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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. 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.

  3. Automatic Speech Recognition

    NASA Astrophysics Data System (ADS)

    Potamianos, Gerasimos; Lamel, Lori; Wölfel, Matthias; Huang, Jing; Marcheret, Etienne; Barras, Claude; Zhu, Xuan; McDonough, John; Hernando, Javier; Macho, Dusan; Nadeu, Climent

    Automatic speech recognition (ASR) is a critical component for CHIL services. For example, it provides the input to higher-level technologies, such as summarization and question answering, as discussed in Chapter 8. In the spirit of ubiquitous computing, the goal of ASR in CHIL is to achieve a high performance using far-field sensors (networks of microphone arrays and distributed far-field microphones). However, close-talking microphones are also of interest, as they are used to benchmark ASR system development by providing a best-case acoustic channel scenario to compare against.

  4. Automatic aircraft recognition

    NASA Astrophysics Data System (ADS)

    Hmam, Hatem; Kim, Jijoong

    2002-08-01

    Automatic aircraft recognition is very complex because of clutter, shadows, clouds, self-occlusion and degraded imaging conditions. This paper presents an aircraft recognition system, which assumes from the start that the image is possibly degraded, and implements a number of strategies to overcome edge fragmentation and distortion. The current vision system employs a bottom up approach, where recognition begins by locating image primitives (e.g., lines and corners), which are then combined in an incremental fashion into larger sets of line groupings using knowledge about aircraft, as viewed from a generic viewpoint. Knowledge about aircraft is represented in the form of whole/part shape description and the connectedness property, and is embedded in production rules, which primarily aim at finding instances of the aircraft parts in the image and checking the connectedness property between the parts. Once a match is found, a confidence score is assigned and as evidence in support of an aircraft interpretation is accumulated, the score is increased proportionally. Finally a selection of the resulting image interpretations with the highest scores, is subjected to competition tests, and only non-ambiguous interpretations are allowed to survive. Experimental results demonstrating the effectiveness of the current recognition system are given.

  5. Towards spoken clinical-question answering: evaluating and adapting automatic speech-recognition systems for spoken clinical questions

    PubMed Central

    Liu, Feifan; Tur, Gokhan; Hakkani-Tür, Dilek

    2011-01-01

    Objective To evaluate existing automatic speech-recognition (ASR) systems to measure their performance in interpreting spoken clinical questions and to adapt one ASR system to improve its performance on this task. Design and measurements The authors evaluated two well-known ASR systems on spoken clinical questions: Nuance Dragon (both generic and medical versions: Nuance Gen and Nuance Med) and the SRI Decipher (the generic version SRI Gen). The authors also explored language model adaptation using more than 4000 clinical questions to improve the SRI system's performance, and profile training to improve the performance of the Nuance Med system. The authors reported the results with the NIST standard word error rate (WER) and further analyzed error patterns at the semantic level. Results Nuance Gen and Med systems resulted in a WER of 68.1% and 67.4% respectively. The SRI Gen system performed better, attaining a WER of 41.5%. After domain adaptation with a language model, the performance of the SRI system improved 36% to a final WER of 26.7%. Conclusion Without modification, two well-known ASR systems do not perform well in interpreting spoken clinical questions. With a simple domain adaptation, one of the ASR systems improved significantly on the clinical question task, indicating the importance of developing domain/genre-specific ASR systems. PMID:21705457

  6. Automatic barcode recognition method based on adaptive edge detection and a mapping model

    NASA Astrophysics Data System (ADS)

    Yang, Hua; Chen, Lianzheng; Chen, Yifan; Lee, Yong; Yin, Zhouping

    2016-09-01

    An adaptive edge detection and mapping (AEDM) algorithm to address the challenging one-dimensional barcode recognition task with the existence of both image degradation and barcode shape deformation is presented. AEDM is an edge detection-based method that has three consecutive phases. The first phase extracts the scan lines from a cropped image. The second phase involves detecting the edge points in a scan line. The edge positions are assumed to be the intersecting points between a scan line and a corresponding well-designed reference line. The third phase involves adjusting the preliminary edge positions to more reasonable positions by employing prior information of the coding rules. Thus, a universal edge mapping model is established to obtain the coding positions of each edge in this phase, followed by a decoding procedure. The Levenberg-Marquardt method is utilized to solve this nonlinear model. The computational complexity and convergence analysis of AEDM are also provided. Several experiments were implemented to evaluate the performance of AEDM algorithm. The results indicate that the efficient AEDM algorithm outperforms state-of-the-art methods and adequately addresses multiple issues, such as out-of-focus blur, nonlinear distortion, noise, nonlinear optical illumination, and situations that involve the combinations of these issues.

  7. Automatic transmission adapter kit

    SciTech Connect

    Stich, R.L.; Neal, W.D.

    1987-02-10

    This patent describes, in a four-wheel-drive vehicle apparatus having a power train including an automatic transmission and a transfer case, an automatic transmission adapter kit for installation of a replacement automatic transmission of shorter length than an original automatic transmission in the four-wheel-drive vehicle. The adapter kit comprises: an extension housing interposed between the replacement automatic transmission and the transfer case; an output shaft, having a first end which engages the replacement automatic transmission and a second end which engages the transfer case; first sealing means for sealing between the extension housing and the replacement automatic transmission; second sealing means for sealing between the extension housing and the transfer case; and fastening means for connecting the extension housing between the replacement automatic transmission and the transfer case.

  8. Automatic Recognition of Deaf Speech.

    ERIC Educational Resources Information Center

    Abdelhamied, Kadry; And Others

    1990-01-01

    This paper describes a speech perception system for automatic recognition of deaf speech. Using a 2-step segmentation approach for 468 utterances by 2 hearing-impaired men and 2 normal-hearing men, rates as high as 93.01 percent and 81.81 percent recognition were obtained in recognizing from deaf speech isolated words and connected speech,…

  9. Automatic object recognition

    NASA Technical Reports Server (NTRS)

    Ranganath, H. S.; Mcingvale, Pat; Sage, Heinz

    1988-01-01

    Geometric and intensity features are very useful in object recognition. An intensity feature is a measure of contrast between object pixels and background pixels. Geometric features provide shape and size information. A model based approach is presented for computing geometric features. Knowledge about objects and imaging system is used to estimate orientation of objects with respect to the line of sight.

  10. Automatic testing of speech recognition.

    PubMed

    Francart, Tom; Moonen, Marc; Wouters, Jan

    2009-02-01

    Speech reception tests are commonly administered by manually scoring the oral response of the subject. This requires a test supervisor to be continuously present. To avoid this, a subject can type the response, after which it can be scored automatically. However, spelling errors may then be counted as recognition errors, influencing the test results. We demonstrate an autocorrection approach based on two scoring algorithms to cope with spelling errors. The first algorithm deals with sentences and is based on word scores. The second algorithm deals with single words and is based on phoneme scores. Both algorithms were evaluated with a corpus of typed answers based on three different Dutch speech materials. The percentage of differences between automatic and manual scoring was determined, in addition to the mean difference in speech recognition threshold. The sentence correction algorithm performed at a higher accuracy than commonly obtained with these speech materials. The word correction algorithm performed better than the human operator. Both algorithms can be used in practice and allow speech reception tests with open set speech materials over the internet.

  11. Speaker-Machine Interaction in Automatic Speech Recognition. Technical Report.

    ERIC Educational Resources Information Center

    Makhoul, John I.

    The feasibility and limitations of speaker adaptation in improving the performance of a "fixed" (speaker-independent) automatic speech recognition system were examined. A fixed vocabulary of 55 syllables is used in the recognition system which contains 11 stops and fricatives and five tense vowels. The results of an experiment on speaker…

  12. Aided versus automatic target recognition

    NASA Astrophysics Data System (ADS)

    O'Hair, Mark A.; Purvis, Bradley D.; Brown, Jeff

    1997-06-01

    Automatic target recognition (ATR) algorithms have offered the promise of recognizing items of military importance over the past 20 years. It is the experience of the authors that greater ATR success would be possible if the ATR were used to 'aid' the human operator instead of automatically 'direct' the operator. ATRs have failed not due to their probability of detection versus false alarm rate, but to neglect of the human component. ATRs are designed to improve overall throughput by relieving the human operator of the need to perform repetitive tasks like scanning vast quantities of imagery for possible targets. ATRs are typically inserted prior to the operator and provide cues, which are then accepted or rejected. From our experience at three field exercises and a current operational deployment to the Bosnian theater, this is not the best way to get total system performance. The human operator makes decisions based on learning, history of past events, and surrounding contextual information. Loss of these factors by providing imagery, latent with symbolic cues on top of the original imagery, actually increases the workload of the operator. This paper covers the lessons learned from the field demonstrations and the operational deployment. The reconnaissance and intelligence community's primary use of an ATR should be to establish prioritized cues of potential targets for an operator to 'pull' from and to be able to 'send' targets identified by the operator for a 'second opinion.' The Army and Air Force are modifying their exploitation workstations over the next 18 months to use ATRs, which operate in this fashion. This will be the future architecture that ATRs for the reconnaissance and intelligence community should integrate into.

  13. 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.

  14. Pattern Recognition For Automatic Visual Inspection

    NASA Astrophysics Data System (ADS)

    Fu, K. S.

    1982-11-01

    Three major approaches to pattern recognition, (1) template matching, (2) decision-theoretic approach, and (3) structural and syntactic approach, are briefly introduced. The application of these approaches to automatic visual inspection of manufactured products are then reviewed. A more general method for automatic visual inspection of IC chips is then proposed. Several practical examples are included for illustration.

  15. Towards Multilingual Interoperability in Automatic Speech Recognition

    DTIC Science & Technology

    2000-08-01

    UNCLASSIFIED Defense Technical Information Center Compilation Part Notice ADP010388 TITLE: Towards Multilingual Interoperability in Automatic Speech...component part numbers comprise the compilation report: ADPO10378 thru ADPO10397 UNCLASSIFIED 69 TOWARDS MULTILINGUAL INTEROPERABILITY IN AUTOMATIC SPEECH...communication, we address multilingual interoperability (DARPA) [39, 5, 12, 40, 14, 43]. aspects in speech recognition. After giving a tentative

  16. Prospects for Automatic Recognition of Speech.

    ERIC Educational Resources Information Center

    Houde, Robert

    1979-01-01

    Originally part of a symposium on educational media for the deaf, the paper discusses problems with the development of technology permitting simultaneous automatic captioning of speech. It is concluded that success with a machine which will provide automatic recognition of speech is still many years in the future. (PHR)

  17. Sparsity-motivated automatic target recognition.

    PubMed

    Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

    2011-04-01

    We present an automatic target recognition algorithm using the recently developed theory of sparse representations and compressive sensing. We show how sparsity can be helpful for efficient utilization of data for target recognition. We verify the efficacy of the proposed algorithm in terms of the recognition rate and confusion matrices on the well known Comanche (Boeing-Sikorsky, USA) forward-looking IR data set consisting of ten different military targets at different orientations.

  18. 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.

  19. Speech production knowledge in automatic speech recognition.

    PubMed

    King, Simon; Frankel, Joe; Livescu, Karen; McDermott, Erik; Richmond, Korin; Wester, Mirjam

    2007-02-01

    Although much is known about how speech is produced, and research into speech production has resulted in measured articulatory data, feature systems of different kinds, and numerous models, speech production knowledge is almost totally ignored in current mainstream approaches to automatic speech recognition. Representations of speech production allow simple explanations for many phenomena observed in speech which cannot be easily analyzed from either acoustic signal or phonetic transcription alone. In this article, a survey of a growing body of work in which such representations are used to improve automatic speech recognition is provided.

  20. Automatic face recognition in HDR imaging

    NASA Astrophysics Data System (ADS)

    Pereira, Manuela; Moreno, Juan-Carlos; Proença, Hugo; Pinheiro, António M. G.

    2014-05-01

    The gaining popularity of the new High Dynamic Range (HDR) imaging systems is raising new privacy issues caused by the methods used for visualization. HDR images require tone mapping methods for an appropriate visualization on conventional and non-expensive LDR displays. These visualization methods might result in completely different visualization raising several issues on privacy intrusion. In fact, some visualization methods result in a perceptual recognition of the individuals, while others do not even show any identity. Although perceptual recognition might be possible, a natural question that can rise is how computer based recognition will perform using tone mapping generated images? In this paper, a study where automatic face recognition using sparse representation is tested with images that result from common tone mapping operators applied to HDR images. Its ability for the face identity recognition is described. Furthermore, typical LDR images are used for the face recognition training.

  1. 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.

  2. 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.

  3. 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.

  4. Automatic recognition of auroral forms

    NASA Astrophysics Data System (ADS)

    Pudovkin, Mikhail I.; Steen, Ake; Nikolaev, N. V.; Kornilov, O. I.; Brandstrom, Urban; Gustavsson, Bjorn; Rydesater, Peter

    1999-03-01

    A method for recognition of geometrical shapes in auroral forms is presented. The method is based on the analysis of isolines of auroral luminosity shapes. The basic variables used are the angle, (phi) (s), between the tangent of the contour and the x-axis of an arbitrary coordinate system, and the differential, d(phi) (s), as a function of the distance, s, along the contour. The analysis also includes Fourier transformation of the experimental function d(phi) (s) obtained for the observed auroral forms, and the comparison of the power spectrum, F(k), with those for a series of model contours. Some dynamical characteristics of the aurora are also discussed.

  5. 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.

  6. Automatic target recognition employing signal compression.

    PubMed

    Ragothaman, Pradeep; Mikhael, Wasfy B; Muise, Robert R; Mahalanobis, Abhijit

    2007-07-20

    Quadratic correlation filters (QCFs) have been used successfully to detect and recognize targets embedded in background clutter. Recently, a QCF called the Rayleigh quotient quadratic correlation filter (RQQCF) was formulated for automatic target recognition (ATR) in IR imagery. Using training images from target and clutter classes, the RQQCF explicitly maximized a class separation metric. What we believe to be a novel approach is presented for ATR that synthesizes the RQQCF using compressed images. The proposed approach considerably reduces the computational complexity and storage requirements while retaining the high recognition accuracy of the original RQQCF technique. The advantages of the proposed scheme are illustrated using sample results obtained from experiments on IR imagery.

  7. 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.

  8. Multilingual Vocabularies in Automatic Speech Recognition

    DTIC Science & Technology

    2000-08-01

    monolingual (a few thousands) is an obstacle to a full generalization of the inventories, then moved to the multilingual case. In the approach towards the...language. of multilingual models than the monolingual models, and it was specifically observed in the test with Spanish utterances. In fact...UNCLASSIFIED Defense Technical Information Center Compilation Part Notice ADP010389 TITLE: Multilingual Vocabularies in Automatic Speech Recognition

  9. Automatic target recognition via classical detection theory

    NASA Astrophysics Data System (ADS)

    Morgan, Douglas R.

    1995-07-01

    Classical Bayesian detection and decision theory applies to arbitrary problems with underlying probabilistic models. When the models describe uncertainties in target type, pose, geometry, surround, scattering phenomena, sensor behavior, and feature extraction, then classical theory directly yields detailed model-based automatic target recognition (ATR) techniques. This paper reviews options and considerations arising under a general Bayesian framework for model- based ATR, including approaches to the major problems of acquiring probabilistic models and of carrying out the indicated Bayesian computations.

  10. Automatic target recognition on the connection machine

    SciTech Connect

    Buchanan, J.R. )

    1989-09-01

    Automatic target recognition (ATR) is a computationally intensive problem that benefits from the abilities of the Connection Machine (CM), a massively parallel computer used for data-level parallel computing. The large computational resources of the CM can efficiently handle an approach to ATR that uses parallel stereo-matching and neural-network algorithms. Such an approach shows promise as an ATR system of satisfactory performance. 13 refs.

  11. Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

    NASA Astrophysics Data System (ADS)

    Caballero Morales, Santiago Omar; Cox, Stephen J.

    2009-12-01

    Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.

  12. Unification of automatic target tracking and automatic target recognition

    NASA Astrophysics Data System (ADS)

    Schachter, Bruce J.

    2014-06-01

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

  13. Automatic target recognition in acoustics: An overview

    NASA Astrophysics Data System (ADS)

    Sacha, John R.

    2002-11-01

    Automatic target recognition (ATR) constitutes one of the major uses for acoustical signal processing. ATR is employed in manned systems for operator workload reduction and performance improvement, as well as in autonomous applications. An overview of some of the major components involved in the architecture of such systems is provided. Feature extraction is the most critical step of ATR and is necessarily application specific. Generic feature selection and ranking methods are presented, including heuristic search and information-theoretic measures. Basic pattern recognition definitions and techniques are reviewed. Commonly used classification paradigms include classical statistical formulations, both parametric and nonparametric, and neural nets; support vector machines and nonmetric methods such as decision forests are some alternative techniques that have received recent attention. A few practical issues often encountered when constructing recognition systems, including training data requirements, ground truth labeling, and performance evaluation methodologies and metrics, are also addressed.

  14. Automatic target recognition via sparse representations

    NASA Astrophysics Data System (ADS)

    Estabridis, Katia

    2010-04-01

    Automatic target recognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ∈ Rnxm(n<recognition problems are solved by finding the sparse-solution to the undetermined system y=Ax via Orthogonal Matching Pursuit (OMP) and l1 minimization techniques. Visible and MWIR imagery collected by the Army Night Vision and Electronic Sensors Directorate (NVESD) was utilized to test the algorithm. Results show an average detection and recognition rates above 95% for targets at ranges up to 3Km for both image modalities.

  15. Automatic speech recognition for large vocabularies

    NASA Astrophysics Data System (ADS)

    Aktas, A.; Kaemmerer, B.; Kuepper, W.; Lagger, H.

    1985-12-01

    An isolated word recognition system for large vocabularies (1000 to 5000 words) with 98% recognition performance was developed. It was implemented on an array processor for real time requirements. The speech signal is described by short time autocorrelation functions. Short response times as well as high recognition accuracies are achieved by means of a hierarchical classification scheme. A fast preselection stage yields a small number of suitable word candidates to be considered for further classification. To that end a linear segmentation or a segmentation based on acoustic or phonetic cues was performed. High selectivity is obtained by using fine temporal resolution and nonlinear time alignment in the final classification step. By taking into account phonetically identical fragments of words, a distinction between highly confusable words can be made. Speaker adaptation for new system users is performed within a relatively short training phase.

  16. 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.

  17. 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.

  18. Automatic semantic feedback during visual word recognition.

    PubMed

    Reimer, Jason F; Lorsbach, Thomas C; Bleakney, Dana M

    2008-04-01

    Four experiments were conducted to determine whether semantic feedback spreads to orthographic and/or phonological representations during visual word recognition and whether such feedback occurs automatically. Three types of prime-target word pairs were used within the mediated-priming paradigm: (1) homophonically mediated (e.g.,frog-[toad]-towed), (2) orthographically mediated (e.g.,frog-[toad]-told), and (3) associatively related (e.g.,frog-toad). Using both brief (53 msec; Experiment 1) and long (413 msec; Experiment 3) prime exposure durations, significant facilitatory-priming effects were found in the response time data with orthographically, but not homophonically, mediated prime-target word pairs. When the prime exposure duration was shortened to 33 msec in Experiment 4, however, facilitatory priming was absent with both orthographically and homophonically mediated word pairs. In addition, with a brief (53-msec) prime exposure duration, direct-priming effects were found with associatively (e.g.,frog-toad), orthographically (e.g., toad-told), and homophonically (e.g., toad-towed) related word pairs in Experiment 2. Taken together, these results indicate that following the initial activation of semantic representations, activation automatically feeds back to orthographic, but not phonological, representations during the early stages of word processing. These findings were discussed in the context of current accounts of visual word recognition.

  19. 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.

  20. Automatic Speech Recognition Based on Electromyographic Biosignals

    NASA Astrophysics Data System (ADS)

    Jou, Szu-Chen Stan; Schultz, Tanja

    This paper presents our studies of automatic speech recognition based on electromyographic biosignals captured from the articulatory muscles in the face using surface electrodes. We develop a phone-based speech recognizer and describe how the performance of this recognizer improves by carefully designing and tailoring the extraction of relevant speech feature toward electromyographic signals. Our experimental design includes the collection of audibly spoken speech simultaneously recorded as acoustic data using a close-speaking microphone and as electromyographic signals using electrodes. Our experiments indicate that electromyographic signals precede the acoustic signal by about 0.05-0.06 seconds. Furthermore, we introduce articulatory feature classifiers, which had recently shown to improved classical speech recognition significantly. We describe that the classification accuracy of articulatory features clearly benefits from the tailored feature extraction. Finally, these classifiers are integrated into the overall decoding framework applying a stream architecture. Our final system achieves a word error rate of 29.9% on a 100-word recognition task.

  1. Towards automatic musical instrument timbre recognition

    NASA Astrophysics Data System (ADS)

    Park, Tae Hong

    This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

  2. Image simulation for automatic license plate recognition

    NASA Astrophysics Data System (ADS)

    Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

    2012-01-01

    Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

  3. 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.

  4. 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.

  5. 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.

  6. Cascaded automatic target recognition (Cascaded ATR)

    NASA Astrophysics Data System (ADS)

    Walls, Bradley

    2010-04-01

    The global war on terror has plunged US and coalition forces into a battle space requiring the continuous adaptation of tactics and technologies to cope with an elusive enemy. As a result, technologies that enhance the intelligence, surveillance, and reconnaissance (ISR) mission making the warfighter more effective are experiencing increased interest. In this paper we show how a new generation of smart cameras built around foveated sensing makes possible a powerful ISR technique termed Cascaded ATR. Foveated sensing is an innovative optical concept in which a single aperture captures two distinct fields of view. In Cascaded ATR, foveated sensing is used to provide a coarse resolution, persistent surveillance, wide field of view (WFOV) detector to accomplish detection level perception. At the same time, within the foveated sensor, these detection locations are passed as a cue to a steerable, high fidelity, narrow field of view (NFOV) detector to perform recognition level perception. Two new ISR mission scenarios, utilizing Cascaded ATR, are proposed.

  7. 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

  8. Automatic detection and recognition of signs from natural scenes.

    PubMed

    Chen, Xilin; Yang, Jie; Zhang, Jing; Waibel, Alex

    2004-01-01

    In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

  9. Automatic target recognition apparatus and method

    SciTech Connect

    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.

  10. Automatic anatomy recognition via fuzzy object models

    NASA Astrophysics Data System (ADS)

    Udupa, Jayaram K.; Odhner, Dewey; Falcão, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Matsumoto, Monica; Grevera, George J.; Saboury, Babak; Torigian, Drew A.

    2012-02-01

    To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) during radiological image reading becomes essential. As part of this larger goal, last year at this conference we presented a fuzzy strategy for building body-wide group-wise anatomic models. In the present paper, we describe the further advances made in fuzzy modeling and the algorithms and results achieved for AAR by using the fuzzy models. The proposed AAR approach consists of three distinct steps: (a) Building fuzzy object models (FOMs) for each population group G. (b) By using the FOMs to recognize the individual objects in any given patient image I under group G. (c) To delineate the recognized objects in I. This paper will focus mostly on (b). FOMs are built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. The hierarchical pose relationships from the parent to offspring are codified in the FOMs. Several approaches are being explored currently, grouped under two strategies, both being hierarchical: (ra1) those using search strategies; (ra2) those strategizing a one-shot approach by which the model pose is directly estimated without searching. Based on 32 patient CT data sets each from the thorax and abdomen and 25 objects modeled, our analysis indicates that objects do not all scale uniformly with patient size. Even the simplest among the (ra2) strategies of recognizing the root object and then placing all other descendants as per the learned parent-to-offspring pose relationship bring the models on an average within about 18 mm of the true locations.

  11. Hybrid ANN-ES architecture for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Teng, Chungte; Ligomenides, Panos A.

    1992-03-01

    Automatic target recognition can benefit from cooperation of artificial neural networks (ANNs) and expert systems (ESs). Bottom-up training and generalization properties of artificial neural networks, and top-down utilization of accumulated knowledge by expert system processors, can be combined to offer robust performance of the automatic target recognition models. In this paper, we propose a modular, flexible and expandable, hybrid architecture which provides cooperative, functional and operational interfaces between expert system and artificial neural networks facilities. In order to make the problem more specific, we apply this architecture to the Multline Optical Character Reader (MLOCR) system, which is being developed to sort the postal mail pieces automatically.

  12. Autonomous learning approach for automatic target recognition processor

    NASA Astrophysics Data System (ADS)

    Chao, Tien-Hsin; Lu, Thomas

    2011-04-01

    JPL is developing a comprehensive Automatic Target Recognition (ATR) system that consists of an innovative anomaly detection preprocessing module and an automatic training target recognition module. The anomaly detection module is trained with an imaging data feature retrieved from an imaging sensor suite that represents the states of the normalcy model. The normalcy model is then trained from a self-organizing learning system over a period of time and fed into the anomaly detection module for scene anomaly monitoring and detection. The "abnormal" event detection will be sent to a human operator for further investigation responses. The target recognition will be continuously updated with the "normal' input sensor data. The combination of the anomaly detection preprocessing module to the re-trainable target recognition processor will result in a dynamic ATR system that is capable of automatic detection of anomaly event and provide an early warning to a human operator for in-time warning and response.

  13. Automatic Intention Recognition in Conversation Processing

    ERIC Educational Resources Information Center

    Holtgraves, Thomas

    2008-01-01

    A fundamental assumption of many theories of conversation is that comprehension of a speaker's utterance involves recognition of the speaker's intention in producing that remark. However, the nature of intention recognition is not clear. One approach is to conceptualize a speaker's intention in terms of speech acts [Searle, J. (1969). "Speech…

  14. 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.

  15. Automatic recognition of targets from hyperspectra images

    NASA Astrophysics Data System (ADS)

    Lampropoulos, George A.; Li, Yifeng; Boulter, James F.

    1998-12-01

    In this paper, we present the formulation of the problem for recognition of targets from hyperspectra images. It is shown that conventional recognition techniques may be extended to hyperspectra images for the distribution process. It is also shown that the recognition process is directly proportional to the number of multispectra frames that represent each target. For the discrimination process we propose a parametric and a nonparametric process in which both are extensions to Fisher and Fukunaga-Mantock methods respectively. Examples that show the composition of hyperspectra features are presented.

  16. 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…

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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.

  3. 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.

  4. Kernel generalized neighbor discriminant embedding for SAR automatic target recognition

    NASA Astrophysics Data System (ADS)

    Huang, Yulin; Pei, Jifang; Yang, Jianyu; Wang, Tao; Yang, Haiguang; Wang, Bing

    2014-12-01

    In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also proposed, called kernel-GNDE (KGNDE). The experiment in this paper shows that the proposed algorithms have better recognition performance than PCA and KPCA.

  5. 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.

  6. 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.

  7. Automatic Target Recognition Based on Cross-Plot

    PubMed Central

    Wong, Kelvin Kian Loong; Abbott, Derek

    2011-01-01

    Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

  8. Automatic target recognition based on cross-plot.

    PubMed

    Wong, Kelvin Kian Loong; Abbott, Derek

    2011-01-01

    Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.

  9. 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…

  10. 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…

  11. 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…

  12. Explicit Pronunciation Training Using Automatic Speech Recognition Technology.

    ERIC Educational Resources Information Center

    Dalby, Jonathan; Kewley-Port, Diane

    1999-01-01

    Describes a system, provisionally named Pronto, that uses automatic speech recognition for training pronunciation of second languages in adult learners. Methods are included for developing training in Pronto, and results are presented from evaluating classes of speech recognizers for use in different aspects of pronunciation training. (Author/VWL)

  13. 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.

  14. 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.

  15. 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

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

    PubMed

    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.

  17. Graphical models and automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Bilmes, Jeff A.

    2002-11-01

    Graphical models (GMs) are a flexible statistical abstraction that has been successfully used to describe problems in a variety of different domains. Commonly used for ASR, hidden Markov models are only one example of the large space of models constituting GMs. Therefore, GMs are useful to understand existing ASR approaches and also offer a promising path towards novel techniques. In this work, several such ways are described including (1) using both directed and undirected GMs to represent sparse Gaussian and conditional Gaussian distributions, (2) GMs for representing information fusion and classifier combination, (3) GMs for representing hidden articulatory information in a speech signal, (4) structural discriminability where the graph structure itself is discriminative, and the difficulties that arise when learning discriminative structure (5) switching graph structures, where the graph may change dynamically, and (6) language modeling. The graphical model toolkit (GMTK), a software system for general graphical-model based speech recognition and time series analysis, will also be described, including a number of GMTK's features that are specifically geared to ASR.

  18. 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.

  19. 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.

  20. 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

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

    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 gives a general description of the ATR system.

  2. Automatic recognition and analysis of synapses. [in brain tissue

    NASA Technical Reports Server (NTRS)

    Ungerleider, J. A.; Ledley, R. S.; Bloom, F. E.

    1976-01-01

    An automatic system for recognizing synaptic junctions would allow analysis of large samples of tissue for the possible classification of specific well-defined sets of synapses based upon structural morphometric indices. In this paper the three steps of our system are described: (1) cytochemical tissue preparation to allow easy recognition of the synaptic junctions; (2) transmitting the tissue information to a computer; and (3) analyzing each field to recognize the synapses and make measurements on them.

  3. 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.

  4. Automatic target recognition flight prototype for police helicopters

    NASA Astrophysics Data System (ADS)

    Stahl, Christoph; Haisch, Stefan; Wolf, Peter

    2002-07-01

    A cooperation between the European Aeronautic Defence and Space Company AG (EADS) and the Bavarian Ministry of the Interior was started at the beginning of 2001 to develop an application for automatic target recognition technology for police helicopter missions. The Bavarian Police Air Support Unit is the main support partner and first user. Bavarian polic helicopters are equipped with a modern infrared system (FLIR) especially for night missions. EADS has extensive knowledge in the area of sensor image exploitation and automatic target recognition (ATR). The technology has been developed for military aircraft reconnaissance missions. The same software kernel is used for a flight prototype which is integrated in a police helicopter. The integration concept presented in this paper is set up so as not to interfere the existing FLIR system in any way. The flight prototype which will be described in detail consists of standard hardware (COTS) components and has the main functionality of detecting pre-selected object classes. With the flight prototype a comprehensive in-flight testing of the automatic target recognition application is enabled. The test procedure and first results of the flight tests are explained with selected examples. The cooperation will go on to further enhance the operational effectiveness of the Bavarian police helicopters.

  5. Adaptive automatic balancing of magnetic bearing systems

    NASA Astrophysics Data System (ADS)

    Kim, Jong-Sun

    Rotating machinery including magnetic bearings are usually persistently excited by the rotation related disturbances such as mass unbalance; hence there exists a residual vibration in the steady state response even if the closed loop system is asymptotically stable. In order to control the periodic disturbances, a disturbance accommodating controller (DAC) is designed based on the disturbance estimator and applied to the forced balancing of magnetic bearing system. The control objective is to minimize the synchronous component of shaft displacement or control current. In order to account for the variation of the disturbance model due to the shaft of operating speed, an adaptive disturbance accommodating control scheme is developed based on a certain optimality criterion. The continuous time design discretized to implement the controller in the digital computer and the merits and demerits are studied numerically. It is shown that the proposed method is efficient in reducing rotor unbalance and automatic balancing.

  6. DWT features performance analysis for automatic speech recognition of Urdu.

    PubMed

    Ali, Hazrat; Ahmad, Nasir; Zhou, Xianwei; Iqbal, Khalid; Ali, Sahibzada Muhammad

    2014-01-01

    This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition. The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task.

  7. 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.

  8. 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.

  9. Error Rates in Users of Automatic Face Recognition Software.

    PubMed

    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.

  10. 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

  11. Automatic solar feature detection using image processing and pattern recognition techniques

    NASA Astrophysics Data System (ADS)

    Qu, Ming

    The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system. For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In the applications of the solar filament detection, the Stabilized Inverse Diffusion Equation (SIDE) is used to enhance and sharpen filaments; a new method for automatic threshold selection is proposed to extract filaments from background; an SVM classifier with nine input features is used to differentiate between sunspots and filaments. Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are applied to determine filament properties. Furthermore, a filament matching method is proposed to detect filament disappearance. The automatic detection and characterization of flares and filaments have been successfully applied on Halpha full-disk images that are continuously obtained at Big Bear Solar Observatory (BBSO). For automatically detecting and classifying CMEs, the image enhancement, segmentation, and pattern recognition techniques are applied to Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The processed LASCO and BBSO images are saved to file archive, and the physical properties of detected solar features such as intensity and speed are recorded in our database. Researchers are able to access the solar feature database and analyze the solar data efficiently and effectively. The detection and characterization system greatly improves

  12. 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.

  13. Automatic speech recognition using a predictive echo state network classifier.

    PubMed

    Skowronski, Mark D; Harris, John G

    2007-04-01

    We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

  14. The acoustic-modeling problem in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Brown, Peter F.

    1987-12-01

    This thesis examines the acoustic-modeling problem in automatic speech recognition from an information-theoretic point of view. This problem is to design a speech-recognition system which can extract from the speech waveform as much information as possible about the corresponding word sequence. The information extraction process is broken down into two steps: a signal processing step which converts a speech waveform into a sequence of information bearing acoustic feature vectors, and a step which models such a sequence. This thesis is primarily concerned with the use of hidden Markov models to model sequences of feature vectors which lie in a continuous space such as R sub N. It explores the trade-off between packing a lot of information into such sequences and being able to model them accurately. The difficulty of developing accurate models of continuous parameter sequences is addressed by investigating a method of parameter estimation which is specifically designed to cope with inaccurate modeling assumptions.

  15. Automatic procedure for generating symmetry adapted wavefunctions.

    PubMed

    Johansson, Marcus; Veryazov, Valera

    2017-01-01

    Automatic detection of point groups as well as symmetrisation of molecular geometry and wavefunctions are useful tools in computational quantum chemistry. Algorithms for developing these tools as well as an implementation are presented. The symmetry detection algorithm is a clustering algorithm for symmetry invariant properties, combined with logical deduction of possible symmetry elements using the geometry of sets of symmetrically equivalent atoms. An algorithm for determining the symmetry adapted linear combinations (SALCs) of atomic orbitals is also presented. The SALCs are constructed with the use of projection operators for the irreducible representations, as well as subgroups for determining splitting fields for a canonical basis. The character tables for the point groups are auto generated, and the algorithm is described. Symmetrisation of molecules use a projection into the totally symmetric space, whereas for wavefunctions projection as well and partner function determination and averaging is used. The software has been released as a stand-alone, open source library under the MIT license and integrated into both computational and molecular modelling software.Graphical abstract.

  16. A robust face recognition algorithm under varying illumination using adaptive retina modeling

    NASA Astrophysics Data System (ADS)

    Cheong, Yuen Kiat; Yap, Vooi Voon; Nisar, Humaira

    2013-10-01

    Variation in illumination has a drastic effect on the appearance of a face image. This may hinder the automatic face recognition process. This paper presents a novel approach for face recognition under varying lighting conditions. The proposed algorithm uses adaptive retina modeling based illumination normalization. In the proposed approach, retina modeling is employed along with histogram remapping following normal distribution. Retina modeling is an approach that combines two adaptive nonlinear equations and a difference of Gaussians filter. Two databases: extended Yale B database and CMU PIE database are used to verify the proposed algorithm. For face recognition Gabor Kernel Fisher Analysis method is used. Experimental results show that the recognition rate for the face images with different illumination conditions has improved by the proposed approach. Average recognition rate for Extended Yale B database is 99.16%. Whereas, the recognition rate for CMU-PIE database is 99.64%.

  17. Spectral analysis methods for automatic speech recognition applications

    NASA Astrophysics Data System (ADS)

    Parinam, Venkata Neelima Devi

    In this thesis, we evaluate the front-end of Automatic Speech Recognition (ASR) systems, with respect to different types of spectral processing methods that are extensively used. A filter bank approach for front end spectral analysis is one of the common methods used for spectral analysis. In this work we describe and evaluate spectral analysis based on Mel and Gammatone filter banks. These filtering methods are derived from auditory models and are thought to have some advantages for automatic speech recognition work. Experimentally, however, we show that direct use of FFT spectral values is just as effective as using either Mel or Gammatone filter banks, provided that the features extracted from the FFT spectral values take into account a Mel or Mel-like frequency scale. It is also shown that trajectory features based on sliding block of spectral features, computed using either FFT or filter bank spectral analysis are considerably more effective, in terms of ASR accuracy, than are delta and delta-delta terms often used for ASR. Although there is no major performance disadvantage to using a filter bank, simplicity of analysis is a reason to eliminate this step in speech processing. These assertions hold for both clean and noisy speech.

  18. PATTERN RECOGNITION AND CLASSIFICATION USING ADAPTIVE LINEAR NEURON DEVICES

    DTIC Science & Technology

    adaption by an adaptive linear neuron ( Adaline ), as applied to the pattern recognition and classification problem; (2) Four possible iterative adaption...schemes which may be used to train as Adaline ; (3) Use of Multiple Adalines (Madaline) and two logic layers to increase system capability; and (4) Use...of Adaline in the practical fields of Speech Recognition, Weather Forecasting and Adaptive Control Systems and the possible use of Madaline in the Character Recognition field.

  19. Contour-based automatic crater recognition using digital elevation models from Chang'E missions

    NASA Astrophysics Data System (ADS)

    Zuo, Wei; Zhang, Zhoubin; Li, Chunlai; Wang, Rongwu; Yu, Linjie; Geng, Liang

    2016-12-01

    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). Through DEM and image processing, this method can be used to reconstruct contour surfaces, extract and combine contour lines, set the characteristic parameters of crater morphology, and establish a crater pattern recognition program. The method has been tested and verified with DEM data from Chang'E-1 (CE-1) and Chang'E-2 (CE-2), showing a strong crater recognition ability with high detection rate, high robustness, and good adaptation to recognize various craters with different diameter and morphology. The method has been used to identify craters with high precision and accuracy on the Moon. The results meet requirements for supporting exploration and related scientific research for the Moon and planets.

  20. 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).

  1. Compositional Dictionaries for Domain Adaptive Face Recognition.

    PubMed

    Qiang Qiu; Chellappa, Rama

    2015-12-01

    We present a dictionary learning approach to compensate for the transformation of faces due to the changes in view point, illumination, resolution, and so on. The key idea of our approach is to force domain-invariant sparse coding, i.e., designing a consistent sparse representation of the same face in different domains. In this way, the classifiers trained on the sparse codes in the source domain consisting of frontal faces can be applied to the target domain (consisting of faces in different poses, illumination conditions, and so on) without much loss in recognition accuracy. The approach is to first learn a domain base dictionary, and then describe each domain shift (identity, pose, and illumination) using a sparse representation over the base dictionary. The dictionary adapted to each domain is expressed as the sparse linear combinations of the base dictionary. In the context of face recognition, with the proposed compositional dictionary approach, a face image can be decomposed into sparse representations for a given subject, pose, and illumination. This approach has three advantages. First, the extracted sparse representation for a subject is consistent across domains, and enables pose and illumination insensitive face recognition. Second, sparse representations for pose and illumination can be subsequently used to estimate the pose and illumination condition of a face image. Last, by composing sparse representations for the subject and the different domains, we can also perform pose alignment and illumination normalization. Extensive experiments using two public face data sets are presented to demonstrate the effectiveness of the proposed approach for face recognition.

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

    PubMed

    Botti, F; Alexander, A; Drygajlo, A

    2004-12-02

    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.

  3. Automatic Speech Recognition in the Teaching of Second Languages: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Pulliam, Robert

    This bibliography, containing 73 annotated entries, is designed as an aid to researchers who may wish to experiment using automatic speech recognition in programed instruction, computer assisted instruction, or task simulation devices. The bibliography samples recent literature on the technology of automatic speech recognition, on efforts to…

  4. Acoustic censusing using automatic vocalization classification and identity recognition.

    PubMed

    Adi, Kuntoro; Johnson, Michael T; Osiejuk, Tomasz S

    2010-02-01

    This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden Markov models (HMMs) and Gaussian mixture models (GMMs) similar to methods used in the speech recognition community for tasks such as speaker identification and clustering. Initial experiments using a Norwegian ortolan bunting (Emberiza hortulana) data set show the feasibility and effectiveness of the approach. Individually distinct acoustic features have been observed in a wide range of animal species, and this combined with the widespread success of speaker identification and verification methods for human speech suggests that robust automatic identification of individuals from their vocalizations is attainable. Only a few studies, however, have yet attempted to use individual acoustic distinctiveness to directly assess population density and structure. The approach introduced here offers a direct mechanism for using individual vocal variability to create simpler and more accurate population assessment tools in vocally active species.

  5. Exploring the Effect of Illumination on Automatic Expression Recognition using the ICT-3DRFE Database

    DTIC Science & Technology

    2011-11-04

    expres- sion recognition with illumination-corrected image sequences,” Auto- matic Face and Gesture Recognition , 2008. FG ’08. 8th IEEE Interna...Wu, Movellan, J., Computer Expres- sion Recognition Toolbox, Univ. of California, San Diego, CA, Automatic Face and Gesture Recognition , 2008 [8] Peers...and Gesture Recognition (FG’00), 2000 [17] Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J. (1998), Coding fa- cial expressions with Gabor wavelets

  6. 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.

  7. 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.

  8. Automatic DarkAdaptation Threshold Detection Algorithm.

    PubMed

    G de Azevedo, Dario; Helegda, Sergio; Glock, Flavio; Russomano, Thais

    2005-01-01

    This paper describes an algorithm used to automatically determine the threshold sensitivity in a new dark adaptometer. The new instrument is controlled by a personal computer and can be used in the investigation of several retinal diseases. The stimulus field is delivered to the eye through the modified optics of a fundus camera. An automated light stimulus source was developed to operate together with this fundus camera. New control parameters were developed in this instrument to improve the traditional Goldmann-Weekers dark adaptometer.

  9. 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%.

  10. Neural-network classifiers for automatic real-world aerial image recognition

    NASA Astrophysics Data System (ADS)

    Greenberg, Shlomo; Guterman, Hugo

    1996-08-01

    We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

  11. Noise reduction using a multimicrophone array for automatic speech recognition on a handheld computer

    NASA Astrophysics Data System (ADS)

    Shaw, Scott T.; Larow, Andrew J.; Schoenborn, William E.; Rodriguez, Jason; Gibian, Gary L.

    2004-05-01

    A four-microphone array and signal-processing card have been integrated with a handheld computer such that the integrated device can be carried in and operated with one hand. Automatic speech recognition (ASR) was added to the USAMRMC/TATRCs Battlefield Medical Information System (BMIST) software using an approach that does not require modifying the original code, to produce a Speech-Capable Personal Digital Assistant (SCPDA). Noise reduction was added to allow operation in noisier environments, using the previously reported Hybrid Adaptive Beamformer (HAB) algorithm. Tests demonstrated benefits of the array over the HP/COMPAQ-iPAQ built-in shielded microphone for noise reduction and automatic speech recognition. In electroacoustic and human testing including voice control and voice annotation, the array provided substantial benefit over the built-in microphone. The benefit varied from about 5 dB (worst-case scenario, diffuse noise) to about 20 dB (best-case scenario, directional noise). Future work is expected to produce more rugged SCPDA prototypes for user evaluations, revise the design based on user feedback and real-world testing, and possibly to allow hands-free use by using ASR to replace the push-to-talk switch, providing feedback aurally and/or via a head-up display. [Work supported by the U.S. Army Medical Research and Materiel Command (USAMRMC), Contract No. DAMD17-02-C-0112.

  12. 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.

  13. 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.

  14. 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

  15. Automatic Ship Recognition Using A Passive Radiometric Sensor

    NASA Astrophysics Data System (ADS)

    Keng, Janmin

    1982-03-01

    Automatic ship recognition is of interest in such problems as over-the-horizon surface surveillance and targeting, long range air targeting, and satellite ocean surveillance. Our approach is model-driven. It uses the fact that the wake caused by a cruising ship has a higher temperature profile than the surrounding water background. In addition, we distinguish between the active wake and the turbulent water surrounding the ship. Furthermore, the temperature of the ship itself is usually lower than that of the ocean. Finally, we make use of the knowledge of ship sizes, convoy patterns and other information concerning ships traveling in formation. An image from a radiometric sensor forms the basis of the analysis. Edge detection and region association techniques are used to locate a "zone of activity", a region of the image that contains the ship. Grey level histogram analysis of the zone is then used to categorize pixels into "ship", "wake", and "water". Results of experiments using this technique are presented.

  16. 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.

  17. 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.

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

    SciTech Connect

    Tescher, A.G.

    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.

  19. Multiexpert automatic speech recognition using acoustic and myoelectric signals.

    PubMed

    Chan, Adrian D C; Englehart, Kevin B; Hudgins, Bernard; Lovely, Dennis F

    2006-04-01

    Classification accuracy of conventional automatic speech recognition (ASR) systems can decrease dramatically under acoustically noisy conditions. To improve classification accuracy and increase system robustness a multiexpert ASR system is implemented. In this system, acoustic speech information is supplemented with information from facial myoelectric signals (MES). A new method of combining experts, known as the plausibility method, is employed to combine an acoustic ASR expert and a MES ASR expert. The plausibility method of combining multiple experts, which is based on the mathematical framework of evidence theory, is compared to the Borda count and score-based methods of combination. Acoustic and facial MES data were collected from 5 subjects, using a 10-word vocabulary across an 18-dB range of acoustic noise. As expected the performance of an acoustic expert decreases with increasing acoustic noise; classification accuracies of the acoustic ASR expert are as low as 11.5%. The effect of noise is significantly reduced with the addition of the MES ASR expert. Classification accuracies remain above 78.8% across the 18-dB range of acoustic noise, when the plausibility method is used to combine the opinions of multiple experts. In addition, the plausibility method produced classification accuracies higher than any individual expert at all noise levels, as well as the highest classification accuracies, except at the 9-dB noise level. Using the Borda count and score-based multiexpert systems, classification accuracies are improved relative to the acoustic ASR expert but are as low as 51.5% and 59.5%, respectively.

  20. Chandra Automatic Processing Task Interface: An Adaptable System Architecture

    NASA Astrophysics Data System (ADS)

    Grier, J. D., Jr.; Plummer, D.

    2007-10-01

    The Chandra Automatic Processing Task Interface (CAPTAIN) is an operations interface to Chandra Automatic Processing (AP) that provides detail management and execution of the AP pipelines. In particular, this kind of management is used in Special Automatic Processing (SAP) where there is a need to select specific pipelines that require non-standard handling for reprocessing of a given data set. Standard AP currently contains approximately 200 pipelines with complex interactions between them. As AP has evolved over the life of the mission, so has the number and attributes of these pipelines. As a result, CAPTAIN provides a system architecture capable of managing and adapting to this evolving system. This adaptability has allowed CAPTAIN to also be used to initiate Chandra Source Catalog Automatic Processing (Level~3 AP) and positions it for use with future automatic processing systems. This paper describes the approach to the development of the CAPTAIN system architecture and the maintainable, extensible and reusable software architecture by which it is implemented.

  1. A hierarchical structure for automatic meshing and adaptive FEM analysis

    NASA Technical Reports Server (NTRS)

    Kela, Ajay; Saxena, Mukul; Perucchio, Renato

    1987-01-01

    A new algorithm for generating automatically, from solid models of mechanical parts, finite element meshes that are organized as spatially addressable quaternary trees (for 2-D work) or octal trees (for 3-D work) is discussed. Because such meshes are inherently hierarchical as well as spatially addressable, they permit efficient substructuring techniques to be used for both global analysis and incremental remeshing and reanalysis. The global and incremental techniques are summarized and some results from an experimental closed loop 2-D system in which meshing, analysis, error evaluation, and remeshing and reanalysis are done automatically and adaptively are presented. The implementation of 3-D work is briefly discussed.

  2. Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

    ERIC Educational Resources Information Center

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

    2016-01-01

    This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

  3. Automatic Format Recognition of MARC Bibliographic Elements: A Review and Projection

    ERIC Educational Resources Information Center

    Butler, Brett

    1974-01-01

    Review and discussion of the techniques of automatic format recognition (AFR) of bibliographic data. Comparison of record-building facilities of Library of Congress, University of California (both AFR techniques), and Ohio College Library Center (non-AFR). Projection of a next logical generation, original format recognition. (Author/LS)

  4. 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.…

  5. Fractional adaptive control for an automatic voltage regulator.

    PubMed

    Aguila-Camacho, Norelys; Duarte-Mermoud, Manuel A

    2013-11-01

    This paper presents the application of a direct Fractional Order Model Reference Adaptive Controller (FOMRAC) to an Automatic Voltage Regulator (AVR). A direct FOMRAC is a direct Model Reference Adaptive Control (MRAC), whose controller parameters are adjusted using fractional order differential equations. Four realizations of the FOMRAC were designed in this work, each one considering different orders for the plant model. The design procedure consisted of determining the optimal values of the fractional order and the adaptive gains for each adaptive law, using Genetic algorithm optimization. Comparisons were made among the four FOMRAC designs, a fractional order PID (FOPID), a classical PID, and four Integer Order Model Reference Adaptive Controllers (IOMRAC), showing that the FOMRAC can improve the controlled system behavior and its robustness with respect to model uncertainties. Finally, some performance indices are presented here for the controlled schemes, in order to show the advantages and disadvantages of the FOMRAC.

  6. 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.

  7. Automatic anatomy recognition in whole-body PET/CT images

    SciTech Connect

    Wang, Huiqian; Udupa, Jayaram K. Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

    2016-01-15

    Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

  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. Multiclassifier fusion of an ultrasonic lip reader in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Jennnings, David L.

    1994-12-01

    This thesis investigates the use of two active ultrasonic devices in collecting lip information for performing and enhancing automatic speech recognition. The two devices explored are called the 'Ultrasonic Mike' and the 'Lip Lock Loop.' The devices are tested in a speaker dependent isolated word recognition task with a vocabulary consisting of the spoken digits from zero to nine. Two automatic lip readers are designed and tested based on the output of the ultrasonic devices. The automatic lip readers use template matching and dynamic time warping to determine the best candidate for a given test utterance. The automatic lip readers alone achieve accuracies of 65-89%, depending on the number of reference templates used. Next the automatic lip reader is combined with a conventional automatic speech recognizer. Both classifier level fusion and feature level fusion are investigated. Feature fusion is based on combining the feature vectors prior to dynamic time warping. Classifier fusion is based on a pseudo probability mass function derived from the dynamic time warping distances. The combined systems are tested with various levels of acoustic noise added. In one typical test, at a signal to noise ratio of 0dB, the acoustic recognizer's accuracy alone was 78%, the automatic lip reader's accuracy was 69%, but the combined accuracy was 93%. This experiment demonstrates that a simple ultrasonic lip motion detector, that has an output data rate 12,500 times less than a typical video camera, can significantly improve the accuracy of automatic speech recognition in noise.

  10. Speech Recognition as a Support Service for Deaf and Hard of Hearing Students: Adaptation and Evaluation. Final Report to Spencer Foundation.

    ERIC Educational Resources Information Center

    Stinson, Michael; Elliot, Lisa; McKee, Barbara; Coyne, Gina

    This report discusses a project that adapted new automatic speech recognition (ASR) technology to provide real-time speech-to-text transcription as a support service for students who are deaf and hard of hearing (D/HH). In this system, as the teacher speaks, a hearing intermediary, or captionist, dictates into the speech recognition system in a…

  11. The effect of mismatched recording conditions on human and automatic speaker recognition in forensic applications.

    PubMed

    Alexander, A; Botti, F; Dessimoz, D; Drygajlo, A

    2004-12-02

    In this paper, we analyse mismatched technical conditions in training and testing phases of speaker recognition and their effect on forensic human and automatic speaker recognition. We use perceptual tests performed by non-experts and compare their performance with that of a baseline automatic speaker recognition system. The degradation of the accuracy of human recognition in mismatched recording conditions is contrasted with that of the automatic system under similar recording conditions. The conditions considered are of public switched telephone network (PSTN) and global system for mobile communications (GSM) transmission and background noise. The perceptual cues that the human subjects use to perceive differences in voices are studied along with their importance in different conditions. We discuss the possibility of increasing the accuracy of automatic systems using the perceptual cues that remain robust to mismatched conditions. We estimate the strength of evidence for both humans and automatic systems, calculating likelihood ratios using the perceptual scores for humans and the log-likelihood scores for automatic systems.

  12. 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

  13. 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.

  14. Preliminary Analysis of Automatic Speech Recognition and Synthesis Technology.

    DTIC Science & Technology

    1983-05-01

    Development 4 Sponsoring Agency Code Washington, D.C. 20593 G-DMT-3 33. Supplementory Notes The Contracting Officer’s Technical Representative for this...descriptions of many commercially available devices. We do not recommend that the Coast Guard pursue the development of a speech recognition system at...SYNTHESIS TECHNIQUES ........................ 130 6.1 SPEECH RECOGNITION TECHNOLOGY....................... 130 6.2 SPEECH SYNTHESIS TECHNOLOGY

  15. 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.

  16. 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.

  17. 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...

  18. 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…

  19. 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…

  20. 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.

  1. Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

    NASA Astrophysics Data System (ADS)

    Kim, Jonghwa; André, Elisabeth

    This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

  2. 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.

  3. 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.

  4. 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.

  5. Automatic adaptive grid refinement for the Euler equations

    NASA Technical Reports Server (NTRS)

    Berger, M. J.; Jameson, A.

    1983-01-01

    A method of adaptive grid refinement for the solution of the steady Euler equations for transonic flow is presented. Algorithm automatically decides where the coarse grid accuracy is insufficient, and creates locally uniform refined grids in these regions. This typically occurs at the leading and trailing edges. The solution is then integrated to steady state using the same integrator (FLO52) in the interior of each grid. The boundary conditions needed on the fine grids are examined and the importance of treating the fine/coarse grid inerface conservatively is discussed. Numerical results are presented.

  6. Automatic Target Recognition, Executive Summary and Annotated Brief (PR)

    DTIC Science & Technology

    2005-07-01

    development and the staring sensor called the “ Gotcha ” radar. The Advanced Recognition Capability (ARC) Lab is a critical node for the assessment...enthusiastically Innovative new concepts being addressed Creative mix of physics, phenomenology and M&S 3D Ladar development and GOTCHA staring radar...the UAV can autonomously taxi, take off, fly, remain on station capturing imagery, return and land. Gotcha Radar is a persistent radar with temporal

  7. A comparison of automatic and human speech recognition in null grammar.

    PubMed

    Juneja, Amit

    2012-03-01

    The accuracy of automatic speech recognition (ASR) systems is generally evaluated using corpora of grammatically sound read speech or natural spontaneous speech. This prohibits an accurate estimation of the performance of the acoustic modeling part of ASR because the language modeling performance is inherently integrated in the overall performance metric. In this work, ASR and human speech recognition (HSR) accuracies are compared for null grammar sentences in different signal-to-noise ratios and vocabulary sizes-1000, 2000, 4000, and 8000. The results shed light on differences between ASR and HSR in relative significance of bottom-up word recognition and context awareness.

  8. Photon counting passive 3D image sensing for automatic target recognition.

    PubMed

    Yeom, Seokwon; Javidi, Bahram; Watson, Edward

    2005-11-14

    In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.

  9. 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,…

  10. 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…

  11. 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…

  12. 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…

  13. Automatic Speech Recognition Technology as an Effective Means for Teaching Pronunciation

    ERIC Educational Resources Information Center

    Elimat, Amal Khalil; AbuSeileek, Ali Farhan

    2014-01-01

    This study aimed to explore the effect of using automatic speech recognition technology (ASR) on the third grade EFL students' performance in pronunciation, whether teaching pronunciation through ASR is better than regular instruction, and the most effective teaching technique (individual work, pair work, or group work) in teaching pronunciation…

  14. Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR)

    DTIC Science & Technology

    2005-10-01

    systems employing synthetic aperture radar . This report summarizes the major technical accomplishments that were realized. We developed a set of...automatic target recognition, ATR performance prediction, synthetic aperture radar 16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON...Std. Z39-18 Contents 1 INTRODUCTION 1 2 ATTRIBUTED SCATTERING MODELS FOR SYNTHETIC APER- TURE RADAR 6 2.1 Introduction

  15. 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…

  16. 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.

  17. Noise Adaptive Stream Weighting in Audio-Visual Speech Recognition

    NASA Astrophysics Data System (ADS)

    Heckmann, Martin; Berthommier, Frédéric; Kroschel, Kristian

    2002-12-01

    It has been shown that integration of acoustic and visual information especially in noisy conditions yields improved speech recognition results. This raises the question of how to weight the two modalities in different noise conditions. Throughout this paper we develop a weighting process adaptive to various background noise situations. In the presented recognition system, audio and video data are combined following a Separate Integration (SI) architecture. A hybrid Artificial Neural Network/Hidden Markov Model (ANN/HMM) system is used for the experiments. The neural networks were in all cases trained on clean data. Firstly, we evaluate the performance of different weighting schemes in a manually controlled recognition task with different types of noise. Next, we compare different criteria to estimate the reliability of the audio stream. Based on this, a mapping between the measurements and the free parameter of the fusion process is derived and its applicability is demonstrated. Finally, the possibilities and limitations of adaptive weighting are compared and discussed.

  18. Knowledge-based automatic recognition technology of radome from infrared images

    NASA Astrophysics Data System (ADS)

    Wang, Xiao-jian; Ma, Ling; Fang, Xiao; Chen, Lei; Lu, Hong-bin

    2009-07-01

    In this paper, a kind of knowledge-based automatic target recognition (ATR) technology of radome from infrared image is studied. The circular imaging of radome is used as the characteristic distinguished from background to realize target recognition. For the characteristic of low contrast of infrared image, brightness transformation is used to preliminarily enhance the contrast of the original image. In the light of the fact that target background outline statistically takes on vertical and horizontal directivity, a kind of revised Sobel operator with direction of 45°and 135°is adopted to detect edge feature so that background noise is effectively suppressed. To reduce the error ratio of target recognition from single frame image, the method to inspect the relativity of target recognition results of successive frames is adopted. The performance of the algorithm is tested using actually taken infrared radome images, and the right recognition ratio is around 90%, which turns out that this technology is feasible.

  19. Automatic recognition of biological shapes using the Hotelling transform.

    PubMed

    Sanchez-Marin, F J

    2001-03-01

    We present two contour-based techniques, for computerized object recognition that avoid the difficulties due to translations, rotations and scaling. Our techniques do not require any shape representation. The first technique uses the scale-space filtered coordinate functions of contours and their "largest diameters". The second, uses the Hotelling transform of the vector representations of the points of contours. We applied both techniques to recognize human corneal endothelial cells, embedded in a sample of tissue. The results obtained using the Hotelling transform represent a considerable improvement when compared to those previously obtained using the coordinate functions, the curvature function and Fourier descriptors as representations of shape.

  20. 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%.

  1. 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.

  2. Using Automatic Speech Recognition to Assess Spoken Responses to Cognitive Tests of Semantic Verbal Fluency.

    PubMed

    Pakhomov, Serguei V S; Marino, Susan E; Banks, Sarah; Bernick, Charles

    2015-12-01

    Cognitive tests of verbal fluency (VF) consist of verbalizing as many words as possible in one minute that either start with a specific letter of the alphabet or belong to a specific semantic category. These tests are widely used in neurological, psychiatric, mental health, and school settings and their validity for clinical applications has been extensively demonstrated. However, VF tests are currently administered and scored manually making them too cumbersome to use, particularly for longitudinal cognitive monitoring in large populations. The objective of the current study was to determine if automatic speech recognition (ASR) could be used for computerized administration and scoring of VF tests. We examined established techniques for constraining language modeling to a predefined vocabulary from a specific semantic category (e.g., animals). We also experimented with post-processing ASR output with confidence scoring, as well as with using speaker adaptation to improve automated VF scoring. Audio responses to a VF task were collected from 38 novice and experienced professional fighters (boxing and mixed martial arts) participating in a longitudinal study of effects of repetitive head trauma on brain function. Word error rate, correlation with manual word count and distance from manual word count were used to compare ASR-based approaches to scoring to each other and to the manually scored reference standard. Our study's results show that responses to the VF task contain a large number of extraneous utterances and noise that lead to relatively poor baseline ASR performance. However, we also found that speaker adaptation combined with confidence scoring significantly improves all three metrics and can enable use of ASR for reliable estimates of the traditional manual VF scores.

  3. Using Automatic Speech Recognition to Assess Spoken Responses to Cognitive Tests of Semantic Verbal Fluency

    PubMed Central

    Pakhomov, Serguei V.S.; Marino, Susan E.; Banks, Sarah; Bernick, Charles

    2015-01-01

    Cognitive tests of verbal fluency (VF) consist of verbalizing as many words as possible in one minute that either start with a specific letter of the alphabet or belong to a specific semantic category. These tests are widely used in neurological, psychiatric, mental health, and school settings and their validity for clinical applications has been extensively demonstrated. However, VF tests are currently administered and scored manually making them too cumbersome to use, particularly for longitudinal cognitive monitoring in large populations. The objective of the current study was to determine if automatic speech recognition (ASR) could be used for computerized administration and scoring of VF tests. We examined established techniques for constraining language modeling to a predefined vocabulary from a specific semantic category (e.g., animals). We also experimented with post-processing ASR output with confidence scoring, as well as with using speaker adaptation to improve automated VF scoring. Audio responses to a VF task were collected from 38 novice and experienced professional fighters (boxing and mixed martial arts) participating in a longitudinal study of effects of repetitive head trauma on brain function. Word error rate, correlation with manual word count and distance from manual word count were used to compare ASR-based approaches to scoring to each other and to the manually scored reference standard. Our study's results show that responses to the VF task contain a large number of extraneous utterances and noise that lead to relatively poor baseline ASR performance. However, we also found that speaker adaptation combined with confidence scoring significantly improves all three metrics and can enable use of ASR for reliable estimates of the traditional manual VF scores. PMID:26622073

  4. Adaptive feature-specific imaging: a face recognition example.

    PubMed

    Baheti, Pawan K; Neifeld, Mark A

    2008-04-01

    We present an adaptive feature-specific imaging (AFSI) system and consider its application to a face recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. Using sequential hypothesis testing, we compare AFSI with static-FSI (SFSI) and static or adaptive conventional imaging in terms of the number of measurements required to achieve a specified probability of misclassification (Pe). The AFSI system exhibits significant improvement compared to SFSI and conventional imaging at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses and desired Pe=10(-2), AFSI requires 100 times fewer measurements than the adaptive conventional imager at SNR= -20 dB. We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage, resulting in an optimal value of integration time (equivalent to SNR) per measurement.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. Underwater color constancy: enhancement of automatic live fish recognition

    NASA Astrophysics Data System (ADS)

    Chambah, Majed; Semani, Dahbia; Renouf, Arnaud; Courtellemont, Pierre; Rizzi, Alessandro

    2003-12-01

    We present in this paper some advances in color restoration of underwater images, especially with regard to the strong and non uniform color cast which is typical of underwater images. The proposed color correction method is based on ACE model, an unsupervised color equalization algorithm. ACE is a perceptual approach inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. A perceptual approach presents a lot of advantages: it is unsupervised, robust and has local filtering properties, that lead to more effective results. The restored images give better results when displayed or processed (fish segmentation and feature extraction). The presented preliminary results are satisfying and promising.

  10. Towards automatic recognition of scientifically rigorous clinical research evidence.

    PubMed

    Kilicoglu, Halil; Demner-Fushman, Dina; Rindflesch, Thomas C; Wilczynski, Nancy L; Haynes, R Brian

    2009-01-01

    The growing numbers of topically relevant biomedical publications readily available due to advances in document retrieval methods pose a challenge to clinicians practicing evidence-based medicine. It is increasingly time consuming to acquire and critically appraise the available evidence. This problem could be addressed in part if methods were available to automatically recognize rigorous studies immediately applicable in a specific clinical situation. We approach the problem of recognizing studies containing useable clinical advice from retrieved topically relevant articles as a binary classification problem. The gold standard used in the development of PubMed clinical query filters forms the basis of our approach. We identify scientifically rigorous studies using supervised machine learning techniques (Naïve Bayes, support vector machine (SVM), and boosting) trained on high-level semantic features. We combine these methods using an ensemble learning method (stacking). The performance of learning methods is evaluated using precision, recall and F(1) score, in addition to area under the receiver operating characteristic (ROC) curve (AUC). Using a training set of 10,000 manually annotated MEDLINE citations, and a test set of an additional 2,000 citations, we achieve 73.7% precision and 61.5% recall in identifying rigorous, clinically relevant studies, with stacking over five feature-classifier combinations and 82.5% precision and 84.3% recall in recognizing rigorous studies with treatment focus using stacking over word + metadata feature vector. Our results demonstrate that a high quality gold standard and advanced classification methods can help clinicians acquire best evidence from the medical literature.

  11. A Full-Body Layered Deformable Model for Automatic Model-Based Gait Recognition

    NASA Astrophysics Data System (ADS)

    Lu, Haiping; Plataniotis, Konstantinos N.; Venetsanopoulos, Anastasios N.

    2007-12-01

    This paper proposes a full-body layered deformable model (LDM) inspired by manually labeled silhouettes for automatic model-based gait recognition from part-level gait dynamics in monocular video sequences. The LDM is defined for the fronto-parallel gait with 22 parameters describing the human body part shapes (widths and lengths) and dynamics (positions and orientations). There are four layers in the LDM and the limbs are deformable. Algorithms for LDM-based human body pose recovery are then developed to estimate the LDM parameters from both manually labeled and automatically extracted silhouettes, where the automatic silhouette extraction is through a coarse-to-fine localization and extraction procedure. The estimated LDM parameters are used for model-based gait recognition by employing the dynamic time warping for matching and adopting the combination scheme in AdaBoost.M2. While the existing model-based gait recognition approaches focus primarily on the lower limbs, the estimated LDM parameters enable us to study full-body model-based gait recognition by utilizing the dynamics of the upper limbs, the shoulders and the head as well. In the experiments, the LDM-based gait recognition is tested on gait sequences with differences in shoe-type, surface, carrying condition and time. The results demonstrate that the recognition performance benefits from not only the lower limb dynamics, but also the dynamics of the upper limbs, the shoulders and the head. In addition, the LDM can serve as an analysis tool for studying factors affecting the gait under various conditions.

  12. Automatic Mexican sign language and digits recognition using normalized central moments

    NASA Astrophysics Data System (ADS)

    Solís, Francisco; Martínez, David; Espinosa, Oscar; Toxqui, Carina

    2016-09-01

    This work presents a framework for automatic Mexican sign language and digits recognition based on computer vision system using normalized central moments and artificial neural networks. Images are captured by digital IP camera, four LED reflectors and a green background in order to reduce computational costs and prevent the use of special gloves. 42 normalized central moments are computed per frame and used in a Multi-Layer Perceptron to recognize each database. Four versions per sign and digit were used in training phase. 93% and 95% of recognition rates were achieved for Mexican sign language and digits respectively.

  13. Vocabulary and Environment Adaptation in Vocabulary-Independent Speech Recognition

    DTIC Science & Technology

    1992-01-01

    normalization (ISDCN) proposed by Acero et al. [2] for microphone adaptation are incorporated into the our VI system to achieve environmental...reverberation from surface reflec- tions, etc. Acero at al. [1,2] proposed a series of environment normal- ization algorithms based on joint...support. References [1] Acero , A. Acoustical and Environmental Robustness in Auto- matic Speech Recognition. Department of Electrical Engineer- ing

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Health smart home for elders - a tool for automatic recognition of activities of daily living.

    PubMed

    Le, Xuan Hoa Binh; Di Mascolo, Maria; Gouin, Alexia; Noury, Norbert

    2008-01-01

    Elders live preferently in their own home, but with aging comes the loss of autonomy and associated risks. In order to help them live longer in safe conditions, we need a tool to automatically detect their loss of autonomy by assessing the degree of performance of activities of daily living. This article presents an approach enabling the activities recognition of an elder living alone in a home equipped with noninvasive sensors.

  19. Automatic Recognition of Phonemes Using a Syntactic Processor for Error Correction.

    DTIC Science & Technology

    1980-12-01

    OF PHONEMES USING A SYNTACTIC PROCESSOR FOR ERROR CORRECTION THESIS AFIT/GE/EE/8D-45 Robert B. ’Taylor 2Lt USAF Approved for public release...distribution unlimilted. AbP AFIT/GE/EE/ 80D-45 AUTOMATIC RECOGNITION OF PHONEMES USING A SYNTACTIC PROCESSOR FOR ERROR CORRECTION THESIS Presented to the...Testing ..................... 37 Bayes Decision Rule for Minimum Error ........... 37 Bayes Decision Rule for Minimum Risk ............ 39 Mini Max Test

  20. Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes.

    PubMed

    Meyer, Bernd T; Brand, Thomas; Kollmeier, Birger

    2011-01-01

    The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.

  1. 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.

  2. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather

    DTIC Science & Technology

    2013-05-01

    Aschwanden, M. J. 2005, Physics of the Solar Corona . An Introduction with Problems and Solutions (2nd edition), ed. Aschwanden, M. J. Balasubramaniam, K...AFRL-OSR-VA-TR-2013-0020 Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity...Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather 5a. CONTRACT NUMBER FA9550-09

  3. 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.

  4. An Adaptive Feature Extractor for Gesture SEMG Recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Xu; Chen, Xiang; Zhao, Zhang-Yan; Li, Qiang; Yang, Ji-Hai; Lantz, Vuokko; Wang, Kong-Qiao

    This paper proposes an adaptive feature extraction method for pattern recognition of hand gesture action sEMG to enhance the reusability of myoelectric control. The feature extractor is based on wavelet packet transform and Local Discriminant Basis (LDB) algorithms to select several optimized decomposition subspaces of origin SEMG waveforms caused by hand gesture motions. Then the square roots of mean energy of signal in those subspaces are calculated to form the feature vector. In data acquisition experiments, five healthy subjects implement six kinds of hand motions every day for a week. The recognition results of hand gesture on the basis of the measured SEMG signals from different use sessions demonstrate that the feature extractor is effective. Our work is valuable for the realization of myoelectric control system in rehabilitation and other medical applications.

  5. Enhanced synthetic aperture radar automatic target recognition method based on novel features.

    PubMed

    Ning, Chen; Liu, Wenbo; Zhang, Gong; Yin, Jiejun; Ji, Xiuxia

    2016-11-01

    This paper proposes a set of uncommonly rich feature representations for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. The proposed novel feature representations capture both the spatial and spectral properties of a target in a unified framework, while simultaneously offering discrimination and robustness to aspect variations. Specifically, the proposed features are mainly derived from the ideas of the monogenic signal and polar mapping. The applicability of the monogenic signal within the field of SAR target recognition is demonstrated by its capability of capturing both the broad spectral information and spatial localization with compact support. Further, to reduce the influence of inevitable variations due to aspect changes in SAR images, the monogenic components are transformed from Cartesian to polar coordinates through polar mapping. Additionally, a new target-shadow feature is also presented to compensate for the important discriminative information about target geometry, which exists in the shadow area. Finally, the proposed features are jointly considered into a unified multiple kernel learning framework for target recognition. Experiments on the moving and stationary target acquisition and recognition (MSTAR) public dataset demonstrate the strength and applicability of the proposed representations to SAR ATR. Moreover, it is also shown that overall high recognition accuracy can be obtained by the established unified framework.

  6. 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

  7. 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.

  8. 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.

  9. 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%.

  10. The use of automatic speech recognition showing the influence of nasality on speech intelligibility.

    PubMed

    Mayr, S; Burkhardt, K; Schuster, M; Rogler, K; Maier, A; Iro, H

    2010-11-01

    Altered nasality influences speech intelligibility. Automatic speech recognition (ASR) has proved suitable for quantifying speech intelligibility in patients with different degrees of nasal emissions. We investigated the influence of hyponasality on the results of speech recognition before and after nasal surgery using ASR. Speech recordings, nasal peak inspiratory flow and self-perception measurements were carried out in 20 German-speaking patients (8 women, 12 men; aged 38 ± 22 years) who underwent surgery for various nasal and sinus pathologies. The degree of speech intelligibility was quantified as the percentage of correctly recognized words of a standardized word chain by ASR (word recognition rate; WR). WR was measured 1 day before (t1), 1 day after with nasal packings (t2), and 3 months after (t3) surgery; nasal peak flow on t1 and t3. WR was calculated with program for the automatic evaluation of all kinds of speech disorders (PEAKS). WR as a parameter of speech intelligibility was significantly decreased immediately after surgery (t1 vs. t2 p < 0.01) but increased 3 months after surgery (t2 vs. t3 p < 0.01). WR showed no association with age or gender. There was no significant difference between WR at t1 and t3, despite a post-operative increase in nasal peak inspiratory flow measurements. The results show that ASR is capable of quantifying the influence of hyponasality on speech; nasal obstruction leads to significantly reduced WR and nasal peak flow cannot replace evaluation of nasality.

  11. A simulation-based approach towards automatic target recognition of high resolution space borne radar signatures

    NASA Astrophysics Data System (ADS)

    Anglberger, H.; Kempf, T.

    2016-10-01

    Specific imaging effects that are caused mainly by the range measurement principle of a radar device, its much lower frequency range as compared to the optical spectrum, the slanted imaging geometry and certainly the limited spatial resolution complicates the interpretation of radar signatures decisively. Especially the coherent image formation which causes unwanted speckle noise aggravates the problem of visually recognizing target objects. Fully automatic approaches with acceptable false alarm rates are therefore an even harder challenge. At the Microwaves and Radar Institute of the German Aerospace Center (DLR) the development of methods to implement a robust overall processing workflow for automatic target recognition (ATR) out of high resolution synthetic aperture radar (SAR) image data is under progress. The heart of the general approach is to use time series exploitation for the former detection step and simulation-based signature matching for the subsequent recognition. This paper will show the overall ATR chain as a proof of concept for the special case of airplane recognition on image data from the space borne SAR sensor TerraSAR-X.

  12. Automatic speech recognition in cocktail-party situations: a specific training for separated speech.

    PubMed

    Marti, Amparo; Cobos, Maximo; Lopez, Jose J

    2012-02-01

    Automatic speech recognition (ASR) refers to the task of extracting a transcription of the linguistic content of an acoustical speech signal automatically. Despite several decades of research in this important area of acoustic signal processing, the accuracy of ASR systems is still far behind human performance, especially in adverse acoustic scenarios. In this context, one of the most challenging situations is the one concerning simultaneous speech in cocktail-party environments. Although source separation methods have already been investigated to deal with this problem, the separation process is not perfect and the resulting artifacts pose an additional problem to ASR performance. In this paper, a specific training to improve the percentage of recognized words in real simultaneous speech cases is proposed. The combination of source separation and this specific training is explored and evaluated under different acoustical conditions, leading to improvements of up to a 35% in ASR performance.

  13. 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.

  14. Evolution of adaptive immune recognition in jawless vertebrates.

    PubMed

    Saha, Nil Ratan; Smith, Jeramiah; Amemiya, Chris T

    2010-02-01

    All extant vertebrates possess an adaptive immune system wherein diverse immune receptors are created and deployed in specialized blood cell lineages. Recent advances in DNA sequencing and developmental resources for basal vertebrates have facilitated numerous comparative analyses that have shed new light on the molecular and cellular bases of immune defense and the mechanisms of immune receptor diversification in the "jawless" vertebrates. With data from these key species in hand, it is becoming possible to infer some general aspects of the early evolution of vertebrate adaptive immunity. All jawed vertebrates assemble their antigen-receptor genes through combinatorial recombination of different "diversity" segments into immunoglobulin or T-cell receptor genes. However, the jawless vertebrates employ an analogous, but independently derived set of immune receptors in order to recognize and bind antigens: the variable lymphocyte receptors (VLRs). The means by which this locus generates receptor diversity and achieves antigen specificity is of considerable interest because these mechanisms represent a completely independent strategy for building a large immune repertoire. Therefore, studies of the VLR system are providing insight into the fundamental principles and evolutionary potential of adaptive immune recognition systems. Here we review and synthesize the wealth of data that have been generated towards understanding the evolution of the adaptive immune system in the jawless vertebrates.

  15. Unsupervised Adaptation of Categorical Prosody Models for Prosody Labeling and Speech Recognition

    PubMed Central

    Ananthakrishnan, Sankaranarayanan; Narayanan, Shrikanth

    2009-01-01

    Automatic speech recognition (ASR) systems rely almost exclusively on short-term segment-level features (MFCCs), while ignoring higher level suprasegmental cues that are characteristic of human speech. However, recent experiments have shown that categorical representations of prosody, such as those based on the Tones and Break Indices (ToBI) annotation standard, can be used to enhance speech recognizers. However, categorical prosody models are severely limited in scope and coverage due to the lack of large corpora annotated with the relevant prosodic symbols (such as pitch accent, word prominence, and boundary tone labels). In this paper, we first present an architecture for augmenting a standard ASR with symbolic prosody. We then discuss two novel, un-supervised adaptation techniques for improving, respectively, the quality of the linguistic and acoustic components of our categorical prosody models. Finally, we implement the augmented ASR by enriching ASR lattices with the adapted categorical prosody models. Our experiments show that the proposed unsupervised adaptation techniques significantly improve the quality of the prosody models; the adapted prosodic language and acoustic models reduce binary pitch accent (presence versus absence) classification error rate by 13.8% and 4.3%, respectively (relative to the seed models) on the Boston University Radio News Corpus, while the prosody-enriched ASR exhibits a 3.1% relative reduction in word error rate (WER) over the baseline system. PMID:19763253

  16. 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.

  17. Automatic speech recognition using articulatory features from subject-independent acoustic-to-articulatory inversion.

    PubMed

    Ghosh, Prasanta Kumar; Narayanan, Shrikanth

    2011-10-01

    An automatic speech recognition approach is presented which uses articulatory features estimated by a subject-independent acoustic-to-articulatory inversion. The inversion allows estimation of articulatory features from any talker's speech acoustics using only an exemplary subject's articulatory-to-acoustic map. Results are reported on a broad class phonetic classification experiment on speech from English talkers using data from three distinct English talkers as exemplars for inversion. Results indicate that the inclusion of the articulatory information improves classification accuracy but the improvement is more significant when the speaking style of the exemplar and the talker are matched compared to when they are mismatched.

  18. A hierarchical classifier using new support vector machines for automatic target recognition.

    PubMed

    Casasent, David; Wang, Yu-Chiang

    2005-01-01

    A binary hierarchical classifier is proposed for automatic target recognition. We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is used at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform (|FT|) features, which provide shift-invariance, initial test results on infra-red (IR) data are excellent.

  19. A system for large-scale automatic traffic sign recognition and mapping

    NASA Astrophysics Data System (ADS)

    Chigorin, A.; Konushin, A.

    2013-10-01

    We present a system for the large-scale automatic traffic signs recognition and mapping and experimentally justify design choices made for different components of the system. Our system works with more than 140 different classes of traffic signs and does not require labor-intensive labelling of a large amount of training data due to the training on synthetically generated images. We evaluated our system on the large dataset of Russian traffic signs and made this dataset publically available to encourage future comparison.

  20. Automatic modulation format recognition for the next generation optical communication networks using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Guesmi, Latifa; Hraghi, Abir; Menif, Mourad

    2015-03-01

    A new technique for Automatic Modulation Format Recognition (AMFR) in next generation optical communication networks is presented. This technique uses the Artificial Neural Network (ANN) in conjunction with the features of Linear Optical Sampling (LOS) of the detected signal at high bit rates using direct detection or coherent detection. The use of LOS method for this purpose mainly driven by the increase of bit rates which enables the measurement of eye diagrams. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of -500 to 500 ps/nm, differential group delay (DGD) in the range of 0-15 ps and the optical signal tonoise ratio (OSNR) in the range of 10-30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.8%.

  1. Integrated approach for automatic target recognition using a network of collaborative sensors.

    PubMed

    Mahalanobis, Abhijit; Van Nevel, Alan

    2006-10-01

    We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.

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

    PubMed

    Narayanan, Arun; Wang, DeLiang

    2013-05-01

    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.

  3. Hybrid neuro-fuzzy approach for automatic vehicle license plate recognition

    NASA Astrophysics Data System (ADS)

    Lee, Hsi-Chieh; Jong, Chung-Shi

    1998-03-01

    Most currently available vehicle identification systems use techniques such as R.F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory system. It is considered expensive to install a transponder in each vehicle and the malfunction of the transponder will result in the failure of the vehicle identification system. In this study, novel hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for the SimNet neuro-fuzzy system. To test the performance of the proposed system, three hundred and eighty vehicle image samples are taken by a digital camera. The license plate recognition success rate of the prototype is approximately 91% while the character recognition success rate of the prototype is approximately 97%.

  4. Automatic recognition of conceptualization zones in scientific articles and two life science applications

    PubMed Central

    Liakata, Maria; Saha, Shyamasree; Dobnik, Simon; Batchelor, Colin; Rebholz-Schuhmann, Dietrich

    2012-01-01

    Motivation: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. Results: We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with ‘Experiment’, ‘Background’ and ‘Model’ being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness

  5. Automatic target classification of slow moving ground targets using space-time adaptive processing

    NASA Astrophysics Data System (ADS)

    Malas, John Alexander

    2002-04-01

    Air-to-ground surveillance radar technologies are increasingly being used by theater commanders to detect, track, and identify ground moving targets. New radar automatic target recognition (ATR) technologies are being developed to aid the pilot in assessing the ground combat picture. Most air-to-ground surveillance radars use Doppler filtering techniques to separate target returns from ground clutter. Unfortunately, Doppler filter techniques fall short on performance when target geometry and ground vehicle speed result in low line of sight velocities. New clutter filter techniques compatible with emerging advancements in wideband radar operation are needed to support surveillance modes of radar operation when targets enter this low velocity regime. In this context, space-time adaptive processing (STAP) in conjunction with other algorithms offers a class of signal processing that provide improved target detection, tracking, and classification in the presence of interference through the adaptive nulling of both ground clutter and/or jamming. Of particular interest is the ability of the radar to filter and process the complex target signature data needed to generate high range resolution (HRR) signature profiles on ground targets. A new approach is proposed which will allow air-to-ground target classification of slow moving vehicles in clutter. A wideband STAP approach for clutter suppression is developed which preserves the amplitude integrity of returns from multiple range bins consistent with the HRR ATR approach. The wideband STAP processor utilizes narrowband STAP principles to generate a series of adaptive sub-band filters. Each sub-band filter output is used to construct the complete filtered response of the ground target. The performance of this new approach is demonstrated and quantified through the implementation of a one dimensional template-based minimum mean squared error classifier. Successful minimum velocity identification is defined in terms of

  6. 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...

  7. 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.

  8. 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.

  9. 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.

  10. Phonological priming in auditory word recognition: when both controlled and automatic processes are responsible for the effects.

    PubMed

    Dufour, Sophie

    2008-03-01

    The phonological priming paradigm provides an interesting methodological tool for studying various components of the speech recognition process. However, concerns about response biases distorting the effects have been repeatedly voiced. This article reviews the main studies on priming and aims to distinguish effects under automatic processes from those under some level of strategic control. Both controlled and automatic processes appear to be responsible for the effects observed in phonological priming experiments. Nonetheless, with careful procedures, it is possible to separate them.

  11. Acoustic sleepiness detection: framework and validation of a speech-adapted pattern recognition approach.

    PubMed

    Krajewski, Jarek; Batliner, Anton; Golz, Martin

    2009-08-01

    This article describes a general framework for detecting sleepiness states on the basis of prosody, articulation, and speech-quality-related speech characteristics. The advantages of this automatic real-time approach are that obtaining speech data is nonobstrusive and is free from sensor application and calibration efforts. Different types of acoustic features derived from speech, speaker, and emotion recognition were employed (frame-level-based speech features). Combing these features with high-level contour descriptors, which capture the temporal information of frame-level descriptor contours, results in 45,088 features per speech sample. In general, the measurement process follows the speech-adapted steps of pattern recognition: (1) recording speech, (2) preprocessing, (3) feature computation (using perceptual and signal-processing-related features such as, e.g., fundamental frequency, intensity, pause patterns, formants, and cepstral coefficients), (4) dimensionality reduction, (5) classification, and (6) evaluation. After a correlation-filter-based feature subset selection employed on the feature space in order to find most relevant features, different classification models were trained. The best model-namely, the support-vector machine-achieved 86.1% classification accuracy in predicting sleepiness in a sleep deprivation study (two-class problem, N=12; 01.00-08.00 a.m.).

  12. 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.

  13. 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.

  14. The relationship between perceptual disturbances in dysarthric speech and automatic speech recognition performance.

    PubMed

    Tu, Ming; Wisler, Alan; Berisha, Visar; Liss, Julie M

    2016-11-01

    State-of-the-art automatic speech recognition (ASR) engines perform well on healthy speech; however recent studies show that their performance on dysarthric speech is highly variable. This is because of the acoustic variability associated with the different dysarthria subtypes. This paper aims to develop a better understanding of how perceptual disturbances in dysarthric speech relate to ASR performance. Accurate ratings of a representative set of 32 dysarthric speakers along different perceptual dimensions are obtained and the performance of a representative ASR algorithm on the same set of speakers is analyzed. This work explores the relationship between these ratings and ASR performance and reveals that ASR performance can be predicted from perceptual disturbances in dysarthric speech with articulatory precision contributing the most to the prediction followed by prosody.

  15. Automatic and real time recognition of microalgae by means of pigment signature and shape.

    PubMed

    Coltelli, Primo; Barsanti, Laura; Evangelista, Valtere; Frassanito, Anna Maria; Passarelli, Vincenzo; Gualtieri, Paolo

    2013-07-01

    Microalgae are unicellular photoautotrophic organisms that grow in any habitat such as fresh and salt water bodies, hot springs, ice, air, and in or on other organisms and substrates. Massive growth of microalgae may produce harmful effects on the marine and freshwater ecological environment and fishery resources. Therefore, rapid and accurate recognition and classification of microalgae is one of the most important issues in water resource management. In this paper, a new methodology for automatic and real time identification of microalgae by means of microscopy image analysis is presented. This methodology is based on segmentation, shape features extraction, and characteristic colour (i.e. pigment signature) determination. A classifier algorithm based on the minimum distance criterion was used for microalgae grouping according to the measured features. 96.6% accuracy from a set of 3423 images of 24 different microalgae representing the major algal phyla was achieved by this methodology.

  16. Clutter metric based on the Cramer-Rao lower bound on automatic target recognition.

    PubMed

    He, Guojing; Zhang, Jianqi; Liu, Delian; Chang, Honghua

    2008-10-10

    This is a performance evaluation on the implementation of the Cramer-Rao lower bound (CRLB) for background clutter measurement on automatic target recognition (ATR). In essence the background clutter evaluation problem for ATR is consistent with the deterministic parameter estimation problem. Thus, useful concepts and theories of deterministic parameter estimation can be introduced into the investigation of background clutter. In this paper, the CRLB is employed as a metric for clutter measurement. Requirements needed for this application are analyzed, and the approach for obtaining the CRLB of a scene image is produced. The flexibility of the CRLB metric is analyzed. Discussion and comparison are made on the relationship between the CRLB metric and the Sims signal-to-clutter metric. Finally, we illustrate how this metric defines the potential for false alarms by determining the correspondence level between a target and background through the application of the CRLB.

  17. 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…

  18. 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.

  19. An automatic recognition and parameter extraction method for structural planes in borehole image

    NASA Astrophysics Data System (ADS)

    Wang, Chuanying; Zou, Xianjian; Han, Zengqiang; Wang, Yiteng; Wang, Jinchao

    2016-12-01

    As a breakthrough in borehole imaging technology, digital panoramic borehole camera technology has been widely employed. The high-resolution panoramic borehole images can accurately reproduce the geometric features of structural planes. However, the detection of these features is usually done manually, which is both time-consuming and introduces human errors. To solve this problem, this paper presents a method for the automatic recognition and parameter extraction of borehole geometric features of camera images. In this method, the image's gray and gradient level, and also their projection on the depth axis are used to identify the locations of structural planes. Afterwards, iterative matching is employed by using a template of sinusoidal function to search for structural planes in the identified image blocks. Finally, optimal sine curves are selected as the feature curves of structural planes, and their related parameters are converted into structural plane parameters required for engineering, such as their positions, dip directions, dip angles and fracture widths. The method can automatically identify all of structural planes throughout the whole borehole camera image in a continuous and rapid manner, and obtain the corresponding structural parameters. It has proven highly reliable, accurate and efficient.

  20. An automatic and user-driven training method for locomotion mode recognition for artificial leg control.

    PubMed

    Zhang, Xiaorong; Wang, Ding; Yang, Qing; Huang, He

    2012-01-01

    Our previously developed locomotion-mode-recognition (LMR) system has provided a great promise to intuitive control of powered artificial legs. However, the lack of fast, practical training methods is a barrier for clinical use of our LMR system for prosthetic legs. This paper aims to design a new, automatic, and user-driven training method for practical use of LMR system. In this method, a wearable terrain detection interface based on a portable laser distance sensor and an inertial measurement unit (IMU) is applied to detect the terrain change in front of the prosthesis user. The mechanical measurement from the prosthetic pylon is used to detect gait phase. These two streams of information are used to automatically identify the transitions among various locomotion modes, switch the prosthesis control mode, and label the training data with movement class and gait phase in real-time. No external device is required in this training system. In addition, the prosthesis user without assistance from any other experts can do the whole training procedure. The pilot experimental results on an able-bodied subject have demonstrated that our developed new method is accurate and user-friendly, and can significantly simplify the LMR training system and training procedure without sacrificing the system performance. The novel design paves the way for clinical use of our designed LMR system for powered lower limb prosthesis control.

  1. 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

  2. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

    PubMed

    Abbas, Qaisar; Fondon, Irene; Sarmiento, Auxiliadora; Jiménez, Soledad; Alemany, Pedro

    2017-03-28

    Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge. In this article, a novel automatic recognition system for the five severity level of diabetic retinopathy (SLDR) is developed without performing any pre- and post-processing steps on retinal fundus images through learning of deep visual features (DVFs). These DVF features are extracted from each image by using color dense in scale-invariant and gradient location-orientation histogram techniques. To learn these DVF features, a semi-supervised multilayer deep-learning algorithm is utilized along with a new compressed layer and fine-tuning steps. This SLDR system was evaluated and compared with state-of-the-art techniques using the measures of sensitivity (SE), specificity (SP) and area under the receiving operating curves (AUC). On 750 fundus images (150 per category), the SE of 92.18%, SP of 94.50% and AUC of 0.924 values were obtained on average. These results demonstrate that the SLDR system is appropriate for early detection of DR and provide an effective treatment for prediction type of diabetes.

  3. Spectro-temporal modulation subspace-spanning filter bank features for robust automatic speech recognition.

    PubMed

    Schädler, Marc René; Meyer, Bernd T; Kollmeier, Birger

    2012-05-01

    In an attempt to increase the robustness of automatic speech recognition (ASR) systems, a feature extraction scheme is proposed that takes spectro-temporal modulation frequencies (MF) into account. This physiologically inspired approach uses a two-dimensional filter bank based on Gabor filters, which limits the redundant information between feature components, and also results in physically interpretable features. Robustness against extrinsic variation (different types of additive noise) and intrinsic variability (arising from changes in speaking rate, effort, and style) is quantified in a series of recognition experiments. The results are compared to reference ASR systems using Mel-frequency cepstral coefficients (MFCCs), MFCCs with cepstral mean subtraction (CMS) and RASTA-PLP features, respectively. Gabor features are shown to be more robust against extrinsic variation than the baseline systems without CMS, with relative improvements of 28% and 16% for two training conditions (using only clean training samples or a mixture of noisy and clean utterances, respectively). When used in a state-of-the-art system, improvements of 14% are observed when spectro-temporal features are concatenated with MFCCs, indicating the complementarity of those feature types. An analysis of the importance of specific MF shows that temporal MF up to 25 Hz and spectral MF up to 0.25 cycles/channel are beneficial for ASR.

  4. A compact methodology to understand, evaluate, and predict the performance of automatic target recognition.

    PubMed

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

    2014-06-25

    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.

  5. Automatic recognition of biological shapes with and without representations of shape.

    PubMed

    Sánchez-Marín, F J

    2000-02-01

    In this work it is described how to use the curvature function, the Fourier descriptors, and the coordinate functions of a contour to achieve automatic recognition of biological shapes. Those representations of shape and the coordinate functions were applied to recognize human corneal endothelial cells embedded in a sample of tissue. We assume that when the coordinates of the points of contours are analyzed directly, no representation of shape is being used. We applied scale-space filtering to the coordinate functions, to compensate the effects of scaling and to minimize the error due to quantization. A technique for compensating the effects of rotation, with or without the use of a representation of shape, is proposed. Our results show that, for a wide range of biological shapes, no representation of shape is required to solve or avoid the problems caused by translation, scaling, and rotation. We conclude that for certain applications the use of a representation of shape can provide some advantages. However, the coordinate functions of contours, evolved in scale-space, can be efficiently used, yielding even better results in applications of robotics and computer vision related to the recognition of biological shapes.

  6. 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…

  7. 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…

  8. Machines a Comprendre la Parole: Methodologie et Bilan de Recherche (Automatic Speech Recognition: Methodology and the State of the Research)

    ERIC Educational Resources Information Center

    Haton, Jean-Pierre

    1974-01-01

    Still no decisive result has been achieved in the automatic machine recognition of sentences of a natural language. Current research concentrates on developing algorithms for syntactic and semantic analysis. It is obvious that clues from all levels of perception have to be taken into account if a long term solution is ever to be found. (Author/MSE)

  9. Automatic Activation of Phonological Code during Visual Word Recognition in Children: A Masked Priming Study in Grades 3 and 5

    ERIC Educational Resources Information Center

    Sauval, Karinne; Perre, Laetitia; Casalis, Séverine

    2017-01-01

    The present study aimed to investigate the development of automatic phonological processes involved in visual word recognition during reading acquisition in French. A visual masked priming lexical decision experiment was carried out with third, fifth graders and adult skilled readers. Three different types of partial overlap between the prime and…

  10. The ARTEMIS Catalog of LASCO Coronal Mass Ejections. Automatic Recognition of Transient Events and Marseille Inventory from Synoptic maps

    NASA Astrophysics Data System (ADS)

    Boursier, Y.; Lamy, P.; Llebaria, A.; Goudail, F.; Robelus, S.

    2009-06-01

    The LASCO-C2 coronagraph aboard the SOHO solar observatory has been providing a continuous flow of coronal images since 1996. Synoptic maps for each Carrington rotation have been built from these images, and offer a global view of the temporal evolution of the solar corona, particularly the occurrence of transient events. Coronal Mass Ejections (CMEs) present distinct signatures thus offering a novel approach to the problem of their identification and characterization. We present in this article an automated method of detection based on their morphological appearance on synoptic maps. It is based on adaptive filtering and segmentation, followed by merging with high-level knowledge. The program builds a catalog which lists the CMEs detected for each Carrington Rotation, together with their main estimated parameters: time of appearance, position angle, angular extent, average velocity and intensity. Our final catalog LASCO-ARTEMIS (Automatic Recognition of Transient Events and Marseille Inventory from Synoptic maps) is compared with existing catalogs, CDAW, CACTUS and SEEDS. We find that, likewise the automated CACTUS and SEEDS catalogs, we detect many more events than the CDAW catalog which is based on visual detection. The total number of detected CMEs strongly depends upon the sensitivity to small, faint and numerous events.

  11. 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.

  12. 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…

  13. 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…

  14. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    NASA Astrophysics Data System (ADS)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  15. 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.

  16. 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

  17. A comparison of signal-processing front ends for automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Jankowski, C. R., Jr.; Vo, H.-D. H.; Lippmann, R. P.

    1994-07-01

    The first stage of any system for automatic speech recognition (ASR) is a signal-processing front end that converts a sampled speech waveform into a more suitable representation for later processing. Several front ends are compared, three of which are based on knowledge about the human auditory system. The performance of an ASR system with these front ends was compared to a control mel filter bank (MFB)-based cepstral representation in clean speech and with speech degraded by noise and spectral variability. Using the TI-105 isolated word data base, it was found that auditory front ends performed comparably to MFB cepstra, sometimes slightly better in noise. With MFB cepstral recognition error rates ranging from 0.5% to 26.9%, depending on signal-to-noise ratio (SNR), auditory models could perform as high as four percentage points better. With speech degraded by linear filtering, where MFB cepstra showed error rates ranging from 0.5% to 3.1%, auditory outputs could improve performance by as much as 0.4% for conditions with high baseline error rates. This performance gain comes at a significant computational expense-approximately one-third real time for MFB cepstra as opposed to as much as over 100 times real time for auditory models. These results disagree with previous studies that suggest considerably more improvement with auditory models. However, these earlier studies used a linear predictive coding (LPC)-based control front end, which is shown to perform significantly worse than MFB cepstra under noisy conditions (e.g., 2.7% error rate with mel-cepstra vs. 25.3% with LPC at 18-dB SNR). Data-reduction techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) were also evaluated. PCA provided no gain in noise and slight gain with spectral variability.

  18. Difficulties in automatic speech recognition of dysarthric speakers and implications for speech-based applications used by the elderly: a literature review.

    PubMed

    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 adults, in whom speech disorders are commonly present. As one ages, the older adult voice naturally begins to resemble some aspects of mildly dysarthric speech. Dysarthria, a common neuromotor speech disorder, is particularly useful for exploring performance limitations of automatic speech recognizers owing to its wide range of speech expression. This article reviews clinical research literature examining the use of commercial speech-to-text automatic speech recognition technology by individuals with dysarthria. The main factors limiting automatic speech recognition performance with dysarthric speakers are highlighted and extended to the elderly using a specific example of a novel, automated, speech-based personal emergency response system for older adults.

  19. Multi-modal automatic montaging of adaptive optics retinal images

    PubMed Central

    Chen, Min; Cooper, Robert F.; Han, Grace K.; Gee, James; Brainard, David H.; Morgan, Jessica I. W.

    2016-01-01

    We present a fully automated adaptive optics (AO) retinal image montaging algorithm using classic scale invariant feature transform with random sample consensus for outlier removal. Our approach is capable of using information from multiple AO modalities (confocal, split detection, and dark field) and can accurately detect discontinuities in the montage. The algorithm output is compared to manual montaging by evaluating the similarity of the overlapping regions after montaging, and calculating the detection rate of discontinuities in the montage. Our results show that the proposed algorithm has high alignment accuracy and a discontinuity detection rate that is comparable (and often superior) to manual montaging. In addition, we analyze and show the benefits of using multiple modalities in the montaging process. We provide the algorithm presented in this paper as open-source and freely available to download. PMID:28018714

  20. Multi-modal automatic montaging of adaptive optics retinal images.

    PubMed

    Chen, Min; Cooper, Robert F; Han, Grace K; Gee, James; Brainard, David H; Morgan, Jessica I W

    2016-12-01

    We present a fully automated adaptive optics (AO) retinal image montaging algorithm using classic scale invariant feature transform with random sample consensus for outlier removal. Our approach is capable of using information from multiple AO modalities (confocal, split detection, and dark field) and can accurately detect discontinuities in the montage. The algorithm output is compared to manual montaging by evaluating the similarity of the overlapping regions after montaging, and calculating the detection rate of discontinuities in the montage. Our results show that the proposed algorithm has high alignment accuracy and a discontinuity detection rate that is comparable (and often superior) to manual montaging. In addition, we analyze and show the benefits of using multiple modalities in the montaging process. We provide the algorithm presented in this paper as open-source and freely available to download.

  1. Pattern recognition with adaptive-thresholds for sleep spindle in high density EEG signals.

    PubMed

    Gemignani, Jessica; Agrimi, Jacopo; Cheli, Enrico; Gemignani, Angelo; Laurino, Marco; Allegrini, Paolo; Landi, Alberto; Menicucci, Danilo

    2015-01-01

    Medicine and Surgery, University of Pisa, via Savi 10, 56126, Pisa, Italy Sleep spindles are electroencephalographic oscillations peculiar of non-REM sleep, related to neuronal mechanisms underlying sleep restoration and learning consolidation. Based on their very singular morphology, sleep spindles can be visually recognized and detected, even though this approach can lead to significant mis-detections. For this reason, many efforts have been put in developing a reliable algorithm for spindle automatic detection, and a number of methods, based on different techniques, have been tested via visual validation. This work aims at improving current pattern recognition procedures for sleep spindles detection by taking into account their physiological sources of variability. We provide a method as a synthesis of the current state of art that, improving dynamic threshold adaptation, is able to follow modification of spindle characteristics as a function of sleep depth and inter-subjects variability. The algorithm has been applied to physiological data recorded by a high density EEG in order to perform a validation based on visual inspection and on evaluation of expected results from normal night sleep in healthy subjects.

  2. Diagnostic Assessment of Childhood Apraxia of Speech Using Automatic Speech Recognition (ASR) Methods.

    PubMed

    Hosom, John-Paul; Shriberg, Lawrence; Green, Jordan R

    2004-12-01

    We report findings from two feasibility studies using automatic speech recognition (ASR) methods in childhood speech sound disorders. The studies evaluated and implemented the automation of two recently proposed diagnostic markers for suspected Apraxia of Speech (AOS) termed the Lexical Stress Ratio (LSR) and the Coefficient of Variation Ratio (CVR). The LSR is a weighted composite of amplitude area, frequency area , and duration in the stressed compared to the unstressed vowel as obtained from a speaker's productions of eight trochaic word forms. Composite weightings for the three stress parameters were determined from a principal components analysis. The CVR expresses the average normalized variability of durations of pause and speech events that were obtained from a conversational speech sample. We describe the automation procedures used to obtain LSR and CVR scores for four children with suspected AOS and report comparative findings. The LSR values obtained with ASR were within 1.2% to 6.7% of the LSR values obtained manually using Computerized Speech Lab (CSL). The CVR values obtained with ASR were within 0.7% to 2.7% of the CVR values obtained manually using Matlab. These results indicate the potential of ASR-based techniques to process these and other diagnostic markers of childhood speech sound disorders.

  3. Automatic recognition of harmonic bird sounds using a frequency track extraction algorithm.

    PubMed

    Heller, Jason R; Pinezich, John D

    2008-09-01

    This paper demonstrates automatic recognition of vocalizations of four common bird species (herring gull [Larus argentatus], blue jay [Cyanocitta cristata], Canada goose [Branta canadensis], and American crow [Corvus brachyrhynchos]) using an algorithm that extracts frequency track sets using track properties of importance and harmonic correlation. The main result is that a complex harmonic vocalization is rendered into a set of related tracks that is easily applied to statistical models of the actual bird vocalizations. For each vocalization type, a statistical model of the vocalization was created by transforming the training set frequency tracks into feature vectors. The extraction algorithm extracts sets of frequency tracks from test recordings that closely approximate harmonic sounds in the file being processed. Each extracted set in its final form is then compared with the statistical models generated during the training phase using Mahalanobis distance functions. If it matches one of the models closely, the recognizer declares the set an occurrence of the corresponding vocalization. The method was evaluated against a test set containing vocalizations of both the 4 target species and 16 additional species as well as background noise containing planes, cars, and various natural sounds.

  4. Improved local ternary patterns for automatic target recognition in infrared imagery.

    PubMed

    Wu, Xiaosheng; Sun, Junding; Fan, Guoliang; Wang, Zhiheng

    2015-03-16

    This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods.

  5. Interim results from a neural network 3-D automatic target recognition program

    NASA Astrophysics Data System (ADS)

    Thoet, William; Rainey, Timothy G.; Slutz, Lee A.; Weingard, Fred

    1992-09-01

    Recent results from the Artificial Neural VIsion Learning (ANVIL) program are presented. The focus of the ANVIL program is to apply neural network technologies to the air-to-surface 3D automatic target recognition (ATR) problem. The 3D Multiple Object Detection and Location System (MODALS) neural network was developed under the ANVIL program to simultaneously detect, locate, segment, and identify multiple targets. The performance results show a very high identification accuracy, a high detection rate, and low false alarm rate, even for areas with high clutter and shadowing. The results are shown as detection/false alarm curves and identification/false alarm curves. In addition, positional detection accuracy is shown for various scale sizes. To provide data for the program, visible terrain board imagery was collected under a variety of background and lighting conditions. Tests were made on over 500 targets of five types and two classes. These targets varied in scale by up to -25%, varied in azimuth by up to 120 degrees, and varied in elevation by up to 10 degrees. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target. This work is being performed under contract to Wright Laboratory AAAT-1.

  6. ANVIL neural network program for three-dimensional automatic target recognition

    NASA Astrophysics Data System (ADS)

    Thoet, William; Rainey, Timothy G.; Brettle, Dean W.; Slutz, Lee A.; Weingard, Fred

    1992-12-01

    The focus of the artificial neural vision learning (ANVIL) program is to apply neural network technologies to the air-to-surface 3-D automatic target recognition (ATR) problem. The 3-D multiple object detection and location system (MODALS) neural network was developed under the ANVIL program to simultaneously detect, locate, segment, and identify multiple targets. The performance results show a very high identification accuracy, a high detection rate, and a low false alarm rate, even for areas with high clutter and shadowing. The results are shown as detection/false alarm curves and identification/false alarm curves. In addition, positional detection accuracy is shown for various scale sizes. To provide data for the program, visible terrain board imagery was collected under a variety of background and lighting conditions. Tests were made on more than 500 targets of five types and two classes. These targets varied in scale by up to -25%, varied in azimuth by up to 120 deg, and varied in elevation by up to 10 deg. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target.

  7. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather

    DTIC Science & Technology

    2012-07-06

    Ephemeral Brightening,” 2nd ATST – East Workshop In Solar Physics: Magnetic Fields From The Photosphere To The Corona , Washington D.C., Mar 2012. [6...AFRL-RV-PS- AFRL-RV-PS- TR-2012-0133 TR-2012-0133 AUTOMATIC RECOGNITION OF SOLAR FEATURES FOR DEVELOPING DATA DRIVEN PREDICTION MODELS OF... SOLAR ACTIVITY AND SPACE WEATHER Jason Jackiewicz New Mexico State University Department of Astronomy PO Box 30001, MSC 4500 Las

  8. 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

  9. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference

    NASA Astrophysics Data System (ADS)

    Lee, Byeongwook; Cho, Kwang-Hyun

    2016-11-01

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintain high recognition performance under any circumstance? Recent neurophysiological studies have suggested that the phase of neuronal oscillations in the auditory cortex contributes to accurate speech recognition by guiding speech segmentation into smaller units at different timescales. A phase-locked relationship between neuronal oscillation and the speech envelope has recently been obtained, which suggests that the speech envelope provides a foundation for multi-timescale speech segmental information. In this study, we quantitatively investigated the role of the speech envelope as a potential temporal reference to segment speech using its instantaneous phase information. We evaluated the proposed approach by the achieved information gain and recognition performance in various noisy environments. The results indicate that the proposed segmentation scheme not only extracts more information from speech but also provides greater robustness in a recognition test.

  10. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference.

    PubMed

    Lee, Byeongwook; Cho, Kwang-Hyun

    2016-11-23

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintain high recognition performance under any circumstance? Recent neurophysiological studies have suggested that the phase of neuronal oscillations in the auditory cortex contributes to accurate speech recognition by guiding speech segmentation into smaller units at different timescales. A phase-locked relationship between neuronal oscillation and the speech envelope has recently been obtained, which suggests that the speech envelope provides a foundation for multi-timescale speech segmental information. In this study, we quantitatively investigated the role of the speech envelope as a potential temporal reference to segment speech using its instantaneous phase information. We evaluated the proposed approach by the achieved information gain and recognition performance in various noisy environments. The results indicate that the proposed segmentation scheme not only extracts more information from speech but also provides greater robustness in a recognition test.

  11. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference

    PubMed Central

    Lee, Byeongwook; Cho, Kwang-Hyun

    2016-01-01

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintain high recognition performance under any circumstance? Recent neurophysiological studies have suggested that the phase of neuronal oscillations in the auditory cortex contributes to accurate speech recognition by guiding speech segmentation into smaller units at different timescales. A phase-locked relationship between neuronal oscillation and the speech envelope has recently been obtained, which suggests that the speech envelope provides a foundation for multi-timescale speech segmental information. In this study, we quantitatively investigated the role of the speech envelope as a potential temporal reference to segment speech using its instantaneous phase information. We evaluated the proposed approach by the achieved information gain and recognition performance in various noisy environments. The results indicate that the proposed segmentation scheme not only extracts more information from speech but also provides greater robustness in a recognition test. PMID:27876875

  12. 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.

  13. Evaluation of synthetic aperture radar image segmentation algorithms in the context of automatic target recognition

    NASA Astrophysics Data System (ADS)

    Xue, Kefu; Power, Gregory J.; Gregga, Jason B.

    2002-11-01

    Image segmentation is a process to extract and organize information energy in the image pixel space according to a prescribed feature set. It is often a key preprocess in automatic target recognition (ATR) algorithms. In many cases, the performance of image segmentation algorithms will have significant impact on the performance of ATR algorithms. Due to the variations in feature set definitions and the innovations in the segmentation processes, there is large number of image segmentation algorithms existing in ATR world. Recently, the authors have investigated a number of measures to evaluate the performance of segmentation algorithms, such as Percentage Pixels Same (pps), Partial Directed Hausdorff (pdh) and Complex Inner Product (cip). In the research, we found that the combination of the three measures shows effectiveness in the evaluation of segmentation algorithms against truth data (human master segmentation). However, we still don't know what are the impact of those measures in the performance of ATR algorithms that are commonly measured by Probability of detection (PDet), Probability of false alarm (PFA), Probability of identification (PID), etc. In all practical situations, ATR boxes are implemented without human observer in the loop. The performance of synthetic aperture radar (SAR) image segmentation should be evaluated in the context of ATR rather than human observers. This research establishes a segmentation algorithm evaluation suite involving segmentation algorithm performance measures as well as the ATR algorithm performance measures. It provides a practical quantitative evaluation method to judge which SAR image segmentation algorithm is the best for a particular ATR application. The results are tabulated based on some baseline ATR algorithms and a typical image segmentation algorithm used in ATR applications.

  14. 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.

  15. 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%.

  16. 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.

  17. 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

  18. A rectangular-fit classifier for synthetic aperture radar automatic target recognition

    NASA Astrophysics Data System (ADS)

    Saghri, John A.; Cary, Daniel A.

    2007-09-01

    The utility of a rectangular-fit classifier for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) is examined. The target is fitted with and modeled as a rectangle that can best approximate its boundary. The rectangular fit procedure involves 1) a preprocessing phase to remove the background clutter and noise, 2) a pose detection phase to establish the alignment of the rectangle via a least squares straight line fitting algorithm, and 3) size determination phase via stretching the width and the height dimensions of the rectangle in order to encapsulate a pre-specified, e.g., 90%, of the points in the target. A training set composed of approximately half the total images in the MSTAR public imagery database are used to obtain and record the statistical variations in the width and height of the resulting rectangles for each potential target. The remaining half of the images is then used to assess the performance of this classifier. Preliminary results using minimum Euclidean and Mahalanobis distance classifiers show overall accuracies of 44% and 42%, respectively. Although the classification accuracy is relatively low, this technique can be successfully used in combination with other classifiers such as peaks, edges, corners, and shadow-based classifiers to enhance their performances. A unique feature of the rectangular fit classifier is that it is rotation invariant in its present form. However, observation of the dataset reveals that in general the shapes of the targets in SAR imagery are not fully rotation invariant. Thus, the classification accuracy is expected to improve considerably using multiple training sets, i.e., one training set generated and used for each possible pose. The tradeoff is the increased computation complexity which tends to be offset by ever increasing efficiency and speed of the processing hardware and software. The rectangular fit classifier can also be used as a pose detection routine and/or in conjunction with other ATR

  19. 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

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

    NASA Astrophysics Data System (ADS)

    Péron, Stéphanie; 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.

  1. Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition

    NASA Astrophysics Data System (ADS)

    Ding, Baiyuan; Wen, Gongjian; Zhong, Jinrong; Ma, Conghui; Yang, Xiaoliang

    2016-01-01

    This paper proposes a robust method for the matching of attributed scattering centers (ASCs) with application to synthetic aperture radar automatic target recognition (ATR). For the testing image to be classified, ASCs are extracted to match with the ones predicted by templates. First, Hungarian algorithm is employed to match those two ASC sets initially. Then, a precise matching is carried out through a threshold method. Point similarity and structure similarity are calculated, which are fused to evaluate the overall similarity of the two ASC sets based on the Dempster-Shafer theory of evidence. Finally, the target type is determined by such similarities between the testing image and various types of targets. Experiments on the moving and stationary target acquisition and recognition data verify the validity of the proposed method.

  2. LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

    PubMed

    He, Wei; Liang, Zhi; Teng, MaiKun; Niu, LiWen

    2016-12-01

    Identifying conserved binding motifs is an efficient way to study protein-ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand-binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its analogs are highly similar to well-known motifs reported by previous studies. The superiority of our method to handle flexible ligands was also demonstrated using isocitric acid as an example. Finally, we show that these motifs, together with their visual exhibition, permit better investigating and understanding of protein-ligand recognition process.

  3. 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.

  4. 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

  5. Adaptive remote sensing technology for feature recognition and tracking

    NASA Technical Reports Server (NTRS)

    Wilson, R. G.; Sivertson, W. E., Jr.; Bullock, G. F.

    1979-01-01

    A technology development plan designed to reduce the data load and data-management problems associated with global study and monitoring missions is described with a heavy emphasis placed on developing mission capabilities to eliminate the collection of unnecessary data. Improved data selectivity can be achieved through sensor automation correlated with the real-time needs of data users. The first phase of the plan includes the Feature Identification and Location Experiment (FILE) which is scheduled for the 1980 Shuttle flight. The FILE experiment is described with attention given to technology needs, development plan, feature recognition and classification, and cloud-snow detection/discrimination. Pointing, tracking and navigation received particular consideration, and it is concluded that this technology plan is viewed as an alternative to approaches to real-time acquisition that are based on extensive onboard format and inventory processing and reliance upon global-satellite-system navigation data.

  6. 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.

  7. 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.

  8. Adaptation to Phosphene Parameters Based on Multi-Object Recognition Using Simulated Prosthetic Vision.

    PubMed

    Xia, Peng; Hu, Jie; Peng, Yinghong

    2015-12-01

    Retinal prostheses for the restoration of functional vision are under development and visual prostheses targeting proximal stages of the visual pathway are also being explored. To investigate the experience with visual prostheses, psychophysical experiments using simulated prosthetic vision in normally sighted individuals are necessary. In this study, a helmet display with real-time images from a camera attached to the helmet provided the simulated vision, and experiments of recognition and discriminating multiple objects were used to evaluate visual performance under different parameters (gray scale, distortion, and dropout). The process of fitting and training with visual prostheses was simulated and estimated by adaptation to the parameters with time. The results showed that the increase in the number of gray scale and the decrease in phosphene distortion and dropout rate improved recognition performance significantly, and the recognition accuracy was 61.8 ± 7.6% under the optimum condition (gray scale: 8, distortion: k = 0, dropout: 0%). The adaption experiments indicated that the recognition performance was improved with time and the effect of adaptation to distortion was greater than dropout, which implies the difference of adaptation mechanism to the two parameters.

  9. First steps toward automatic patterns recognition from sequences of a restored river corridor photographs

    NASA Astrophysics Data System (ADS)

    Nouta, S.; Perona, P.; Schneider, P.; Wombacher, A.; Burlando, P.

    2009-04-01

    Obtaining information about river morphology and riparian vegetation patterns by means of photographic techniques is a challenging task with promising applications in the field of river hydraulic engineering and restoration. For instance, such a tool would speed up the post- processing phase of aerial images that is needed to calibrate both hydraulics and ecosystem models. Recognizing patterns automatically is relatively easy in the presence of well-defined objects and contrasting background colors, but this operation becomes rather difficult for open air or environmental photographs (e.g. of river corridors) where a multitude of colors, shadows, reflections and changing light conditions typically characterize the images. In this work an attempt is made in this direction and we begin with the already challenging task of recognizing water and non-water classes from digital photographs under changing light and surface albedo. Such conditions are typically due to either diurnal variability or bad weather conditions (e.g., like fog or snow). We use aerial photographs of the restored corridor of River Thur at Niederneunforn (Switzerland), which is currently monitored with high-resolution digital cameras as a task of the research project RECORD. Images of the river reach are taken from the top of two observation towers installed on the river levee and shots are frequency-dependent on current flow conditions. The approach we present here consists of masking the images by ignoring the irrelevant parts like mountains and sky, for instance. Next, features are defined which describe properties of the image or the image content, like e.g. color values, gradients of neighboring pixels, or application specific information like a probability distribution of a pixel being water derived from the digital elevation model. The investigated features can be classified according to two orthogonal dimensions: i) Pixel based features and features derived from a group of pixels and ii) Time

  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. 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.

  12. Fully automatic hp-adaptivity for acoustic and electromagnetic scattering in three dimensions

    NASA Astrophysics Data System (ADS)

    Kurtz, Jason Patrick

    We present an algorithm for fully automatic hp-adaptivity for finite element approximations of elliptic and Maxwell boundary value problems in three dimensions. The algorithm automatically generates a sequence of coarse grids, and a corresponding sequence of fine grids, such that the energy norm of the error decreases exponentially with respect to the number of degrees of freedom in either sequence. At each step, we employ a discrete optimization algorithm to determine the refinements for the current coarse grid such that the projection-based interpolation error for the current fine grid solution decreases with an optimal rate with respect to the number of degrees of freedom added by the refinement. The refinements are restricted only by the requirement that the resulting mesh is at most 1-irregular, but they may be anisotropic in both element size h and order of approximation p. While we cannot prove that our method converges at all, we present numerical evidence of exponential convergence for a diverse suite of model problems from acoustic and electromagnetic scattering. In particular we show that our method is well suited to the automatic resolution of exterior problems truncated by the introduction of a perfectly matched layer. To enable and accelerate the solution of these problems on commodity hardware, we include a detailed account of three critical aspects of our implementation, namely an efficient implementation of sum factorization, several efficient interfaces to the direct multi-frontal solver MUMPS, and some fast direct solvers for the computation of a sequence of nested projections.

  13. Restricted autoantigen recognition associated with deletional and adaptive regulatory mechanisms.

    PubMed

    Gebe, John A; Yue, Betty B; Unrath, Kelly A; Falk, Ben A; Nepom, Gerald T

    2009-07-01

    Autoimmune diabetes (T1D) is characterized by CD4(+) T cell reactivity to a variety of islet-associated Ags. At-risk individuals, genetically predisposed to T1D, often have similar T cell reactivity, but nevertheless fail to progress to clinically overt disease. To study the immune tolerance and regulatory environment permissive for such autoreactive T cells, we expressed TCR transgenes derived from two autoreactive human T cells, 4.13 and 164, in HLA-DR4 transgenic mice on a C57BL/6-derived "diabetes-resistant" background. Both TCR are responsive to an immunodominant epitope of glutamic acid decarboxylase 65(555-567), which is identical in sequence between humans and mice, is restricted by HLA-DR4, and is a naturally processed self Ag associated with T1D. Although both TCR use the identical Valpha and Vbeta genes, differing only in CDR3, we found stark differences in the mechanisms utilized in vivo in the maintenance of immune tolerance. A combination of thymic deletion (negative selection), TCR down-regulation, and peripheral activation-induced cell death dominated the phenotype of 164 T cells, which nevertheless still maintain their Ag responsiveness in the periphery. In contrast, 4.13 T cells are much less influenced by central and deletional tolerance mechanisms, and instead display a peripheral immune deviation including differentiation into IL-10-secreting Tr1 cells. These findings indicate a distinct set of regulatory alternatives for autoreactive T cells, even within a single highly restricted HLA-peptide-TCR recognition profile.

  14. 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.

  15. Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces.

    PubMed

    Rodríguez-Bermúdez, Germán; García-Laencina, Pedro J

    2012-11-01

    Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.

  16. 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.

  17. Modeling and Automatic Feedback Control of Tremor: Adaptive Estimation of Deep Brain Stimulation

    PubMed Central

    Rehan, Muhammad; Hong, Keum-Shik

    2013-01-01

    This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations. PMID:23638163

  18. 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

  19. 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%.

  20. Suppressing aliasing noise in the speech feature domain for automatic speech recognition.

    PubMed

    Deng, Huiqun; O'Shaughnessy, Douglas

    2008-07-01

    This letter points out that, although in the audio signal domain low-pass filtering has been used to prevent aliasing noise from entering the baseband of speech signals, an antialias process in the speech feature domain is still needed to prevent high modulation frequency components from entering the baseband of speech features. The existence of aliasing noise in speech features is revealed via spectral analysis of speech feature streams. A method for suppressing such aliasing noise is proposed. Experiments on large vocabulary speech recognition show that antialias processing of speech features can improve speech recognition, especially for noisy speech.

  1. Automatic concept recognition using the human phenotype ontology reference and test suite corpora.

    PubMed

    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.

  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. What’s Wrong With Automatic Speech Recognition (ASR) and How Can We Fix It?

    DTIC Science & Technology

    2013-03-01

    mid- teens to 30-50%, even for the best systems. This range makes further analysis by humans or machines extremely difficult. Over the last year...Name  Sex  Age  Organization  Number of Years in Speech Technologies  Position/Title  Questions as to the state of speech recognition

  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. 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…

  6. 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

  7. 3D passive photon counting automatic target recognition using advanced correlation filters.

    PubMed

    Cho, Myungjin; Mahalanobis, Abhijit; Javidi, Bahram

    2011-03-15

    In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

  8. Automatic speech recognition (ASR) and its use as a tool for assessment or therapy of voice, speech, and language disorders.

    PubMed

    Kitzing, Peter; Maier, Andreas; Ahlander, Viveka Lyberg

    2009-01-01

    In general opinion computerized automatic speech recognition (ASR) seems to be regarded as a method only to accomplish transcriptions from spoken language to written text and as such quite insecure and rather cumbersome. However, due to great advances in computer technology and informatics methodology ASR has nowadays become quite dependable and easier to handle, and the number of applications has increased considerably. After some introductory background information on ASR a number of applications of great interest for professionals in voice, speech, and language therapy are pointed out. In the foreseeable future, the keyboard and mouse will by means of ASR technology be replaced in many functions by a microphone as the human-computer interface, and the computer will talk back via its loud-speaker. It seems important that professionals engaged in the care of oral communication disorders take part in this development so their clients may get the optimal benefit from this new technology.

  9. MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

    SciTech Connect

    Chang, X; Yang, D

    2015-06-15

    Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

  10. 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.

  11. 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.

  12. Automatic seizure detection using correlation integral with nonlinear adaptive denoising and Kalman filter.

    PubMed

    Hongda Wang; Chiu-Sing Choy

    2016-08-01

    The ability of correlation integral for automatic seizure detection using scalp EEG data has been re-examined in this paper. To facilitate the detection performance and overcome the shortcoming of correlation integral, nonlinear adaptive denoising and Kalman filter have been adopted for pre-processing and post-processing. The three-stage algorithm has achieved 84.6% sensitivity and 0.087/h false detection rate, which are comparable to many machine learning based methods, but at much lower computational cost. Since this algorithm is tested with long-term scalp EEG, it has the potential to achieve higher performance with intracranial EEG. The clinical value of this algorithm includes providing a pre-judgement to assist the doctor's diagnosis procedure and acting as a reliable warning system in a wearable device for epilepsy patients.

  13. 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.

  14. [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%.

  15. 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.

  16. Automatic target recognition with image/video understanding systems based on network-symbolic models

    NASA Astrophysics Data System (ADS)

    Kuvich, Gary

    2004-09-01

    In past decades, the solution to ATR problem has been thought of as a solution to the Pattern Recognition problem. The reasons that Pattern Recognition problem has never been solved successfully and reliably for real-world images are more serious than lack of appropriate ideas. Vision is a part of a larger system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. Vision mechanisms cannot be completely understood apart from the informational processes related to knowledge and intelligence. A reliable solution to the ATR problem is possible only within the solution of a more generic Image Understanding Problem. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, converts visual information into relational Network-Symbolic structures, avoiding precise computations of 3-D models. Logic of visual scenes can be captured in Network-Symbolic models and used for disambiguation of visual information. Network-Symbolic Transformations make possible invariant recognition of a real-world object as exemplar of a class. This allows for creating ATR systems, reliable in field conditions.

  17. 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.

  18. Basic study for automatic recognition of osteoporosis using abdominal x-ray CT images

    NASA Astrophysics Data System (ADS)

    Nishihara, Sadamitsu; Fujita, Hiroshi; Iida, Tadayuki; Takigawa, Atsushi; Hara, Takeshi; Zhou, Xiangrong

    2004-05-01

    We have developed an algorithm that can be used to distinguish the central part of the vertebral body from an abdominal X-ray CT image and to automatically calculate three measures to diagnose the degree of osteoporosis in a patient. In addition, we examined whether it is possible to use these CT images as an aid in diagnosing osteoporosis. Three measures that were automatically extracted from the central part of a vertebral body in the CT images were compared with the bone mineral density (BMD) values that were obtained from the same vertebral body. We calculated the mean CT number, coefficient of variation, and the first moment of power spectrum in the recognized vertebral body. We judged whether a patient had osteoporosis using the diagnostic criteria for primary osteoporosis (Year 2000 revision, published by the Japanese Society for Bone and Mineral Research). We classified three measures for normal and abnormal groups using the principal component analysis, and the two groups were compared with the results obtained from the diagnostic criteria. As a result, it was found that the algorithm could be used to distinguish the central part of the vertebral body in the CT images and to calculate these measures automatically. When distinguishing whether a patient was osteoporotic or not with the three measures obtained from the CT images, the ratio (sensitivity) usable for diagnosing a patient as osteoporotic was 0.93 (14/15), and the ratio (specificity) usable for diagnosing a patient as normal was 0.64 (7/11). Based on these results, we believe that it is possible to utilize the measures obtained from these CT images to aid in diagnosing osteoporosis.

  19. Automatic recognition of five types of white blood cells in peripheral blood.

    PubMed

    Rezatofighi, Seyed Hamid; Soltanian-Zadeh, Hamid

    2011-06-01

    This paper proposes image processing algorithms to recognize five types of white blood cells in peripheral blood automatically. First, a method based on Gram-Schmidt orthogonalization is proposed along with a snake algorithm to segment nucleus and cytoplasm of the cells. Then, a variety of features are extracted from the segmented regions. Next, most discriminative features are selected using a Sequential Forward Selection (SFS) algorithm and performances of two classifiers, Artificial Neural Network (ANN) and Support Vector Machine (SVM), are compared. The results demonstrate that the proposed methods are accurate and sufficiently fast to be used in hematological laboratories.

  20. The Development of the Automatic Target Recognition System for the UGV/RSTA LADAR

    DTIC Science & Technology

    1995-03-21

    Vol. 1960, Orlando, FL, 14-16 April 1993, pp. 57-71. 6. J.E. Nettleton and S. Holder, "Active/passive laser radar field test (Fort A.P. Hill, VA...is to develop the automatic target recogni- tion (ATR) system that will process the imagery from the RSTA laser radar (ladar). A real-time...ix 1. INTRODUCTION 1 2. IMAGE DATA BASES 5 2.1 Introduction 5 2.2 Tri-Service Laser -Radar (TSLR) Data 5 2.3 Hobby Shop (HBS) Laser -Radar Data 11

  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. 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.

  3. 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.

  4. 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.

  5. A probability of error-constrained sequential decision algorithm for data-rich automatic target recognition

    NASA Astrophysics Data System (ADS)

    Reyes, Irwin O.; DeVore, Michael D.; Beling, Peter A.; Horowitz, Barry M.

    2010-04-01

    This paper illustrates an approach to sequential hypothesis testing designed not to minimize the amount of data collected but to reduce the overall amount of processing required, while still guaranteeing pre-specified conditional probabilities of error. The approach is potentially useful when sensor data are plentiful but time and processing capability are constrained. The approach gradually reduces the number of target hypotheses under consideration as more sensor data are processed, proportionally allocating time and processing resources to the most likely target classes. The approach is demonstrated on a multi-class ladar-based target recognition problem and compared with uniform-computation tests.

  6. 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.

  7. 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.

  8. Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals.

    PubMed

    Khezri, Mahdi; Firoozabadi, Mohammad; Sharafat, Ahmad Reza

    2015-11-01

    In this study, we proposed a new adaptive method for fusing multiple emotional modalities to improve the performance of the emotion recognition system. Three-channel forehead biosignals along with peripheral physiological measurements (blood volume pressure, skin conductance, and interbeat intervals) were utilized as emotional modalities. Six basic emotions, i.e., anger, sadness, fear, disgust, happiness, and surprise were elicited by displaying preselected video clips for each of the 25 participants in the experiment; the physiological signals were collected simultaneously. In our multimodal emotion recognition system, recorded signals with the formation of several classification units identified the emotions independently. Then the results were fused using the adaptive weighted linear model to produce the final result. Each classification unit is assigned a weight that is determined dynamically by considering the performance of the units during the testing phase and the training phase results. This dynamic weighting scheme enables the emotion recognition system to adapt itself to each new user. The results showed that the suggested method outperformed conventional fusion of the features and classification units using the majority voting method. In addition, a considerable improvement, compared to the systems that used the static weighting schemes for fusing classification units, was also shown. Using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers, the overall classification accuracies of 84.7% and 80% were obtained in identifying the emotions, respectively. In addition, applying the forehead or physiological signals in the proposed scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities.

  9. 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

  10. Automatic nevi segmentation using adaptive mean shift filters and feature analysis

    NASA Astrophysics Data System (ADS)

    King, Michael A.; Lee, Tim K.; Atkins, M. Stella; McLean, David I.

    2004-05-01

    A novel automatic method of segmenting nevi is explained and analyzed in this paper. The first step in nevi segmentation is to iteratively apply an adaptive mean shift filter to form clusters in the image and to remove noise. The goal of this step is to remove differences in skin intensity and hairs from the image, while still preserving the shape of nevi present on the skin. Each iteration of the mean shift filter changes pixel values to be a weighted average of pixels in its neighborhood. Some new extensions to the mean shift filter are proposed to allow for better segmentation of nevi from the skin. The kernel, that describes how the pixels in its neighborhood will be averaged, is adaptive; the shape of the kernel is a function of the local histogram. After initial clustering, a simple merging of clusters is done. Finally, clusters that are local minima are found and analyzed to determine which clusters are nevi. When this algorithm was compared to an assessment by an expert dermatologist, it showed a sensitivity rate and diagnostic accuracy of over 95% on the test set, for nevi larger than 1.5mm.

  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. Application of an automatic adaptive filter for Heart Rate Variability analysis.

    PubMed

    Dos Santos, Laurita; Barroso, Joaquim J; Macau, Elbert E N; de Godoy, Moacir F

    2013-12-01

    The presence of artifacts and noise effects in temporal series can seriously hinder the analysis of Heart Rate Variability (HRV). The tachograms should be carefully edited to avoid erroneous interpretations. The physician should carefully analyze the tachogram in order to detect points that might be associated with unlikely biophysical behavior and manually eliminate them from the data series. However, this is a time-consuming procedure. To facilitate the pre-analysis of the tachogram, this study uses a method of data filtering based on an adaptive filter which is quickly able to analyze a large amount of data. The method was applied to 229 time series from a database of patients with different clinical conditions: premature newborns, full-term newborns, healthy young adults, adults submitted to a very-low-calorie diet, and adults under preoperative evaluation for coronary artery bypass grafting. This proposed method is compared to the demanding conventional method, wherein the corrections of occasional ectopic beats and artifacts are usually manually executed by a specialist. To confirm the reliability of the results obtained, correlation coefficients were calculated, using both automatic and manual methods of ltering for each HRV index selected. A high correlation between the results was found, with highly significant p values, for all cases, except for some parameters analyzed in the premature newborns group, an issue that is thoroughly discussed. The authors concluded that the proposed adaptive filtering method helps to efficiently handle the task of editing temporal series for HRV analysis.

  13. Adaptive automatic data analysis in full-field fringe-pattern-based optical metrology

    NASA Astrophysics Data System (ADS)

    Trusiak, Maciej; Patorski, Krzysztof; Sluzewski, Lukasz; Pokorski, Krzysztof; Sunderland, Zofia

    2016-12-01

    Fringe pattern processing and analysis is an important task of full-field optical measurement techniques like interferometry, digital holography, structural illumination and moiré. In this contribution we present several adaptive automatic data analysis solutions based on the notion of Hilbert-Huang transform for measurand retrieval via fringe pattern phase and amplitude demodulation. The Hilbert-Huang transform consists of 2D empirical mode decomposition algorithm and Hilbert spiral transform analysis. Empirical mode decomposition adaptively dissects a meaningful number of same-scale subimages from the analyzed pattern - it is a data-driven method. Appropriately managing this set of unique subimages results in a very powerful fringe pre-filtering tool. Phase/amplitude demodulation is performed using Hilbert spiral transform aided by the local fringe orientation estimator. We describe several optical measurement techniques for technical and biological objects characterization basing on the especially tailored Hilbert-Huang algorithm modifications for fringe pattern denoising, detrending and amplitude/phase demodulation.

  14. 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.

  15. 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.

  16. 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 .

  17. Automatic segmentation and recognition of lungs and lesion from CT scans of thorax.

    PubMed

    Kakar, Manish; Olsen, Dag Rune

    2009-01-01

    In this study, a fully automated texture-based segmentation and recognition system for lesion and lungs from CT of thorax is presented. For the segmentation part, we have extracted texture features by Gabor filtering the images, and, then combined these features to segment the target volume by using Fuzzy C Means (FCM) clustering. Since clustering is sensitive to initialization of cluster prototypes, optimal initialization of the cluster prototypes was done by using a Genetic Algorithm. For the recognition stage, we have used cortex like mechanism for extracting statistical features in addition to shape-based features. The segmented regions showed a high degree of imbalance between positive and negative samples, so we employed over and under sampling for balancing the data. Finally, the balanced and normalized data was subjected to Support Vector Machine (SimpleSVM) for training and testing. Results reveal an accuracy of delineation to be 94.06%, 94.32% and 89.04% for left lung, right lung and lesion, respectively. Average sensitivity of the SVM classifier was seen to be 89.48%.

  18. A STUDY OF THE APPLICATION OF PATTERN RECOGNITION TO AUTOMATIC CONTROL.

    DTIC Science & Technology

    device, Adaline , to mimic the performance of the controller of a plant. This study discusses Adaline and the minimum square error method of adaption... Adaline is trained by observing the behavior of the controller in numerous situations. The problem of coding the information to be presented to adaline ...is discussed, and finally, a suitably trained Adaline takes control of the plant. (Author)

  19. 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

  20. Automatic feature design for optical character recognition using an evolutionary search procedure.

    PubMed

    Stentiford, F W

    1985-03-01

    An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers.

  1. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach.

    PubMed

    Ren, Xiang; El-Kishky, Ahmed; Wang, Chi; Han, Jiawei

    2015-08-01

    In today's computerized and information-based society, we are soaked with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To unlock the value of these unstructured text data from various domains, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their types (e.g., people, product, food) in a scalable way. We demonstrate on real datasets including news articles and tweets how these typed entities aid in knowledge discovery and management.

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

    PubMed

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

    2016-05-02

    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.

  3. Real-time error detection but not error correction drives automatic visuomotor adaptation.

    PubMed

    Hinder, Mark R; Riek, Stephan; Tresilian, James R; de Rugy, Aymar; Carson, Richard G

    2010-03-01

    We investigated the role of visual feedback of task performance in visuomotor adaptation. Participants produced novel two degrees of freedom movements (elbow flexion-extension, forearm pronation-supination) to move a cursor towards visual targets. Following trials with no rotation, participants were exposed to a 60 degrees visuomotor rotation, before returning to the non-rotated condition. A colour cue on each trial permitted identification of the rotated/non-rotated contexts. Participants could not see their arm but received continuous and concurrent visual feedback (CF) of a cursor representing limb position or post-trial visual feedback (PF) representing the movement trajectory. Separate groups of participants who received CF were instructed that online modifications of their movements either were, or were not, permissible as a means of improving performance. Feedforward-mediated performance improvements occurred for both CF and PF groups in the rotated environment. Furthermore, for CF participants this adaptation occurred regardless of whether feedback modifications of motor commands were permissible. Upon re-exposure to the non-rotated environment participants in the CF, but not PF, groups exhibited post-training aftereffects, manifested as greater angular deviations from a straight initial trajectory, with respect to the pre-rotation trials. Accordingly, the nature of the performance improvements that occurred was dependent upon the timing of the visual feedback of task performance. Continuous visual feedback of task performance during task execution appears critical in realising automatic visuomotor adaptation through a recalibration of the visuomotor mapping that transforms visual inputs into appropriate motor commands.

  4. 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

  5. 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.

  6. 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.

  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. A new approach for semi-automatic rock mass joints recognition from 3D point clouds

    NASA Astrophysics Data System (ADS)

    Riquelme, Adrián J.; Abellán, A.; Tomás, R.; Jaboyedoff, M.

    2014-07-01

    Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information - synthetic and 3D scanned data - were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

  9. Complete automatic target cuer/recognition system for tactical forward-looking infrared images

    NASA Astrophysics Data System (ADS)

    Ernisse, Brian E.; Rogers, Steven K.; DeSimio, Martin P.; Raines, Richard A.

    1997-09-01

    A complete forward-looking IR (FLIR) automatic target cuer/recognizer (ATC/R) is presented. The data used for development and testing of this ATC/R are first generation FLIR images collected using a F-15E. The database contains thousands of images with various mission profiles and target arrangements. The specific target of interest is a mobile missile launcher, the primary target. The goal is to locate all vehicles (secondary targets) within a scene and identify the primary targets. The system developed and tested includes an image segmenter, region cluster algorithm, feature extractor, and classifier. Conventional image processing algorithms in conjunction with neural network techniques are used to form a complete ATC/R system. The conventional techniques include hit/miss filtering, difference of Gaussian filtering, and region clustering. A neural network (multilayer perceptron) is used for classification. These algorithms are developed, tested and then combined into a functional ATC/R system. Overall primary target detection rate (cuer) is 84% with a 69% primary target identification (recognizer) rate at ranges relevant to munitions release. Furthermore, the false alarm rate (a nontarget cued as a target) in only 2.3 per scene. The research is being completed with a 10 flight test profile using third generation FLIR images.

  10. 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

  11. 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

  12. All optical three dimensional spatio-temporal correlator for automatic event recognition using a multiphoton atomic system

    NASA Astrophysics Data System (ADS)

    Monjur, Mehjabin S.; Fouda, Mohamed F.; Shahriar, Selim M.

    2016-12-01

    We describe an automatic event recognition (AER) system based on a three-dimensional spatio-temporal correlator (STC) that combines the techniques of holographic correlation and photon echo based temporal pattern recognition. The STC is shift invariant in space and time. It can be used to recognize rapidly an event (e.g., a short video clip) that may be present in a large video file, and determine the temporal location of the event. Using polar Mellin transform, it is possible to realize an STC that is also scale and rotation invariant spatially. Numerical simulation results of such a system are presented using quantum mechanical equations of evolution. For this simulation we have used the model of an idealized, decay-free two level system of atoms with an inhomogeneous broadening that is larger than the inverse of the temporal resolution of the data stream. We show how such a system can be realized by using a lambda-type three level system in atomic vapor, via adiabatic elimination of the intermediate state. We have also developed analytically a three dimensional transfer function of the system, and shown that it agrees closely with the results obtained via explicit simulation of the atomic response. The analytical transfer function can be used to determine the response of an STC very rapidly. In addition to the correlation signal, other nonlinear terms appear in the explicit numerical model. These terms are also verified by the analytical model. We describe how the AER can be operated in a manner such that the correlation signal remains unaffected by the additional nonlinear terms. We also show how such a practical STC can be realized using a combination of a porous-glass based Rb vapor cell, a holographic video disc, and a lithium niobate crystal.

  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. Automatic recognition of Parkinson's disease using surface electromyography during standardized gait tests.

    PubMed

    Kugler, Patrick; Jaremenko, Christian; Schlachetzki, Johannes; Winkler, Juergen; Klucken, Jochen; Eskofier, Bjoern

    2013-01-01

    Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for PD.

  15. Fraudulent ID using face morphs: Experiments on human and automatic recognition

    PubMed Central

    Robertson, David J.; Kramer, Robin S. S.

    2017-01-01

    Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people’s ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to ‘trained’ human viewers—i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security. PMID:28328928

  16. 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.

  17. Fraudulent ID using face morphs: Experiments on human and automatic recognition.

    PubMed

    Robertson, David J; Kramer, Robin S S; Burton, A Mike

    2017-01-01

    Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people's ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to 'trained' human viewers-i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security.

  18. 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.

  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.

  20. Image processing and pattern recognition with CVIPtools MATLAB toolbox: automatic creation of masks for veterinary thermographic images

    NASA Astrophysics Data System (ADS)

    Mishra, Deependra K.; Umbaugh, Scott E.; Lama, Norsang; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

    2016-09-01

    CVIPtools is a software package for the exploration of computer vision and image processing developed in the Computer Vision and Image Processing Laboratory at Southern Illinois University Edwardsville. CVIPtools is available in three variants - a) CVIPtools Graphical User Interface, b) CVIPtools C library and c) CVIPtools MATLAB toolbox, which makes it accessible to a variety of different users. It offers students, faculty, researchers and any user a free and easy way to explore computer vision and image processing techniques. Many functions have been implemented and are updated on a regular basis, the library has reached a level of sophistication that makes it suitable for both educational and research purposes. In this paper, the detail list of the functions available in the CVIPtools MATLAB toolbox are presented and how these functions can be used in image analysis and computer vision applications. The CVIPtools MATLAB toolbox allows the user to gain practical experience to better understand underlying theoretical problems in image processing and pattern recognition. As an example application, the algorithm for the automatic creation of masks for veterinary thermographic images is presented.

  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. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation.

    PubMed

    Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Geng, Weidong

    2017-02-24

    High-density surface electromyography (HD-sEMG) is to record muscles' electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.

  3. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

    PubMed Central

    Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Geng, Weidong

    2017-01-01

    High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition. PMID:28245586

  4. Real-time task recognition in cataract surgery videos using adaptive spatiotemporal polynomials.

    PubMed

    Quellec, Gwénolé; Lamard, Mathieu; Cochener, Béatrice; Cazuguel, Guy

    2015-04-01

    This paper introduces a new algorithm for recognizing surgical tasks in real-time in a video stream. The goal is to communicate information to the surgeon in due time during a video-monitored surgery. The proposed algorithm is applied to cataract surgery, which is the most common eye surgery. To compensate for eye motion and zoom level variations, cataract surgery videos are first normalized. Then, the motion content of short video subsequences is characterized with spatiotemporal polynomials: a multiscale motion characterization based on adaptive spatiotemporal polynomials is presented. The proposed solution is particularly suited to characterize deformable moving objects with fuzzy borders, which are typically found in surgical videos. Given a target surgical task, the system is trained to identify which spatiotemporal polynomials are usually extracted from videos when and only when this task is being performed. These key spatiotemporal polynomials are then searched in new videos to recognize the target surgical task. For improved performances, the system jointly adapts the spatiotemporal polynomial basis and identifies the key spatiotemporal polynomials using the multiple-instance learning paradigm. The proposed system runs in real-time and outperforms the previous solution from our group, both for surgical task recognition ( Az = 0.851 on average, as opposed to Az = 0.794 previously) and for the joint segmentation and recognition of surgical tasks ( Az = 0.856 on average, as opposed to Az = 0.832 previously).

  5. 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.

  6. 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

  7. 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.

  8. Automatic phonological activation during visual word recognition in bilingual children: A cross-language masked priming study in grades 3 and 5.

    PubMed

    Sauval, Karinne; Perre, Laetitia; Duncan, Lynne G; Marinus, Eva; Casalis, Séverine

    2017-02-01

    Previous masked priming research has shown automatic phonological activation during visual word recognition in monolingual skilled adult readers. Activation also occurs across languages in bilingual adult readers, suggesting that the activation of phonological representations is not language specific. Less is known about developing readers. First, it is unclear whether there is automatic phonological activation during visual word recognition among children in general. Second, no empirical data exist on whether the activation of phonological representations is language specific or not in bilingual children. The current study investigated these issues in bilingual third and fifth graders using cross-language phonological masked priming in a lexical decision task. Targets were French words, and primes were English pseudowords of three types: (a) phonological primes, which share phonological information with the target beginning (e.g., dee-DIMANCHE [Sunday], pronounced /di:/-/dimãʃ/); (b) orthographic control primes, which control for letters shared by the phonological prime and target (e.g., d) and their position (e.g., doo-DIMANCHE, pronounced /du:/-/dimãʃ/); and (c) unrelated primes, which share no phonological or orthographic information with the target beginning (e.g., pow-DIMANCHE, pronounced /paʊ/-/dimãʃ/). Significant phonological priming was observed, suggesting that (a) phonological representations are rapidly and automatically activated by print during visual word recognition from Grade 3 onward and that (b) the activation of phonological representations is not language specific in bilingual children.

  9. Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan

    2017-09-01

    It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus

  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. Toward design of an environment-aware adaptive locomotion-mode-recognition system.

    PubMed

    Du, Lin; Zhang, Fan; Liu, Ming; Huang, He

    2012-10-01

    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.

  12. 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.

  13. Thai Automatic Speech Recognition

    DTIC Science & Technology

    2005-01-01

    reported elsewhere. 1. Introduction This research was performed as part of the DARPA-Babylon program aimed at rapidly developing multilingual speech-to...used in an external DARPA evaluation involving medical scenarios between an American Doctor and a naïve monolingual Thai patient. 2. Thai Language...To create more general acoustic models we collected read speech data from native speakers based on the concepts of our multilingual data collection

  14. Towards Automatic Threat Recognition

    DTIC Science & Technology

    2006-12-01

    York: Bantam. Forschungsinstitut für Kommunikation, Informationsverarbeitung und Ergonomie FGAN Informationstechnik und Führungssysteme KIE Towards...Informationsverarbeitung und Ergonomie FGAN Informationstechnik und Führungssysteme KIE Content Preliminaries about Information Fusion The System Ontology Unification...as Processing Principle Back to the Example Conclusion and Outlook Forschungsinstitut für Kommunikation, Informationsverarbeitung und Ergonomie FGAN

  15. Effects of single cortisol administrations on human affect reviewed: Coping with stress through adaptive regulation of automatic cognitive processing.

    PubMed

    Putman, Peter; Roelofs, Karin

    2011-05-01

    The human stress hormone cortisol may facilitate effective coping after psychological stress. In apparent agreement, administration of cortisol has been demonstrated to reduce fear in response to stressors. For anxious patients with phobias or posttraumatic stress disorder this has been ascribed to hypothetical inhibition of retrieval of traumatic memories. However, such stress-protective effects may also work via adaptive regulation of early cognitive processing of threatening information from the environment. This paper selectively reviews the available literature on effects of single cortisol administrations on affect and early cognitive processing of affectively significant information. The concluded working hypothesis is that immediate effects of high concentration of cortisol may facilitate stress-coping via inhibition of automatic processing of goal-irrelevant threatening information and through increased automatic approach-avoidance responses in early emotional processing. Limitations in the existing literature and suggestions for future directions are briefly discussed.

  16. 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.

  17. Adaptive spatial filtering for off-axis digital holographic microscopy based on region recognition approach with iterative thresholding

    NASA Astrophysics Data System (ADS)

    He, Xuefei; Nguyen, Chuong Vinh; Pratap, Mrinalini; Zheng, Yujie; Wang, Yi; Nisbet, David R.; Rug, Melanie; Maier, Alexander G.; Lee, Woei Ming

    2016-12-01

    Here we propose a region-recognition approach with iterative thresholding, which is adaptively tailored to extract the appropriate region or shape of spatial frequency. In order to justify the method, we tested it with different samples and imaging conditions (different objectives). We demonstrate that our method provides a useful method for rapid imaging of cellular dynamics in microfluidic and cell cultures.

  18. [Creating language model of the forensic medicine domain for developing a autopsy recording system by automatic speech recognition].

    PubMed

    Niijima, H; Ito, N; Ogino, S; Takatori, T; Iwase, H; Kobayashi, M

    2000-11-01

    For the purpose of practical use of speech recognition technology for recording of forensic autopsy, a language model of the speech recording system, specialized for the forensic autopsy, was developed. The language model for the forensic autopsy by applying 3-gram model was created, and an acoustic model for Japanese speech recognition by Hidden Markov Model in addition to the above were utilized to customize the speech recognition engine for forensic autopsy. A forensic vocabulary set of over 10,000 words was compiled and some 300,000 sentence patterns were made to create the forensic language model, then properly mixing with a general language model to attain high exactitude. When tried by dictating autopsy findings, this speech recognition system was proved to be about 95% of recognition rate that seems to have reached to the practical usability in view of speech recognition software, though there remains rooms for improving its hardware and application-layer software.

  19. Priming and interference effects can be dissociated in the Stroop task: new evidence in favor of the automaticity of word recognition.

    PubMed

    Catena, Andrés; Fuentes, Luis J; Tudela, Pío

    2002-03-01

    Recently, Besner, Stolz, and Boutilier (1997) showed that by coloring a single letter instead of the whole word, Stroop interference is reduced or even eliminated, a result that is at odds with the widely accepted assumption that word recognition is automatic. In a replication of the Besner et al. study, we computed priming effects in addition to the standard Stroop interference. Interference results replicated those of Besner et al. Also, negative priming in the all-letter-colored condition and positive priming in the single-letter-colored condition were obtained. Priming findings demonstrate that word processing can take place in the absence of interference effects. These results support the view of automatic processing of words in the Stroop task and call for priming as a more appropriate measure of word processing than interference.

  20. 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.

  1. A Rapid Model Adaptation Technique for Emotional Speech Recognition with Style Estimation Based on Multiple-Regression HMM

    NASA Astrophysics Data System (ADS)

    Ijima, Yusuke; Nose, Takashi; Tachibana, Makoto; Kobayashi, Takao

    In this paper, we propose a rapid model adaptation technique for emotional speech recognition which enables us to extract paralinguistic information as well as linguistic information contained in speech signals. This technique is based on style estimation and style adaptation using a multiple-regression HMM (MRHMM). In the MRHMM, the mean parameters of the output probability density function are controlled by a low-dimensional parameter vector, called a style vector, which corresponds to a set of the explanatory variables of the multiple regression. The recognition process consists of two stages. In the first stage, the style vector that represents the emotional expression category and the intensity of its expressiveness for the input speech is estimated on a sentence-by-sentence basis. Next, the acoustic models are adapted using the estimated style vector, and then standard HMM-based speech recognition is performed in the second stage. We assess the performance of the proposed technique in the recognition of simulated emotional speech uttered by both professional narrators and non-professional speakers.

  2. 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…

  3. 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),…

  4. Structural Basis for Conserved Regulation and Adaptation of the Signal Recognition Particle Targeting Complex.

    PubMed

    Wild, Klemens; Bange, Gert; Motiejunas, Domantas; Kribelbauer, Judith; Hendricks, Astrid; Segnitz, Bernd; Wade, Rebecca C; Sinning, Irmgard

    2016-07-17

    The signal recognition particle (SRP) is a ribonucleoprotein complex with a key role in targeting and insertion of membrane proteins. The two SRP GTPases, SRP54 (Ffh in bacteria) and FtsY (SRα in eukaryotes), form the core of the targeting complex (TC) regulating the SRP cycle. The architecture of the TC and its stimulation by RNA has been described for the bacterial SRP system while this information is lacking for other domains of life. Here, we present the crystal structures of the GTPase heterodimers of archaeal (Sulfolobus solfataricus), eukaryotic (Homo sapiens), and chloroplast (Arabidopsis thaliana) SRP systems. The comprehensive structural comparison combined with Brownian dynamics simulations of TC formation allows for the description of the general blueprint and of specific adaptations of the quasi-symmetric heterodimer. Our work defines conserved external nucleotide-binding sites for SRP GTPase activation by RNA. Structural analyses of the GDP-bound, post-hydrolysis states reveal a conserved, magnesium-sensitive switch within the I-box. Overall, we provide a general model for SRP cycle regulation by RNA.

  5. Adaptive membership functions for handwritten character recognition by Voronoi-based image zoning.

    PubMed

    Pirlo, Giuseppe; Impedovo, Donato

    2012-09-01

    In the field of handwritten character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered to be able to cope with handwritten pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers who have proposed several image-zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions that define the way in which a feature influences different zones of the zoning method. The result is that the membership functions defined to date follow nonadaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine-in a unique process-the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods.

  6. Telemetric control of heart adaptation during automatic and free-fall parachute jumps.

    PubMed

    Deroanne, R; Cession-Fossion, A; Juchmes, J; Servais, J C; Petit, J M

    1975-02-01

    Telmetered heart rate recordings have been ovtaine from 17 parachutists (6 during automatic jumps) 9 Catecholamine (adrenaline and noradrenaline) concentrations have been measured in urine and plasma of six of these subjects. No difference appears between heart rates recorded in the two jumps at egress and at parachute deployment. On the other hand, higher heart rate values are recorded during automatic jumps during descent and at ground impace. The urine catecholamine analysis after jump shows a statistically significant increase in adrenaline and noradrenaline concentration. It is suggested that simulation of the orthosympathetic system is due to two facts; muscular work performed during jumping and the emotional stress which it involves. The importance of these two causes varies with the jump circumstances.

  7. 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.

  8. 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.

  9. What is a nice smile like that doing in a place like this? Automatic affective responses to environments influence the recognition of facial expressions.

    PubMed

    Hietanen, Jari K; Klemettilä, Terhi; Kettunen, Jani E; Korpela, Kalevi M

    2007-09-01

    An affective priming paradigm with pictures of environmental scenes and facial expressions as primes and targets, respectively, was employed in order to investigate the role of natural (e.g., vegetation) and built elements (e.g., buildings) in eliciting rapid affective responses. In Experiment 1, images of environmental scenes were digitally manipulated to make continua of priming pictures with a gradual increase of natural elements (and a decrease of built elements). The primes were followed by presentations of facial expressions of happiness and disgust as to-be-recognized target stimuli. The recognition times of happy faces decreased and the recognition times of disgusted faces increased as the quantity of natural/built material present in the primes increased/decreased. The physical changes also influenced the evaluated restorativeness and affective valence of the primes. In Experiment 2, the primes used in Experiment 1 were manipulated in such a way that they were void of any recognizable natural or built elements but contained either similar colours or similar shapes as primes in Experiment 1. This time the results showed no effect of priming. These results were interpreted to give support for a view that the priming effect by environmental pictures is due to the primes representing environmental scenes and not due to the presence of certain low-level colour or shape information in the primes. In all, the present results provide evidence that perception of environmental scenes elicits automatic affective responses and influences recognition of facial expressions.

  10. Membrane Composition Changes and Physiological Adaptation by Streptococcus mutans Signal Recognition Particle Pathway Mutants▿

    PubMed Central

    Hasona, Adnan; Zuobi-Hasona, Kheir; Crowley, Paula J.; Abranches, Jacqueline; Ruelf, Michael A.; Bleiweis, Arnold S.; Brady, L. Jeannine

    2007-01-01

    Previously, we presented evidence that the oral cariogenic species Streptococcus mutans remains viable but physiologically impaired and sensitive to environmental stress when genes encoding the minimal conserved bacterial signal recognition particle (SRP) elements are inactivated. Two-dimensional gel electrophoresis of isolated membrane fractions from strain UA159 and three mutants (Δffh, ΔscRNA, and ΔftsY) grown at pH 7.0 or pH 5.0 allowed us to obtain insight into the adaptation process and the identities of potential SRP substrates. Mutant membrane preparations contained increased amounts of the chaperones DnaK and GroES and ClpP protease but decreased amounts of transcription- and translation-related proteins, the β subunit of ATPase, HPr, and several metabolic and glycolytic enzymes. Therefore, the acid sensitivity of SRP mutants might be caused in part by diminished ATPase activity, as well as the absence of an efficient mechanism for supplying ATP quickly at the site of proton elimination. Decreased amounts of LuxS were also observed in all mutant membranes. To further define physiological changes that occur upon disruption of the SRP pathway, we studied global gene expression in S. mutans UA159 (parent strain) and AH333 (Δffh mutant) using microarray analysis. Transcriptome analysis revealed up-regulation of 81 genes, including genes encoding chaperones, proteases, cell envelope biosynthetic enzymes, and DNA repair and replication enzymes, and down-regulation of 35 genes, including genes concerned with competence, ribosomal proteins, and enzymes involved in amino acid and protein biosynthesis. Quantitative real-time reverse transcription-PCR analysis of eight selected genes confirmed the microarray data. Consistent with a demonstrated defect in competence and the suggested impairment of LuxS-dependent quorum sensing, biofilm formation was significantly decreased in each SRP mutant. PMID:17085548

  11. 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.

  12. 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…

  13. Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment

    ERIC Educational Resources Information Center

    Premlatha, K. R.; Dharani, B.; Geetha, T. V.

    2016-01-01

    E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…

  14. 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,…

  15. 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…

  16. 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

  17. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

    PubMed

    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-01-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.

  18. Measuring Negative and Positive Thoughts in Children: An Adaptation of the Children's Automatic Thoughts Scale (CATS).

    PubMed

    Hogendoorn, Sanne M; Wolters, Lidewij H; Vervoort, Leentje; Prins, Pier J M; Boer, Frits; Kooij, Emelie; de Haan, Else

    2010-10-01

    The aim of this study is to describe the factor structure and psychometric properties of an extended version of the Children's Automatic Thoughts Scale (CATS), the CATS-Negative/Positive (CATS-N/P). The CATS was originally designed to assess negative self-statements in children and adolescents. However, positive thoughts also play a major role in childhood disorders such as anxiety and depression. Therefore, positive self-statements were added to the CATS. The CATS-N/P was administered to a community sample of 554 children aged 8-18 years. The results of a confirmatory factor analysis revealed that the positive self-statements formed a separate and psychometrically sound factor. Internal and short-term test-retest reliability was good. Boys reported more hostile and positive thoughts than girls; and younger children reported more negative thoughts concerning physical threat, social threat, and failure than older children. In conclusion, the results of the current study support the use of the CATS-N/P for the measurement of positive and negative thoughts in children. The application of the CATS-N/P can facilitate further research on cognitive factors in different childhood disorders.

  19. Study on Information Fusion Based Check Recognition System

    NASA Astrophysics Data System (ADS)

    Wang, Dong

    Automatic check recognition techniques play an important role in financial systems, especially in risk management. This paper presents a novel check recognition system based on multi-cue information fusion theory. For Chinese bank check, the amount can be independently determined by legal amount, courtesy amount, or E13B code. The check recognition algorithm consists of four steps: preprocessing, check layout analysis, segmentation and recognition, and information fusion. For layout analysis, an adaptive template matching algorithm is presented to locate the target recognition regions on the check. The hidden markov model is used to segment and recognize legal amount. Courtesy and E13B code are recognized by artificial neural network method, respectively. Finally, D-S evidence theory is then introduced to fuse above three recognition results for better recognition performance. Experimental results demonstrate that the system can robustly recognize checks and the information fusion based algorithm improves the recognition rate by 5~10 percent.

  20. 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.

  1. Do we need STRFs for cocktail parties? On the relevance of physiologically motivated features for human speech perception derived from automatic speech recognition.

    PubMed

    Kollmeier, B; Schädler, M R René; Meyer, A; Anemüller, J; Meyer, B T

    2013-01-01

    Complex auditory features such as spectro-temporal receptive fields (STRFs) derived from the cortical auditory neurons appear to be advantageous in sound processing. However, their physiological and functional relevance is still unclear. To assess the utility of such feature processing for speech reception in noise, automatic speech recognition (ASR) performance using feature sets obtained from physiological and/or psychoacoustical data and models is compared to human performance. Time-frequency representations with a nonlinear compression are compared with standard features such as mel-scaled spectrograms. Both alternatives serve as an input to model estimators that infer spectro-temporal filters (and subsequent nonlinearity) from physiological measurements in auditory brain areas of zebra finches. Alternatively, a filter bank of 2-dimensional Gabor functions is employed, which covers a wide range of modulation frequencies in the time and frequency domain. The results indicate a clear increase in ASR robustness using complex features (modeled by Gabor functions), while the benefit from physiologically derived STRFs is limited. In all cases, the use of power-normalized spectral representations increases performance, indicating that substantial dynamic compression is advantageous for level-independent pattern recognition. The methods employed may help physiologists to look for more relevant STRFs and to better understand specific differences in estimated STRFs.

  2. The software for automatic creation of the formal grammars used by speech recognition, computer vision, editable text conversion systems, and some new functions

    NASA Astrophysics Data System (ADS)

    Kardava, Irakli; Tadyszak, Krzysztof; Gulua, Nana; Jurga, Stefan

    2017-02-01

    For more flexibility of environmental perception by artificial intelligence it is needed to exist the supporting software modules, which will be able to automate the creation of specific language syntax and to make a further analysis for relevant decisions based on semantic functions. According of our proposed approach, of which implementation it is possible to create the couples of formal rules of given sentences (in case of natural languages) or statements (in case of special languages) by helping of computer vision, speech recognition or editable text conversion system for further automatic improvement. In other words, we have developed an approach, by which it can be achieved to significantly improve the training process automation of artificial intelligence, which as a result will give us a higher level of self-developing skills independently from us (from users). At the base of our approach we have developed a software demo version, which includes the algorithm and software code for the entire above mentioned component's implementation (computer vision, speech recognition and editable text conversion system). The program has the ability to work in a multi - stream mode and simultaneously create a syntax based on receiving information from several sources.

  3. Automatic white matter lesion segmentation using an adaptive outlier detection method.

    PubMed

    Ong, Kok Haur; Ramachandram, Dhanesh; Mandava, Rajeswari; Shuaib, Ibrahim Lutfi

    2012-07-01

    White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box-whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12×10(-3)) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the proposed approach is compared against leading lesion segmentation algorithms using a benchmark dataset.

  4. 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.

  5. [CT image segmentation based on automatic adaptive minimal fuzzy entropy measure].

    PubMed

    Gong, Guifang; Feng, Chengde; Zhang, Hui; Zhu, Yanfang

    2008-04-01

    In order to extract the anatomical feature of several tissues from CT image and solve the contradiction between the improvement of searching speed and the instability of results,we propose a method for image segmentation using auto adaptive minimal fuzzy entropy measure. Firstly, to find the optimal threshoding for segmenting image, the values of the exponent parameters of membership function of fuzzy subsets and the range of the searching thresholding values can be determined by using the iterative approach and the image histogram, and then the thresholding of minimizing the fuzzy entropy is implemented by searching all possible combinations of every thresholding in determinate searching range. The experiment results show that our proposed method facilitates good performance for CT image segmentation. The searching speed is quick, the segmented images show more details, and the results of many runs are steadier than those obtained by using genetic algorithm or simulated annealing algorithm.

  6. 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.

  7. Molecular adaptation in flowering and symbiotic recognition pathways: insights from patterns of polymorphism in the legume Medicago truncatula

    PubMed Central

    2011-01-01

    Background We studied patterns of molecular adaptation in the wild Mediterranean legume Medicago truncatula. We focused on two phenotypic traits that are not functionally linked: flowering time and perception of symbiotic microbes. Phenology is an important fitness component, especially for annual plants, and many instances of molecular adaptation have been reported for genes involved in flowering pathways. While perception of symbiotic microbes is also integral to adaptation in many plant species, very few reports of molecular adaptation exist for symbiotic genes. Here we used data from 57 individuals and 53 gene fragments to quantify the overall strength of both positive and purifying selection in M. truncatula and asked if footprints of positive selection can be detected at key genes of rhizobia recognition pathways. Results We examined nucleotide variation among 57 accessions from natural populations in 53 gene fragments: 5 genes involved in nitrogen-fixing bacteria recognition, 11 genes involved in flowering, and 37 genes used as control loci. We detected 1757 polymorphic sites yielding an average nucleotide diversity (pi) of 0.003 per site. Non-synonymous variation is under sizable purifying selection with 90% of amino-acid changing mutations being strongly selected against. Accessions were structured in two groups consistent with geographical origins. Each of these two groups harboured an excess of rare alleles, relative to expectations of a constant-sized population, suggesting recent population expansion. Using coalescent simulations and an approximate Bayesian computation framework we detected several instances of genes departing from selective neutrality within each group and showed that the polymorphism of two nodulation and four flowering genes has probably been shaped by recent positive selection. Conclusion We quantify the intensity of purifying selection in the M. truncatula genome and show that putative footprints of natural selection can be

  8. 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.; ...

    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

  9. 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.

  10. The Development of Adaptive Decision Making: Recognition-Based Inference in Children and Adolescents

    ERIC Educational Resources Information Center

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

    2016-01-01

    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,…

  11. 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

  12. 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.

  13. 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

  14. 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

  15. Automatic shape recognition of human limbs to avoid errors due to skin marker shifting in motion analysis

    NASA Astrophysics Data System (ADS)

    Hatze, Herbert; Baca, Arnold

    1991-12-01

    A new method in human motion analysis is presented for overcoming the problem of the shifting of skin-mounted position markers relative to the skeleton. The present version of the method is based on two-dimensional video processing and involves the recording of subjects wearing special clothing. The clothing is designed in such a way as to permit the unambiguous spatial shape recognition of each of the 17 body segments by means of an edge detection algorithm. The latter and the algorithms for the computation of segment translation and rotation constitute improved versions of previously used algorithms, especially with respect to the execution times of the respective computer program on ordinary PCs. From the recognized shapes, the translation and rotation of each segment relative to its initial configuration is computed by using positional information from the previous frames. For the first frame to be analyzed, a starting algorithm has to be applied. Finally, the configurational coordinates of the body model are calculated from the respective spatial linear and angular positions.

  16. 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

  17. Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine.

    PubMed

    Liu, Yi-Hung; Wu, Chien-Te; Kao, Yung-Hwa; Chen, Ya-Ting

    2013-01-01

    Single-trial electroencephalography (EEG)-based emotion recognition enables us to perform fast and direct assessments of human emotional states. However, previous works suggest that a great improvement on the classification accuracy of valence and arousal levels is still needed. To address this, we propose a novel emotional EEG feature extraction method: kernel Eigen-emotion pattern (KEEP). An adaptive SVM is also proposed to deal with the problem of learning from imbalanced emotional EEG data sets. In this study, a set of pictures from IAPS are used for emotion induction. Results based on seven participants show that KEEP gives much better classification results than the widely-used EEG frequency band power features. Also, the adaptive SVM greatly improves classification performance of commonly-adopted SVM classifier. Combined use of KEEP and adaptive SVM can achieve high average valence and arousal classification rates of 73.42% and 73.57%. The highest classification rates for valence and arousal are 80% and 79%, respectively. The results are very promising.

  18. Assessing Hippocampal Development and Language in Early Childhood: Evidence From a New Application of the Automatic Segmentation Adapter Tool

    PubMed Central

    Lee, Joshua K.; Nordahl, Christine W.; Amaral, David G.; Lee, Aaron; Solomon, Marjorie; Ghetti, Simona

    2016-01-01

    Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure. PMID:26279309

  19. Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool.

    PubMed

    Lee, Joshua K; Nordahl, Christine W; Amaral, David G; Lee, Aaron; Solomon, Marjorie; Ghetti, Simona

    2015-11-01

    Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure.

  20. Towards the Development of a Comprehensive Pedagogical Framework for Pronunciation Training Based on Adapted Automatic Speech Recognition Systems

    ERIC Educational Resources Information Center

    Ali, Saandia

    2016-01-01

    This paper reports on the early stages of a locally funded research and development project taking place at Rennes 2 university. It aims at developing a comprehensive pedagogical framework for pronunciation training for adult learners of English. This framework will combine a direct approach to pronunciation training (face-to-face teaching) with…

  1. Image set based face recognition using self-regularized non-negative coding and adaptive distance metric learning.

    PubMed

    Mian, Ajmal; Hu, Yiqun; Hartley, Richard; Owens, Robyn

    2013-12-01

    Simple nearest neighbor classification fails to exploit the additional information in image sets. We propose self-regularized nonnegative coding to define between set distance for robust face recognition. Set distance is measured between the nearest set points (samples) that can be approximated from their orthogonal basis vectors as well as from the set samples under the respective constraints of self-regularization and nonnegativity. Self-regularization constrains the orthogonal basis vectors to be similar to the approximated nearest point. The nonnegativity constraint ensures that each nearest point is approximated from a positive linear combination of the set samples. Both constraints are formulated as a single convex optimization problem and the accelerated proximal gradient method with linear-time Euclidean projection is adapted to efficiently find the optimal nearest points between two image sets. Using the nearest points between a query set and all the gallery sets as well as the active samples used to approximate them, we learn a more discriminative Mahalanobis distance for robust face recognition. The proposed algorithm works independently of the chosen features and has been tested on gray pixel values and local binary patterns. Experiments on three standard data sets show that the proposed method consistently outperforms existing state-of-the-art methods.

  2. 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.

  3. 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.

  4. Structured light 3D depth map enhancement and gesture recognition using image content adaptive filtering

    NASA Astrophysics Data System (ADS)

    Ramachandra, Vikas; Nash, James; Atanassov, Kalin; Goma, Sergio

    2013-03-01

    A structured-light system for depth estimation is a type of 3D active sensor that consists of a structured-light projector that projects an illumination pattern on the scene (e.g. mask with vertical stripes) and a camera which captures the illuminated scene. Based on the received patterns, depths of different regions in the scene can be inferred. In this paper, we use side information in the form of image structure to enhance the depth map. This side information is obtained from the received light pattern image reflected by the scene itself. The processing steps run real time. This post-processing stage in the form of depth map enhancement can be used for better hand gesture recognition, as is illustrated in this paper.

  5. Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition.

    PubMed

    Lin, Jia; Ruan, Xiaogang; Yu, Naigong; Yang, Yee-Hong

    2016-12-17

    Noise and constant empirical motion constraints affect the extraction of distinctive spatiotemporal features from one or a few samples per gesture class. To tackle these problems, an adaptive local spatiotemporal feature (ALSTF) using fused RGB-D data is proposed. First, motion regions of interest (MRoIs) are adaptively extracted using grayscale and depth velocity variance information to greatly reduce the impact of noise. Then, corners are used as keypoints if their depth, and velocities of grayscale and of depth meet several adaptive local constraints in each MRoI. With further filtering of noise, an accurate and sufficient number of keypoints is obtained within the desired moving body parts (MBPs). Finally, four kinds of multiple descriptors are calculated and combined in extended gradient and motion spaces to represent the appearance and motion features of gestures. The experimental results on the ChaLearn gesture, CAD-60 and MSRDailyActivity3D datasets demonstrate that the proposed feature achieves higher performance compared with published state-of-the-art approaches under the one-shot learning setting and comparable accuracy under the leave-one-out cross validation.

  6. Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition

    PubMed Central

    Lin, Jia; Ruan, Xiaogang; Yu, Naigong; Yang, Yee-Hong

    2016-01-01

    Noise and constant empirical motion constraints affect the extraction of distinctive spatiotemporal features from one or a few samples per gesture class. To tackle these problems, an adaptive local spatiotemporal feature (ALSTF) using fused RGB-D data is proposed. First, motion regions of interest (MRoIs) are adaptively extracted using grayscale and depth velocity variance information to greatly reduce the impact of noise. Then, corners are used as keypoints if their depth, and velocities of grayscale and of depth meet several adaptive local constraints in each MRoI. With further filtering of noise, an accurate and sufficient number of keypoints is obtained within the desired moving body parts (MBPs). Finally, four kinds of multiple descriptors are calculated and combined in extended gradient and motion spaces to represent the appearance and motion features of gestures. The experimental results on the ChaLearn gesture, CAD-60 and MSRDailyActivity3D datasets demonstrate that the proposed feature achieves higher performance compared with published state-of-the-art approaches under the one-shot learning setting and comparable accuracy under the leave-one-out cross validation. PMID:27999337

  7. Toward the ultimate synthesis/recognition system.

    PubMed

    Furui, S

    1995-10-24

    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.

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

    PubMed Central

    Sintes, Jordi; Polentarutti, Nadia; Walland, A. Cooper; Yeiser, John R.; Cunha, Cristina; Lacerda, João F.; Salvatori, Giovanni; Blander, J. Magarian

    2016-01-01

    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

  9. Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

    NASA Astrophysics Data System (ADS)

    Lubey, D.; Scheeres, D.

    Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal

  10. 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

  11. 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

    2016-01-07

    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.

  12. Three-dimensional modeling of a thermal dendrite using the phase field method with automatic anisotropic and unstructured adaptive finite element meshing

    NASA Astrophysics Data System (ADS)

    Sarkis, C.; Silva, L.; Gandin, Ch-A.; Plapp, M.

    2016-03-01

    Dendritic growth is computed with automatic adaptation of an anisotropic and unstructured finite element mesh. The energy conservation equation is formulated for solid and liquid phases considering an interface balance that includes the Gibbs-Thomson effect. An equation for a diffuse interface is also developed by considering a phase field function with constant negative value in the liquid and constant positive value in the solid. Unknowns are the phase field function and a dimensionless temperature, as proposed by [1]. Linear finite element interpolation is used for both variables, and discretization stabilization techniques ensure convergence towards a correct non-oscillating solution. In order to perform quantitative computations of dendritic growth on a large domain, two additional numerical ingredients are necessary: automatic anisotropic unstructured adaptive meshing [2,[3] and parallel implementations [4], both made available with the numerical platform used (CimLib) based on C++ developments. Mesh adaptation is found to greatly reduce the number of degrees of freedom. Results of phase field simulations for dendritic solidification of a pure material in two and three dimensions are shown and compared with reference work [1]. Discussion on algorithm details and the CPU time will be outlined.

  13. Restricted Autoantigen Recognition Associated With Deletional and Adaptive Regulatory Mechanisms1

    PubMed Central

    Gebe, John A.; Yue, Betty B.; Unrath, Kelly A; Falk, Ben A.; Nepom, Gerald T.

    2009-01-01

    Autoimmune diabetes (T1D) is characterized by CD4+ T cell reactivity to a variety of islet-associated antigens. At-risk individuals, genetically predisposed to T1D, often have similar T cell reactivity, but nevertheless fail to progress to clinically overt disease. In order to study the immune tolerance and regulatory environment permissive for such autoreactive T cells, we expressed TcR transgenes derived from two autoreactive human T cells, 4.13 and 164, in HLA-DR4 transgenic mice on a C57Bl/6-derived “diabetes-resistant” background. Both TcR are responsive to an immunodominant epitope of glutamic acid decarboxylase 65 (555–567), which is identical in sequence between humans and mice, is restricted by HLA-DR4, and is a naturally processed self antigen associated with T1D. Although both TcR use the identical Vα and Vβ genes, differing only in CDR3, we found stark differences in the mechanisms utilized in vivo in the maintenance of immune tolerance. A combination of thymic deletion (negative selection), TcR down-regulation, and peripheral activation-induced cell death dominated the phenotype of 164 T cells, which nevertheless still maintain their antigen responsiveness in the periphery. In contrast, 4.13 T cells are much less influenced by central and deletional tolerance mechanisms, and instead display a peripheral immune deviation including differentiation into IL-10 secreting Tr1 cells. These findings indicate a distinct set of regulatory alternatives for autoreactive T cells, even within a single highly restricted HLA-peptide-TcR recognition profile. PMID:19535636

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

    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.

  15. 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.

  16. 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

    2016-09-15

    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.

  17. 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

  18. An adaptive 3D region growing algorithm to automatically segment and identify thoracic aorta and its centerline using computed tomography angiography scans

    NASA Astrophysics Data System (ADS)

    Ferreira, F.; Dehmeshki, J.; Amin, H.; Dehkordi, M. E.; Belli, A.; Jouannic, A.; Qanadli, S.

    2010-03-01

    Thoracic Aortic Aneurysm (TAA) is a localized swelling of the thoracic aorta. The progressive growth of an aneurysm may eventually cause a rupture if not diagnosed or treated. This necessitates the need for an accurate measurement which in turn calls for the accurate segmentation of the aneurysm regions. Computer Aided Detection (CAD) is a tool to automatically detect and segment the TAA in the Computer tomography angiography (CTA) images. The fundamental major step of developing such a system is to develop a robust method for the detection of main vessel and measuring its diameters. In this paper we propose a novel adaptive method to simultaneously segment the thoracic aorta and to indentify its center line. For this purpose, an adaptive parametric 3D region growing is proposed in which its seed will be automatically selected through the detection of the celiac artery and the parameters of the method will be re-estimated while the region is growing thorough the aorta. At each phase of region growing the initial center line of aorta will also be identified and modified through the process. Thus the proposed method simultaneously detect aorta and identify its centerline. The method has been applied on CT images from 20 patients with good agreement with the visual assessment by two radiologists.

  19. 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.

  20. Adaptive system for automatic stabilization of the power factor for electric drives of separation device by means of serially connected capacitors bank

    NASA Astrophysics Data System (ADS)

    Borisevich, V. D.; Juromskiy, V. M.

    2016-09-01

    A method for designing adaptive systems for automatic extremum search to stabilize the power factor of local electric power system of electric is considered. It consists in application of the serially connected capacitors compensating the reactive component of the total electric power of in parallel connected centrifugal machines usually called as an aggregate. Operation of the system just demands measuring voltage at the output of the static frequency converter for electric drives. The proposed control system is designed to stabilize the power factor close to unity in a case of alteration of parameters of a separation cascade or a single separation device in an aggregate. Such system can be operated continuously or connected occasionally depending on a technological situation. In addition, it totally excludes the phenomenon of overcompensation.

  1. Methods for high-resolution anisotropic finite element modeling of the human head: automatic MR white matter anisotropy-adaptive mesh generation.

    PubMed

    Lee, Won Hee; Kim, Tae-Seong

    2012-01-01

    This study proposes an advanced finite element (FE) head modeling technique through which high-resolution FE meshes adaptive to the degree of tissue anisotropy can be generated. Our adaptive meshing scheme (called wMesh) uses MRI structural information and fractional anisotropy maps derived from diffusion tensors in the FE mesh generation process, optimally reflecting electrical properties of the human brain. We examined the characteristics of the wMeshes through various qualitative and quantitative comparisons to the conventional FE regular-sized meshes that are non-adaptive to the degree of white matter anisotropy. We investigated numerical differences in the FE forward solutions that include the electrical potential and current density generated by current sources in the brain. The quantitative difference was calculated by two statistical measures of relative difference measure (RDM) and magnification factor (MAG). The results show that the wMeshes are adaptive to the anisotropic density of the WM anisotropy, and they better reflect the density and directionality of tissue conductivity anisotropy. Our comparison results between various anisotropic regular mesh and wMesh models show that there are substantial differences in the EEG forward solutions in the brain (up to RDM=0.48 and MAG=0.63 in the electrical potential, and RDM=0.65 and MAG=0.52 in the current density). Our analysis results indicate that the wMeshes produce different forward solutions that are different from the conventional regular meshes. We present some results that the wMesh head modeling approach enhances the sensitivity and accuracy of the FE solutions at the interfaces or in the regions where the anisotropic conductivities change sharply or their directional changes are complex. The fully automatic wMesh generation technique should be useful for modeling an individual-specific and high-resolution anisotropic FE head model incorporating realistic anisotropic conductivity distributions

  2. Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

    PubMed

    Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

    2014-06-01

    Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

  3. Foreign Language Analysis and Recognition (FLARE) Progress

    DTIC Science & Technology

    2015-02-01

    Cleared 29 April 2015 14. ABSTRACT This interim report provides research results in the areas of automatic speech recognition (ASR) and information...retrieval (IR). 15. SUBJECT TERMS Automatic speech recognition (ASR), information retrieval (IR). 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...period 1 October 2012 to 30 November 2014 under contract FA8650-09-D-6939. The following tasks were completed on Automatic Speech Recognition (ASR

  4. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  5. 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

  6. 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…

  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 Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

    PubMed

    Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Yan, Bin; Li, Jianxin

    2015-01-01

    Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

  9. 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).

  10. 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.

  11. 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"…

  12. TU-AB-201-05: Automatic Adaptive Per-Operative Re-Planning for HDR Prostate Brachytherapy - a Simulation Study On Errors in Needle Positioning

    SciTech Connect

    Borot de Battisti, M; Maenhout, M; Lagendijk, J J W; Van Vulpen, M; Moerland, M A; Senneville, B Denis de

    2015-06-15

    Purpose: To develop adaptive planning with feedback for MRI-guided focal HDR prostate brachytherapy with a single divergent needle robotic implant device. After each needle insertion, the dwell positions for that needle are calculated and the positioning of remaining needles and dosimetry are both updated based on MR imaging. Methods: Errors in needle positioning may occur due to inaccurate needle insertion (caused by e.g. the needle’s bending) and unpredictable changes in patient anatomy. Consequently, the dose plan quality might dramatically decrease compared to the preplan. In this study, a procedure was developed to re-optimize, after each needle insertion, the remaining needle angulations, source positions and dwell times in order to obtain an optimal coverage (D95% PTV>19 Gy) without exceeding the constraints of the organs at risk (OAR) (D10% urethra<21 Gy, D1cc bladder<12 Gy and D1cc rectum<12 Gy). Complete HDR procedures with 6 needle insertions were simulated for a patient MR-image set with PTV, prostate, urethra, bladder and rectum delineated. Random angulation errors, modeled by a Gaussian distribution (standard deviation of 3 mm at the needle’s tip), were generated for each needle insertion. We compared the final dose parameters for the situations (I) without re-optimization and (II) with the automatic feedback. Results: The computation time of replanning was below 100 seconds on a current desk computer. For the patient tested, a clinically acceptable dose plan was achieved while applying the automatic feedback (median(range) in Gy, D95% PTV: 19.9(19.3–20.3), D10% urethra: 13.4(11.9–18.0), D1cc rectum: 11.0(10.7–11.6), D1cc bladder: 4.9(3.6–6.8)). This was not the case without re-optimization (median(range) in Gy, D95% PTV: 19.4(14.9–21.3), D10% urethra: 12.6(11.0–15.7), D1cc rectum: 10.9(8.9–14.1), D1cc bladder: 4.8(4.4–5.2)). Conclusion: An automatic guidance strategy for HDR prostate brachytherapy was developed to compensate

  13. U2AF65 adapts to diverse pre-mRNA splice sites through conformational selection of specific and promiscuous RNA recognition motifs

    PubMed Central

    Jenkins, Jermaine L.; Agrawal, Anant A.; Gupta, Ankit; Green, Michael R.; Kielkopf, Clara L.

    2013-01-01

    Degenerate splice site sequences mark the intron boundaries of pre-mRNA transcripts in multicellular eukaryotes. The essential pre-mRNA splicing factor U2AF65 is faced with the paradoxical tasks of accurately targeting polypyrimidine (Py) tracts preceding 3′ splice sites while adapting to both cytidine and uridine nucleotides with nearly equivalent frequencies. To understand how U2AF65 recognizes degenerate Py tracts, we determined six crystal structures of human U2AF65 bound to cytidine-containing Py tracts. As deoxy-ribose backbones were required for co-crystallization with these Py tracts, we also determined two baseline structures of U2AF65 bound to the deoxy-uridine counterparts and compared the original, RNA-bound structure. Local structural changes suggest that the N-terminal RNA recognition motif 1 (RRM1) is more promiscuous for cytosine-containing Py tracts than the C-terminal RRM2. These structural differences between the RRMs were reinforced by the specificities of wild-type and site-directed mutant U2AF65 for region-dependent cytosine- and uracil-containing RNA sites. Small-angle X-ray scattering analyses further demonstrated that Py tract variations select distinct inter-RRM spacings from a pre-existing ensemble of U2AF65 conformations. Our results highlight both local and global conformational selection as a means for universal 3′ splice site recognition by U2AF65. PMID:23376934

  14. Automatic discourse connective detection in biomedical text

    PubMed Central

    Polepalli Ramesh, Balaji; Prasad, Rashmi; Miller, Tim; Harrington, Brian

    2012-01-01

    Objective Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. Materials and Methods Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (∼112 000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (∼1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores. Results and Conclusion Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://bioconn.askhermes.org. PMID:22744958

  15. Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

    PubMed

    Zhang, Jianhua; Yin, Zhong; Wang, Rubin

    2017-01-01

    This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

  16. Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load

    PubMed Central

    Zhang, Jianhua; Yin, Zhong; Wang, Rubin

    2017-01-01

    This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed. PMID:28367110

  17. 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

  18. Sparsity Motivated Automated Target Recognition

    DTIC Science & Technology

    2010-09-29

    been suggested for tasks such as face and iris recognition . In this project, we evaluated the effectiveness of such methods for automatic target...Sparsity-based methods have recently been suggested for tasks such as face and iris recognition . In this project, we evaluated the effectiveness of...have recently been suggested for tasks such as face and iris recognition . In this project, we evaluated the effectiveness of such methods for

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. Automatic trajectory clustering for generating ground truth data sets

    NASA Astrophysics Data System (ADS)

    Moehrmann, Julia; Heidemann, Gunther

    2010-01-01

    We present a novel approach towards the creation of vision based recognition tasks. A lot of domain specific recognition systems have been presented in the past which make use of the large amounts of available video data. The creation of ground truth data sets for the training of theses systems remains difficult and tiresome. We present a system which automatically creates clusters of 2D trajectories. The results of this clustering can then be used to perform the actual labeling of the data, or rather the selection of events or features of interest by the user. The selected clusters can be used as positive training data for a user defined recognition task - without the need to adapt the system. The proposed technique reduces the necessary user interaction and allows the creation of application independent ground truth data sets with minimal effort. In order to achieve the automatic clustering we have developed a distance metric based on the Hidden Markov Model representations of three sequences - movement, speed and orientation - derived from the initial trajectory. The proposed system yields promising results and could prove to be an important steps towards mining very large data sets.

  4. 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.

  5. Development and Utility of Automatic Language Processing Technologies. Volume 2

    DTIC Science & Technology

    2014-04-01

    translation (MT), natural language processing ( NLP ), speech synthesis (TTS) and other speech and language processing technologies. 15. SUBJECT TERMS...Automatic speech recognition (ASR), machine translation (MT), natural language processing ( NLP ), and speech synthesis (TTS). 16. SECURITY CLASSIFICATION OF...investigating the development and utility of Automatic Speech Recognition (ASR), Machine Translation (MT), Natural Language Processing ( NLP ), Speech Synthesis

  6. Adaptive radiation in Lesser Antillean lizards: molecular phylogenetics and species recognition in the Lesser Antillean dwarf gecko complex, Sphaerodactylus fantasticus.

    PubMed

    Thorpe, R S; Jones, A G; Malhotra, A; Surget-Groba, Y

    2008-03-01

    The time associated with speciation varies dramatically among lower vertebrates. The nature and timing of divergence is investigated in the fantastic dwarf gecko Sphaerodactylus fantasticus complex, a nominal species that occurs on the central Lesser Antillean island of Guadeloupe and adjacent islands and islets. This is compared to the divergence in the sympatric anole clade from the Anolis bimaculatus group. A molecular phylogenetic analysis of numerous gecko populations from across these islands, based on three mitochondrial DNA genes, reveals several monophyletic groups occupying distinct geographical areas, these being Les Saintes, western Basse Terre plus Dominica, eastern Basse Terre, Grand Terre, and the northern and eastern islands (Montserrat, Marie Galante, Petite Terre, Desirade). Although part of the same nominal species, the molecular divergence within this species complex is extraordinarily high (27% patristic distance between the most divergent lineages) and is compatible with this group occupying the region long before the origin of the younger island arc. Tests show that several quantitative morphological traits are correlated with the phylogeny, but in general the lineages are not uniquely defined by these traits. The dwarf geckos show notably less nominal species-level adaptive radiation than that found in the sympatric southern clade of Anolis bimculatus, although both appear to have occupied the region for a broadly similar period of time. Nevertheless, the dwarf gecko populations on Les Saintes islets are the most morphologically distinct and are recognized as a full species (Sphaerodactylus phyzacinus), as are anoles on Les Saintes (Anolis terraealtae).

  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. Adaptive infrared target detection

    NASA Astrophysics Data System (ADS)

    McBride, Jonah C.; Stevens, Mark R.; Eaton, Ross S.; Snorrason, Magnus S.

    2004-09-01

    Automatic Target Recognition (ATR) algorithms are extremely sensitive to differences between the operating conditions under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. These extended operating conditions can cause a target's signature to be drastically different from training exemplars/models. For example, a target's signature can be influenced by: the time of day, the time of year, the weather, atmospheric conditions, position of the sun or other illumination sources, the target surface and material properties, the target composition, the target geometry, sensor characteristics, sensor viewing angle and range, the target surroundings and environment, and the target and scene temperature. Recognition rates degrade if an ATR is not trained for a particular EOC. Most infrared target detection techniques are based on a very simple probabilistic theory. This theory states that a pixel should be assigned the label of "target" if a set of measurements (features) is more likely to have come from an assumed (or learned) distribution of target features than from the distribution of background features. However, most detection systems treat these learned distributions as static and they are not adapted to changing EOCs. In this paper, we present an algorithm for assigning a pixel the label of target or background based on a statistical comparison of the distributions of measurements surrounding that pixel in the image. This method provides a feature-level adaptation to changing EOCs. Results are demonstrated on infrared imagery containing several military vehicles.

  9. Automatic transmission

    SciTech Connect

    Miura, M.; Aoki, H.

    1988-02-02

    An automatic transmission is described comprising: an automatic transmission mechanism portion comprising a single planetary gear unit and a dual planetary gear unit; carriers of both of the planetary gear units that are integral with one another; an input means for inputting torque to the automatic transmission mechanism, clutches for operatively connecting predetermined ones of planetary gear elements of both of the planetary gear units to the input means and braking means for restricting the rotation of predetermined ones of planetary gear elements of both of the planetary gear units. The clutches are disposed adjacent one another at an end portion of the transmission for defining a clutch portion of the transmission; a first clutch portion which is attachable to the automatic transmission mechanism portion for comprising the clutch portion when attached thereto; a second clutch portion that is attachable to the automatic transmission mechanism portion in place of the first clutch portion for comprising the clutch portion when so attached. The first clutch portion comprising first clutch for operatively connecting the input means to a ring gear of the single planetary gear unit and a second clutch for operatively connecting the input means to a single gear of the automatic transmission mechanism portion. The second clutch portion comprising a the first clutch, the second clutch, and a third clutch for operatively connecting the input member to a ring gear of the dual planetary gear unit.

  10. AUTOMATIC NAVIGATION.

    DTIC Science & Technology

    NAVIGATION, REPORTS), (*CONTROL SYSTEMS, *INFORMATION THEORY), ABSTRACTS, OPTIMIZATION, DYNAMIC PROGRAMMING, GAME THEORY, NONLINEAR SYSTEMS, CORRELATION TECHNIQUES, FOURIER ANALYSIS, INTEGRAL TRANSFORMS, DEMODULATION, NAVIGATION CHARTS, PATTERN RECOGNITION, DISTRIBUTION THEORY , TIME SHARING, GRAPHICS, DIGITAL COMPUTERS, FEEDBACK, STABILITY

  11. Time series modeling for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Sokolnikov, Andre

    2012-05-01

    Time series modeling is proposed for identification of targets whose images are not clearly seen. The model building takes into account air turbulence, precipitation, fog, smoke and other factors obscuring and distorting the image. The complex of library data (of images, etc.) serving as a basis for identification provides the deterministic part of the identification process, while the partial image features, distorted parts, irrelevant pieces and absence of particular features comprise the stochastic part of the target identification. The missing data approach is elaborated that helps the prediction process for the image creation or reconstruction. The results are provided.

  12. Dismount Threat Recognition through Automatic Pose Identification

    DTIC Science & Technology

    2012-03-01

    camera and joint estimation software of the Kinect for Xbox 360. A threat determination is made based on the pose identified by the network. Ac- curacy...The automobile industry continues to refine pedestrian avoidance systems to assist drivers [14,15]. Microsoft’s Kinect for Xbox 360 [3] uses real-time...Conference on Acoustics, Speech and Signal Process- ing , Vol. 4, April 2007, pp. IV–21 – IV–24. 3. Kinect for Xbox 360 , Microsoft Corp., Redmond WA. 4

  13. Automatic Pattern Recognition and Related Topics.

    DTIC Science & Technology

    1987-01-01

    compared: one is a lensless Fresnel-zone system and the other uses a Fourier achromat. Both are opto-electronic hybrids with a twin-imaging...Fourier transforms are obtained for objects illuminated by spatially incoherent, bandlimited light. A lensless Fresnel zone processor consisting of a...agreement between theory and experiment is achieved. A diffraction theory topic of fundamental importance to sub-resolution in microscopy (or in

  14. Automatic Target Recognition for Hyperspectral Imagery

    DTIC Science & Technology

    2012-03-01

    tanks, T-72 Soviet tanks, and HMMWVs with woodland camouflage . Now that an atmospherically compensated radiance signature exists for each item in the... Symposium , Vol. 5, pp. 3379-..82. Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2011). Sparse Representation for Target Detection in Hyperspectral... Radar , Out-of-Library Identification, and Non-Declarations. PhD Thesis AFIT-DS-ENS-07-04, Air Force Institute of Technology, WPAFB. Fumera, G., Roli

  15. 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).

  16. Automatic Target Recognition Classification System Evaluation Methodology

    DTIC Science & Technology

    2002-09-01

    Testing Example (α=0.1) ....................... 2-16 2.9 Binormal 2AFC ROC Curves...2-17 2.10 Target and Non-target Normal pdfs for a 2AFC Task....................................... 2-20 2.11 Sample N-N ROC Curve...2-23 2.13 Operating Curve Derived from 2AFC Task....................................................... 2-28 2.14 Example

  17. Automatic Modeling and Localization for Object Recognition

    DTIC Science & Technology

    1996-10-25

    here including Kevin Lynch , Tammy Abel, Greg Morrissett, Andrzej Filinski, Lars Birkedal, Margaret Reid-Miller, and John Ockerbloom. Grad school would...Dave Tarditi, Greg and Jack Morrissett, Susan Hinrichs, Alan Carroll, Mei Wang, Kevin Lynch , and Tammy Abel, which shared many fun times together. I also...have been much more difficult without the many of the friends I have made here. I am most grateful for having shared an office and house with Kevin

  18. Reverberant speech recognition exploiting clarity index estimation

    NASA Astrophysics Data System (ADS)

    Parada, Pablo Peso; Sharma, Dushyant; Naylor, Patrick A.; Waterschoot, Toon van

    2015-12-01

    We present single-channel approaches to robust automatic speech recognition (ASR) in reverberant environments based on non-intrusive estimation of the clarity index ( C 50). Our best performing method includes the estimated value of C 50 in the ASR feature vector and also uses C 50 to select the most suitable ASR acoustic model according to the reverberation level. We evaluate our method on the REVERB Challenge database employing two different C 50 estimators and show that our method outperforms the best baseline of the challenge achieved without unsupervised acoustic model adaptation, i.e. using multi-condition hidden Markov models (HMMs). Our approach achieves a 22.4 % relative word error rate reduction in comparison to the best baseline of the challenge.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Presentation video retrieval using automatically recovered slide and spoken text

    NASA Astrophysics Data System (ADS)

    Cooper, Matthew

    2013-03-01

    Video is becoming a prevalent medium for e-learning. Lecture videos contain text information in both the presentation slides and lecturer's speech. This paper examines the relative utility of automatically recovered text from these sources for lecture video retrieval. To extract the visual information, we automatically detect slides within the videos and apply optical character recognition to obtain their text. Automatic speech recognition is used similarly to extract spoken text from the recorded audio. We perform controlled experiments with manually created ground truth for both the slide and spoken text from more than 60 hours of lecture video. We compare the automatically extracted slide and spoken text in terms of accuracy relative to ground truth, overlap with one another, and utility for video retrieval. Results reveal that automatically recovered slide text and spoken text contain different content with varying error profiles. Experiments demonstrate that automatically extracted slide text enables higher precision video retrieval than automatically recovered spoken text.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

    PubMed

    Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

    2016-01-29

    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.

  9. 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.

  10. 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.

  11. 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.

  12. Automatic transmission

    SciTech Connect

    Hamane, M.; Ohri, H.

    1989-03-21

    This patent describes an automatic transmission connected between a drive shaft and a driven shaft and comprising: a planetary gear mechanism including a first gear driven by the drive shaft, a second gear operatively engaged with the first gear to transmit speed change output to the driven shaft, and a third gear operatively engaged with the second gear to control the operation thereof; centrifugally operated clutch means for driving the first gear and the second gear. It also includes a ratchet type one-way clutch for permitting rotation of the third gear in the same direction as that of the drive shaft but preventing rotation in the reverse direction; the clutch means comprising a ratchet pawl supporting plate coaxially disposed relative to the drive shaft and integrally connected to the third gear, the ratchet pawl supporting plate including outwardly projection radial projections united with one another at base portions thereof.

  13. Automatic transmission

    SciTech Connect

    Meyman, U.

    1987-03-10

    An automatic transmission is described comprising wheel members each having discs defining an inner space therebetween; turnable blades and vane members located in the inner space between the discs of at least one of the wheel members, the turnable blades being mechanically connected with the vane members. Each of the turnable blades has an inner surface and an outer surface formed by circular cylindrical surfaces having a common axis, each of the turnable blades being turnable about the common axis of the circular cylindrical surfaces forming the inner and outer surfaces of the respective blade; levers turnable about the axes and supporting the blades; the discs having openings extending coaxially with the surfaces which describe the blades. The blades are partially received in the openings of the discs; and a housing accommodating the wheel members and the turnable blades and the vane members.

  14. Designing a Knowledge Base for Automatic Book Classification.

    ERIC Educational Resources Information Center

    Kim, Jeong-Hyen; Lee, Kyung-Ho

    2002-01-01

    Reports on the design of a knowledge base for an automatic classification in the library science field by using the facet classification principles of colon classification. Discusses inputting titles or key words into the computer to create class numbers through automatic subject recognition and processing title key words. (Author/LRW)

  15. 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.

  16. Automatic detection and recognition of the first-arrival phase of seismic-event signals contaminated by noise. The curious case of the missing explosion. Final report, 1 February 1985-31 January 1987

    SciTech Connect

    Alden, K.A.; Herrin, E.

    1987-06-29

    Two papers are based upon data collected at the Lajitas Seismic Station. In the first paper a historical perspective on the methods that have been used to automatically detect event signals and pick first arrival times is developed. Then the data set used to characterize and test the detection techniques is described and the features that discriminate seismic signals effectively from the background noise are characterized. Two automatic detection methods are investigated: 1) The AR (8)-Spectral estimates of the signal and noise are used to develop the AR(p)-Spectral estimate for a synthetic waveform; and 2) Several non-parametric tests are employed in a time-domain detector to discriminate event signals from background. The non-parametric detector chosen, RANK 2700, employs a modified rank sum test to locate the seismic event and pick its first arrival time. Errors in the automatic first arrival picks for 152 of the event traces in the data set are used to analyze the performance. The second paper presents a scenario for a clandestine, decoupled nuclear explosion in southeastern New Mexico based upon data recorded at Lajitas and other stations at regional distances from the hypothetical clandestine test.

  17. 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.

  18. 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.

  19. 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.

  20. Atmospheric propagation effects on pattern recognition by neural networks

    NASA Astrophysics Data System (ADS)

    Giever, John C.; Hoock, Donald W., Jr.

    1991-07-01

    Smart electro-optical systems of the future will need to be adaptive and robust to function in different environments. In 1989 the authors reported how atmospheric losses in contrast, resolution, edge detail, and signal to noise adversely affect image-based classification using linear matched filters and how the atmosphere alters features such as gray-level moments. They also showed that the performance changes with atmospheric path radiance and transmittance are predictable, however, and that some effects can be mitigated automatically by including the atmosphere as a separate training class. This paper extends that analysis to atmospheric effects on pattern recognition by neural network classifiers. The neural net pattern recognition methods considered here are single- and multi-layer perceptron networks trained with back-propagation. Image classifier performance under different atmospheric propagation conditions is shown to be easily predicted for simple single-layer neural nets. This leads to a specific training strategy to minimize the impact of propagation losses by including the atmosphere as a separate training class. This same strategy also improves the performance of multi-layer neural networks. Examples are given of classification of a vehicle partly obscured by highly scattering white smoke and highly absorptive black smoke. Other methods are being investigated that affect the performance and training convergence properties of neural net pattern recognition in atmospheres.

  1. Photonic correlator pattern recognition: Application to autonomous docking

    NASA Technical Reports Server (NTRS)

    Sjolander, Gary W.

    1991-01-01

    Optical correlators for real-time automatic pattern recognition applications have recently become feasible due to advances in high speed devices and filter formulation concepts. The devices are discussed in the context of their use in autonomous docking.

  2. Offline Arabic handwriting recognition: a survey.

    PubMed

    Lorigo, Liana M; Govindaraju, Venu

    2006-05-01

    The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

  3. 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%.

  4. [Research on automatic external defibrillator based on DSP].

    PubMed

    Jing, Jun; Ding, Jingyan; Zhang, Wei; Hong, Wenxue

    2012-10-01

    Electrical defibrillation is the most effective way to treat the ventricular tachycardia (VT) and ventricular fibrillation (VF). An automatic external defibrillator based on DSP is introduced in this paper. The whole design consists of the signal collection module, the microprocessor controlingl module, the display module, the defibrillation module and the automatic recognition algorithm for VF and non VF, etc. This automatic external defibrillator has achieved goals such as ECG signal real-time acquisition, ECG wave synchronous display, data delivering to U disk and automatic defibrillate when shockable rhythm appears, etc.

  5. Nonlinear filter for pattern recognition invariant to illumination and to out-of-plane rotations.

    PubMed

    Lefebvre, Daniel; Arsenault, Henri H; Roy, Sébastien

    2003-08-10

    Automatic target recognition in uncontrolled conditions is a difficult task because many parametersare involved. This study deals with the recognition of targets under limited out-of-plane rotations while maintaining invariance to ambient light illumination. Contrast invariance is achieved by using the recently developed locally adaptive contrast-invariant filter, a method that yields correlation peaks whose values are invariant under any linear transformation of intensity. To reduce the sensitivity to the orientation of the object we replace the reference in the nonlinear filter by a synthetic discriminant filter. The range used for out-of-plane rotations was 40 degrees with a depression angle of 20 degrees. We present results for unsegmented targets on complex backgrounds with the presence of false targets.

  6. A Bayesian view on acoustic model-based techniques for robust speech recognition

    NASA Astrophysics Data System (ADS)

    Maas, Roland; Huemmer, Christian; Sehr, Armin; Kellermann, Walter

    2015-12-01

    This article provides a unifying Bayesian view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By identifying and converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules. We thus summarize the various approaches as approximations or modifications of the same Bayesian decoding rule leading to a unified view on known derivations as well as to new formulations for certain approaches.

  7. Automatic tools for microprocessor failure analysis

    NASA Astrophysics Data System (ADS)

    Conard, Didier; Laurent, J.; Velazco, Raoul; Ziade, Haissam; Cabestany, J.; Sala, F.

    A new approach for fault location when testing microprocessors is presented. The startpoint for the backtracing analysis converging to the failure is constituted by the automatic localization of a reduced area. Automatic image comparison based on pattern recognition is performed by means of an electron beam tester. The developed hardware and software tools allow large circuit areas to be covered offering powerful diagnosis capabilities to the user. The validation of this technique was performed on faulty 68000 microprocessors. It shows the feasibility of the automation of the first and most important step of failure analysis: fault location at the chip surface.

  8. 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

  9. 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

  10. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.

    PubMed

    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.

  11. Workshop on Standards for Image Pattern Recognition. Computer Seience & Technology Series.

    ERIC Educational Resources Information Center

    Evans, John M. , Ed.; And Others

    Automatic image pattern recognition techniques have been successfully applied to improving productivity and quality in both manufacturing and service applications. Automatic Image Pattern Recognition Algorithms are often developed and tested using unique data bases for each specific application. Quantitative comparison of different approaches and…

  12. Online and Unsupervised Face Recognition for Humanoid Robot: Toward Relationship with People

    DTIC Science & Technology

    2001-01-01

    under Continuous Video Stream. Fourth International Conference on Automatic Face and Gesture Recognition , Grenoble, France (2000). [11] A. Pentland, T...Yacoob, H. Lam, L. Davis, Recognizing Faces With Expression. International Workshop on Automatic Face and Gesture Recognition , Zurich (1995). [14] P

  13. Automatic Adaptation of Tunable Distributed Applications

    DTIC Science & Technology

    2001-01-01

    i.e., function name). Windows NT also allows a process to access the memory im- age of another and to inject a thread into the latter. Together...of application resource us- age as if only the prescribed amount is available to their execution, we can use it to emulate different resource scenarios...decompresses the data, and updates the local image on display. resolution and progressive transmission techniques to improve performance. First, im- ages

  14. Segmentation-free ocular detection and recognition

    NASA Astrophysics Data System (ADS)

    Rodriguez, Andres; Panza, Jeffrey; Vijaya Kumar, B. V. K.

    2011-06-01

    Iris recognition is a well-known technique to identify persons. However this technique requires high resolution images in order to automatically segment the iris. In some scenarios obtaining the required resolution may be difficult. In this paper, we investigate the recognition of ocular regions using correlation filters without segmenting the iris region. This method uses the whole eye region and surrounding areas, i.e., the ocular region, for identification. In our experiments we use the recently developed Quadratic Correlation Filter and show that at low resolutions segmentation-free ocular recognition can succeed while iris segmentation fails.

  15. 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.

  16. 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

  17. Conjoint Recognition.

    ERIC Educational Resources Information Center

    Brainerd, C. J.; Reyna, V. F.; Mojardin, A. H.

    1999-01-01

    Reviews some limiting properties of the process-dissociation model as it applies to the study of dual-process conceptions of memory. A second-generation model (conjoint recognition) is proposed to address these limitations and supply additional capabilities. Worked applications to data are provided. (Author/GCP)

  18. Pronunciation Modeling for Large Vocabulary Speech Recognition

    ERIC Educational Resources Information Center

    Kantor, Arthur

    2010-01-01

    The large pronunciation variability of words in conversational speech is one of the major causes of low accuracy in automatic speech recognition (ASR). Many pronunciation modeling approaches have been developed to address this problem. Some explicitly manipulate the pronunciation dictionary as well as the set of the units used to define the…

  19. 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.

  20. Using Non-Orthogonal Iris Images for Iris Recognition

    DTIC Science & Technology

    2006-05-05

    geometry, face, voice , and iris. These quantifiable features are measured and stored in a database to be used for automatic recognition . The...U.S.N.A. --- Trident Scholar project report; no. 342 (2006) USING NON-ORTHOGONAL IRIS IMAGES FOR IRIS RECOGNITION by MIDN 1/C Ruth Mary...orthogonal iris images for iris recognition 6. AUTHOR(S) Gaunt, Ruth Mary, 1984- 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME