit.

  9. WOLF; automatic typing program

    USGS Publications Warehouse

    Evenden, G.I.

    1982-01-01

    A FORTRAN IV program for the Hewlett-Packard 1000 series computer provides for automatic typing operations and can, when employed with manufacturer's text editor, provide a system to greatly facilitate preparation of reports, letters and other text. The input text and imbedded control data can perform nearly all of the functions of a typist. A few of the features available are centering, titles, footnotes, indentation, page numbering (including Roman numerals), automatic paragraphing, and two forms of tab operations. This documentation contains both user and technical description of the program.

  10. AUTOMATIC COUNTING APPARATUS

    DOEpatents

    Howell, W.D.

    1957-08-20

    An apparatus for automatically recording the results of counting operations on trains of electrical pulses is described. The disadvantages of prior devices utilizing the two common methods of obtaining the count rate are overcome by this apparatus; in the case of time controlled operation, the disclosed system automatically records amy information stored by the scaler but not transferred to the printer at the end of the predetermined time controlled operations and, in the case of count controlled operation, provision is made to prevent a weak sample from occupying the apparatus for an excessively long period of time.

  11. Automatic target extraction in complicated background for camera calibration

    NASA Astrophysics Data System (ADS)

    Guo, Xichao; Wang, Cheng; Wen, Chenglu; Cheng, Ming

    2016-03-01

    In order to perform high precise calibration of camera in complex background, a novel design of planar composite target and the corresponding automatic extraction algorithm are presented. Unlike other commonly used target designs, the proposed target contains the information of feature point coordinate and feature point serial number simultaneously. Then based on the original target, templates are prepared by three geometric transformations and used as the input of template matching based on shape context. Finally, parity check and region growing methods are used to extract the target as final result. The experimental results show that the proposed method for automatic extraction and recognition of the proposed target is effective, accurate and reliable.

  12. Speech-in-Speech Recognition: A Training Study

    ERIC Educational Resources Information Center

    Van Engen, Kristin J.

    2012-01-01

    This study aims to identify aspects of speech-in-noise recognition that are susceptible to training, focusing on whether listeners can learn to adapt to target talkers ("tune in") and learn to better cope with various maskers ("tune out") after short-term training. Listeners received training on English sentence recognition in speech-shaped noise…

  13. Adaptive feature extraction expert

    SciTech Connect

    Yuschik, M.

    1983-01-01

    The identification of discriminatory features places an upper bound on the recognition rate of any automatic speech recognition (ASR) system. One way to structure the extraction of features is to construct an expert system which applies a set of rules to identify particular properties of the speech patterns. However, these patterns vary for an individual speaker and from speaker to speaker so that another expert is actually needed to learn the new variations. The author investigates the problem by using sets of discriminatory features that are suggested by a feature generation expert, improves the selectivity of these features with a training expert, and finally develops a minimally spanning feature set with a statistical selection expert. 12 references.

  14. Locus of Orthographic Effects in Spoken Word Recognition: Novel Insights from the Neighbour Generation Task

    ERIC Educational Resources Information Center

    Muneaux, Mathilde; Ziegler, Johannes C.

    2004-01-01

    Previous research has found that orthographic information can influence auditory word recognition. However, there is still debate about the locus of this effect (lexical versus nonlexical, strategic versus automatic). In the present study, we explored whether orthographic effects in auditory word recognition could be structural-residual effects…

  15. Automaticity of Conceptual Magnitude

    PubMed Central

    Gliksman, Yarden; Itamar, Shai; Leibovich, Tali; Melman, Yonatan; Henik, Avishai

    2016-01-01

    What is bigger, an elephant or a mouse? This question can be answered without seeing the two animals, since these objects elicit conceptual magnitude. How is an object’s conceptual magnitude processed? It was suggested that conceptual magnitude is automatically processed; namely, irrelevant conceptual magnitude can affect performance when comparing physical magnitudes. The current study further examined this question and aimed to expand the understanding of automaticity of conceptual magnitude. Two different objects were presented and participants were asked to decide which object was larger on the screen (physical magnitude) or in the real world (conceptual magnitude), in separate blocks. By creating congruent (the conceptually larger object was physically larger) and incongruent (the conceptually larger object was physically smaller) pairs of stimuli it was possible to examine the automatic processing of each magnitude. A significant congruity effect was found for both magnitudes. Furthermore, quartile analysis revealed that the congruity was affected similarly by processing time for both magnitudes. These results suggest that the processing of conceptual and physical magnitudes is automatic to the same extent. The results support recent theories suggested that different types of magnitude processing and representation share the same core system. PMID:26879153

  16. Automatic sweep circuit

    DOEpatents

    Keefe, Donald J.

    1980-01-01

    An automatically sweeping circuit for searching for an evoked response in an output signal in time with respect to a trigger input. Digital counters are used to activate a detector at precise intervals, and monitoring is repeated for statistical accuracy. If the response is not found then a different time window is examined until the signal is found.

  17. Automatic Program Synthesis Reports.

    ERIC Educational Resources Information Center

    Biermann, A. W.; And Others

    Some of the major results of future goals of an automatic program synthesis project are described in the two papers that comprise this document. The first paper gives a detailed algorithm for synthesizing a computer program from a trace of its behavior. Since the algorithm involves a search, the length of time required to do the synthesis of…

  18. Brut: Automatic bubble classifier

    NASA Astrophysics Data System (ADS)

    Beaumont, Christopher; Goodman, Alyssa; Williams, Jonathan; Kendrew, Sarah; Simpson, Robert

    2014-07-01

    Brut, written in Python, identifies bubbles in infrared images of the Galactic midplane; it uses a database of known bubbles from the Milky Way Project and Spitzer images to build an automatic bubble classifier. The classifier is based on the Random Forest algorithm, and uses the WiseRF implementation of this algorithm.

  19. Automatic multiple applicator electrophoresis

    NASA Technical Reports Server (NTRS)

    Grunbaum, B. W.

    1977-01-01

    Easy-to-use, economical device permits electrophoresis on all known supporting media. System includes automatic multiple-sample applicator, sample holder, and electrophoresis apparatus. System has potential applicability to fields of taxonomy, immunology, and genetics. Apparatus is also used for electrofocusing.

  20. Automatic finite element generators

    NASA Technical Reports Server (NTRS)

    Wang, P. S.

    1984-01-01

    The design and implementation of a software system for generating finite elements and related computations are described. Exact symbolic computational techniques are employed to derive strain-displacement matrices and element stiffness matrices. Methods for dealing with the excessive growth of symbolic expressions are discussed. Automatic FORTRAN code generation is described with emphasis on improving the efficiency of the resultant code.

  1. Reactor component automatic grapple

    SciTech Connect

    Greenaway, P.R.

    1982-12-07

    A grapple for handling nuclear reactor components in a medium such as liquid sodium which, upon proper seating and alignment of the grapple with the component as sensed by a mechanical logic integral to the grapple, automatically seizes the component. The mechanical logic system also precludes seizure in the absence of proper seating and alignment.

  2. Reactor component automatic grapple

    DOEpatents

    Greenaway, Paul R.

    1982-01-01

    A grapple for handling nuclear reactor components in a medium such as liquid sodium which, upon proper seating and alignment of the grapple with the component as sensed by a mechanical logic integral to the grapple, automatically seizes the component. The mechanical logic system also precludes seizure in the absence of proper seating and alignment.

  3. Automatic Data Processing Glossary.

    ERIC Educational Resources Information Center

    Bureau of the Budget, Washington, DC.

    The technology of the automatic information processing field has progressed dramatically in the past few years and has created a problem in common term usage. As a solution, "Datamation" Magazine offers this glossary which was compiled by the U.S. Bureau of the Budget as an official reference. The terms appear in a single alphabetic sequence,…

  4. AUTOmatic Message PACKing Facility

    2004-07-01

    AUTOPACK is a library that provides several useful features for programs using the Message Passing Interface (MPI). Features included are: 1. automatic message packing facility 2. management of send and receive requests. 3. management of message buffer memory. 4. determination of the number of anticipated messages from a set of arbitrary sends, and 5. deterministic message delivery for testing purposes.

  5. Intelligent adaptive structures

    NASA Technical Reports Server (NTRS)

    Wada, Ben K.

    1990-01-01

    'Intelligent Adaptive Structures' (IAS) refers to structural systems whose geometric and intrinsic structural characteristics can be automatically changed to meet mission requirements with changing operational scenarios. An IAS is composed of actuators, sensors, and a control logic; these are integrated in a distributed fashion within the elements of the structure. The IAS concepts thus far developed for space antennas and other precision structures should be applicable to civil, marine, automotive, and aeronautical structural systems.

  6. Reconfigurable environmentally adaptive computing

    NASA Technical Reports Server (NTRS)

    Coxe, Robin L. (Inventor); Galica, Gary E. (Inventor)

    2008-01-01

    Described are methods and apparatus, including computer program products, for reconfigurable environmentally adaptive computing technology. An environmental signal representative of an external environmental condition is received. A processing configuration is automatically selected, based on the environmental signal, from a plurality of processing configurations. A reconfigurable processing element is reconfigured to operate according to the selected processing configuration. In some examples, the environmental condition is detected and the environmental signal is generated based on the detected condition.

  7. A Statistical Approach to Automatic Speech Summarization

    NASA Astrophysics Data System (ADS)

    Hori, Chiori; Furui, Sadaoki; Malkin, Rob; Yu, Hua; Waibel, Alex

    2003-12-01

    This paper proposes a statistical approach to automatic speech summarization. In our method, a set of words maximizing a summarization score indicating the appropriateness of summarization is extracted from automatically transcribed speech and then concatenated to create a summary. The extraction process is performed using a dynamic programming (DP) technique based on a target compression ratio. In this paper, we demonstrate how an English news broadcast transcribed by a speech recognizer is automatically summarized. We adapted our method, which was originally proposed for Japanese, to English by modifying the model for estimating word concatenation probabilities based on a dependency structure in the original speech given by a stochastic dependency context free grammar (SDCFG). We also propose a method of summarizing multiple utterances using a two-level DP technique. The automatically summarized sentences are evaluated by summarization accuracy based on a comparison with a manual summary of speech that has been correctly transcribed by human subjects. Our experimental results indicate that the method we propose can effectively extract relatively important information and remove redundant and irrelevant information from English news broadcasts.

  8. MARZ: Manual and automatic redshifting software

    NASA Astrophysics Data System (ADS)

    Hinton, S. R.; Davis, Tamara M.; Lidman, C.; Glazebrook, K.; Lewis, G. F.

    2016-04-01

    The Australian Dark Energy Survey (OzDES) is a 100-night spectroscopic survey underway on the Anglo-Australian Telescope using the fibre-fed 2-degree-field (2dF) spectrograph. We have developed a new redshifting application MARZ with greater usability, flexibility, and the capacity to analyse a wider range of object types than the RUNZ software package previously used for redshifting spectra from 2dF. MARZ is an open-source, client-based, Javascript web-application which provides an intuitive interface and powerful automatic matching capabilities on spectra generated from the AAOmega spectrograph to produce high quality spectroscopic redshift measurements. The software can be run interactively or via the command line, and is easily adaptable to other instruments and pipelines if conforming to the current FITS file standard is not possible. Behind the scenes, a modified version of the AUTOZ cross-correlation algorithm is used to match input spectra against a variety of stellar and galaxy templates, and automatic matching performance for OzDES spectra has increased from 54% (RUNZ) to 91% (MARZ). Spectra not matched correctly by the automatic algorithm can be easily redshifted manually by cycling automatic results, manual template comparison, or marking spectral features.

  9. Building Group Recognition.

    ERIC Educational Resources Information Center

    Chartier, George

    1994-01-01

    Discusses the value of name recognition for theater companies. Describes steps toward identity and recognition, analyzing the group, the mission statement, symbolic logic, designing and identity, developing a communications plan, and meaningful activities. (SR)

  10. The Recognition Heuristic: A Review of Theory and Tests

    PubMed Central

    Pachur, Thorsten; Todd, Peter M.; Gigerenzer, Gerd; Schooler, Lael J.; Goldstein, Daniel G.

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference). PMID:21779266

  11. Recognition of plant parts with problem-specific algorithms

    NASA Astrophysics Data System (ADS)

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

    1994-06-01

    Automatic micropropagation is necessary to produce cost-effective high amounts of biomass. Juvenile plants are dissected in clean- room environment on particular points on the stem or the leaves. A vision-system detects possible cutting points and controls a specialized robot. This contribution is directed to the pattern- recognition algorithms to detect structural parts of the plant.

  12. Multi-modal vertebrae recognition using Transformed Deep Convolution Network.

    PubMed

    Cai, Yunliang; Landis, Mark; Laidley, David T; Kornecki, Anat; Lum, Andrea; Li, Shuo

    2016-07-01

    Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spinal clinical diagnoses where large amount of imaging data from various of modalities are frequently and interchangeably used. However, the recognition is challenging due to the variations of MR/CT appearances or shape/pose of the vertebrae. In this paper, we propose a method for multi-modal vertebra recognition using a novel deep learning architecture called Transformed Deep Convolution Network (TDCN). This new architecture can unsupervisely fuse image features from different modalities and automatically rectify the pose of vertebra. The fusion of MR and CT image features improves the discriminativity of feature representation and enhances the invariance of the vertebra pattern, which allows us to automatically process images from different contrast, resolution, protocols, even with different sizes and orientations. The feature fusion and pose rectification are naturally incorporated in a multi-layer deep learning network. Experiment results show that our method outperforms existing detection methods and provides a fully automatic location+naming+pose recognition for routine clinical practice. PMID:27104497

  13. Studying the Development of Word Recognition Using a Pseudoword Task

    ERIC Educational Resources Information Center

    Sipala, Christine E.

    2009-01-01

    Fluent reading hinges on automatic word recognition, yet little research has investigated the acquisition process with repeated exposure to novel words. In this study, elementary students in grades three to six were asked to read two-syllable pseudowords five times each (in varied sequences) during two sessions (n = 49). The goal was to study…

  14. FaceID: A face detection and recognition system

    SciTech Connect

    Shah, M.B.; Rao, N.S.V.; Olman, V.; Uberbacher, E.C.; Mann, R.C.

    1996-12-31

    A face detection system that automatically locates faces in gray-level images is described. Also described is a system which matches a given face image with faces in a database. Face detection in an Image is performed by template matching using templates derived from a selected set of normalized faces. Instead of using original gray level images, vertical gradient images were calculated and used to make the system more robust against variations in lighting conditions and skin color. Faces of different sizes are detected by processing the image at several scales. Further, a coarse-to-fine strategy is used to speed up the processing, and a combination of whole face and face component templates are used to ensure low false detection rates. The input to the face recognition system is a normalized vertical gradient image of a face, which is compared against a database using a set of pretrained feedforward neural networks with a winner-take-all fuser. The training is performed by using an adaptation of the backpropagation algorithm. This system has been developed and tested using images from the FERET database and a set of images obtained from Rowley, et al and Sung and Poggio.

  15. Profiles of Discourse Recognition

    ERIC Educational Resources Information Center

    Singer, Murray

    2013-01-01

    A discourse recognition theory derived from more general memory formulations would be broad in its psychological implications. This study compared discourse recognition with some established profiles of item recognition. Participants read 10 stories either once or twice each. They then rated their confidence in recognizing explicit, paraphrased,…

  16. Fully automatic telemetry data processor

    NASA Technical Reports Server (NTRS)

    Cox, F. B.; Keipert, F. A.; Lee, R. C.

    1968-01-01

    Satellite Telemetry Automatic Reduction System /STARS 2/, a fully automatic computer-controlled telemetry data processor, maximizes data recovery, reduces turnaround time, increases flexibility, and improves operational efficiency. The system incorporates a CDC 3200 computer as its central element.

  17. [Research Progress of Automatic Sleep Staging Based on Electroencephalogram Signals].

    PubMed

    Gao, Qunxia; Zhou, Jing; Wu, Xiaoming

    2015-10-01

    The research of sleep staging is not only a basis of diagnosing sleep related diseases but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hot spot and got some achievements. The basic knowledge of sleep staging and electroencephalogram (EEG) is introduced in this paper. Then, feature extraction and pattern recognition, two key technologies for automatic sleep staging, are discussed in detail. Wavelet transform and Hilbert-Huang transform, two methods for feature extraction, are compared. Artificial neural network and support vector machine (SVM), two methods for pattern recognition are discussed. In the end, the research status of this field is summarized, and development trends of next phase are pointed out. PMID:26964329

  18. Speech recognition: Acoustic, phonetic and lexical

    NASA Astrophysics Data System (ADS)

    Zue, V. W.

    1985-10-01

    Our long-term research goal is the development and implementation of speaker-independent continuous speech recognition systems. It is our conviction that proper utilization of speech-specific knowledge is essential for advanced speech recognition systems. With this in mind, we have continued to make progress on the acquisition of acoustic-phonetic and lexical knowledge. We have completed the development of a continuous digit recognition system. The system was constructed to investigate the utilization of acoustic phonetic knowledge in a speech recognition system. Some of the significant development of this study includes a soft-failure procedure for lexical access, and the discovery of a set of acoustic-phonetic features for verification. We have completed a study of the constraints provided by lexical stress on word recognition. We found that lexical stress information alone can, on the average, reduce the number of word candidates from a large dictionary by more than 80%. In conjunction with this study, we successfully developed a system that automatically determines the stress pattern of a word from the acoustic signal.

  19. Robust face recognition via sparse representation.

    PubMed

    Wright, John; Yang, Allen Y; Ganesh, Arvind; Sastry, S Shankar; Ma, Yi

    2009-02-01

    We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by l{1}-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as Eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

  20. Face recognition using ensemble string matching.

    PubMed

    Chen, Weiping; Gao, Yongsheng

    2013-12-01

    In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an ensemble of strings. A novel ensemble string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces.