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19th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Juszkiewicz, Lukasz Wroclaw Univ Technol PL-50370 Wroclaw Poland

ISBN: (纸本)9781479950812

This paper presents modification of a speech emotion recognition system for a social robot. Using speaker dependent classifiers with prior speaker identification step was proposed. Emotion recognition is done using global acoustic features of the speech. Six speech signal parameters are computed with the specialised software. The feature extraction is based on calculation of global statistics of those parameters and their derivatives. Correlation based feature selection algorithms are used for reducing feature vector length. For the evaluation of the system three classifiers were used: Bayes Network, Radial Basis Function Network and support vector machine. Six emotions could be recognised by the system. Several experiments were carried out: emotion recognition of the speaker not known to the system, speaker independent recognition of the known speakers, gender dependent classification as well as speaker dependent recognition with prior identification. The results of identification-driven speech emotion recognition show significant improvement compared to the performance of the speaker independent system based on previous research. The use of the tailored speaker dependent recognition system led to increase in the Bayes Network classification accuracy from 74% to 96%, in the support vector machine classification accuracy from 64% to 96% and in the Radial Basis Function Network classification accuracy from 67% to 88%.

关键词： belief networks emotion recognition feature extraction radial basis function networks robots signal classification speaker recognition speech recognition support vector machines Bayes network classifier correlation based feature selection algorithms global acoustic features radial basis function network classifier social robot speaker dependent classifiers speaker identification speech emotion recognition system speech signal parameters support vector machine classifier Accuracy Emotion recognition Feature extraction Robots Speech Speech recognition support vector machines emotion recognition speaker recognition Feature extraction support vector Network Radial basis function networks Robot Speech recognition Bayesian networks Speech speech recognition system signal classification

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Performance Study of Cyclostationary based Digital Modulation Classification Schemes 9

Performance Study of Cyclostationary based Digital Modulatio...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Satija, Udit Manikandan, M. S. Ramkumar, Barathram Indian Inst Technol Sch Elect Sci Bhubaneswar Orissa India

ISBN: (纸本)9781479964994

Automatic Modulation Classification (AMC) is a essential component in Cognitive Radio (CR) for recognizing the modulation scheme. Many modulated signals manifest the property of cyclostationarity as a feature so it can be exploited for classification. In this paper, we study the performance of digital modulation classification technique based on the cyclostationary features and different classifiers such as Neural Network, support vector machine, k-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis and Neuro-Fuzzy classifier. In this study we considered modulations i.e. BPSK, QPSK, FSK and MSK for classification. All classification methods studied using performance matrix including classification accuracy and computational complexity (time). The robustness of these methods are studied with SNR ranging from 0 to 20dB. Based upon the result we found that combining cyclostationary features with Naive Bayes and Linear Discriminant Analysis classifiers leads to provide better classification accuracy with less computational complexity.

关键词： Bayes methods cognitive radio frequency shift keying fuzzy systems neural nets pattern classification phase shift keying signal classification support vector machines telecommunication computing BPSK FSK MSK QPSK automatic modulation classification classification accuracy computational complexity cyclostationary based digital modulation classification cyclostationary feature digital modulation classification technique k-nearest neighbor classifier linear discriminant analysis classifier modulated signal naive Bayes classifier neural network classifier neuro-fuzzy classifier performance matrix support vector machine classifier Binary phase shift keying Computational complexity Frequency shift keying Neural networks support vector machines frequency shift keying Binary phase shift keying support vector Network complexity classes Cognitive radio Neural network minimum shift keying modulated signal Quadrature phase shift keying telecommunication computing Phase shift keying signal classification Bayes methods Pattern recognition Fuzzy systems

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Video-Based Detection and Analysis of Driver Distraction and Inattention

Video-Based Detection and Analysis of Driver Distraction and...

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1st International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Yun, Yixiao Gu, Irene Y. H. Bolbat, Mikhail Khan, Zulfiqar Hasan Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

ISBN: (纸本)9781479928668

This paper addresses two issues for mitigating driver distraction/inattention by using novel video analysis techniques: (a) inside an ego vehicle, driver inattention is monitored through first tracking drivers face/eye region using Riemannian manifold-based particle filters, followed by recognition of dynamic eye states using PPCA (probabilistic principal component analysis) and SVM (support vector machine) classifier. Frequencies of eye blinking and eye closure are used as the indication of sleepy and warning sign is then generated for recommendation;(b) outside an ego vehicle, road traffic is also analyzed. Surrounding vehicles (in both directions) are tracked, and their states are analyzed by self-calibrated cameras using view-geometries and road information. Parameters (e.g. distance, velocity, number) of tracked vehicles are estimated on the road ground plane in the 3D world coordinate system. These pieces of information are provided for mitigating drivers inattention. The main novelties of the proposed scheme include facial geometry based eye region detection for eye closure identification, combined tracking and detection of vehicles, new formulae derived in camera self-calibration, and the hybrid system that handles both daytime and nighttime scenarios. Experiments have been conducted on video data in two different types of camera settings, i.e., captured inside and outside a vehicle. Preliminary tests have been conducted, results and performance evaluation have indicated the effectiveness of the proposed methods.

关键词： cameras driver information systems eye gaze tracking geometry particle filtering (numerical methods) principal component analysis support vector machines video signal processing PPCA Riemannian manifold-based particle filters SVM classifier driver distraction driver inattention dynamic eye state recognition ego vehicle eye blinking eye closure probabilistic principal component analysis road information self-calibrated cameras support vector machine classifier video-based analysis video-based detection view-geometries Cameras Face Monitoring Roads support vector machines Three-dimensional displays Vehicles

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Characterization of EEG Signals for Identification of Alcoholics using ANOVA Ranked Approximate Entropy and classifiers

Characterization of EEG Signals for Identification of Alcoho...

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International Conference on Circuits, Communication, Control and Computing (I4C)

作者： Shri, T. K. Padma Sriraam, N. Bhat, Vidya

ISBN: (纸本)9781479965458

This work focuses on non-linear characterization of 61-channel electroencephalogram (EEG) signal for detecting alcoholics using ranked Approximate Entropy (ApEn) parameters. Significant channels that contribute to the detection of alcoholism are selected by ranking the ApEn features based on ANOVA test. In order to classify alcoholics from control, the ranked feature set is applied to two non-linear classifiers, namely Back Propagation Neural Network (BPNN) and support vector machine (SVM) classifiers respectively. The performance of the classifiers is evaluated in terms of classification accuracy as well as computational processing time. Experimental results reveal that the BPNN classifier with 40 hidden neurons and SVM classifier with a polynomial kernel of order 3 perform with an accuracy of 90% with only 32 ranked ApEn coefficients.

关键词： EEG Approximation entropy ANOVA Back propagation neural network support vector machine classifier alcoholics

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3rd IEEE Global Conference on Consumer Electronics (GCCE)

作者： Kinoshita, Shohei Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Sch Engn Kita Ku N-13W-8 Sapporo Hokkaido 0608628 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Kita Ku Sapporo Hokkaido 0600814 Japan

ISBN: (纸本)9781479951468

This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then, by using the obtained latent topic distribution features, the estimation of the popular music becomes feasible based on a support vector machine classifier. Experimental results show the effectiveness of our method.

关键词： music pattern classification support vector machines audio features latent Dirichlet allocation music estimation popular music estimation support vector machine classifier time information topic model Educational institutions Estimation Market research Multiple signal classification Music support vector machines vectors

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Topology Error Processing Based On Forecast Measurement Errors

Topology Error Processing Based On Forecast Measurement Erro...

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2014 Power Systems Computation Conference (PSCC)

作者： Gu Chaojun Jirutitijaroen, Panida Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

ISBN: (纸本)9788393580132

Topology errors can cause significant state estimation error or even divergence of power flow solution, which undermines the reliable operation of power grids. Undetected topology errors lead to dangerous situational unawareness in the control center;for example, an unknown transmission line outage may cause a cascading failure that leads to a rolling blackout. Existing detection methods are based on DC state estimation with residual analysis. These detection techniques use only instantaneous measurement data and require convergence of numerical solution. In this paper, a new approach that utilizes both historical and instantaneous measurement data is proposed to detect topology errors. The key idea is to track the dynamics of the power flow measurements by forecasting the measurements. Topology errors will cause the forecast measurements to be significantly different from the actual measurements. To implement this approach, a time-forward kriging-based load forecasting technique is used to forecast bus load for the next time step. The forecast load is then converted to forecast state through power flow analysis using network parameters stored at the control center. The forecast measurements can be further calculated from the forecast state. Topology errors can be detected by comparing the forecast and actual measurements. The forecast measurement errors are used as input to train support vector machine (SVM) classifier offline. Several common SVM kernel models are compared to find the most suitable kernel for detecting topology errors. The SVM classifier can be applied in real-time to detect a topology error. The proposed approach is tested on IEEE 14 bus system with NYISO load data from Year 2011 and 2012. Our analysis shows that the proposed approach can accurately detect a single-line topology error.

关键词： load flow load forecasting measurement errors power engineering computing support vector machines IEEE 14 bus system NYISO load data SVM classifier SVM kernel models bus load forecasting forecast measurement errors forecast state network parameters power flow analysis support vector machine classifier time-forward kriging-based load forecasting technique topology error processing Circuit breakers Kernel Measurement uncertainty Power measurement support vector machines Topology Transmission line measurements support vector Network topology Error measure Power flow analysis Transmission line measurements network parameters load forecasting Power engineering computing circuit breakers Power measurement load flow measurement uncertainty Control centres Kernel

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A comparative study of cell classifiers for image-based high-throughput screening

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BMC BIOINFORMATICS 2014年第1期15卷 1-10页

作者： Abbas, Syed Saiden Dijkstra, Tjeerd M. H. Heskes, Tom Radboud Univ Nijmegen Inst Comp & Informat Sci NL-6525 ED Nijmegen Netherlands Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands

Background: Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells. Results: For the HT29 data set we find that gentle boosting, SVM (linear) and SVM (RBF) are close in performance but SVM (linear) is faster than gentle boosting and SVM (RBF). For the HT29 data set the average performance difference between SVM (RBF) and SVM (linear) is 0.42%. For the HeLa data set we find that SVM (RBF) outperforms other classification methods and is on average 1.41% better in performance than SVM (linear). Conclusions: Our study proposes SVM (linear) for iterative improvement of the training data and SVM (RBF) for the final classifier to classify all unlabeled cells in the whole data set.

关键词： support vector machine Radial Basis Function Linear Discriminant Analysis HT29 Cell support vector machine classifier

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Quantitative change of EEG and respiration signals during mindfulness meditation

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2014年第1期11卷 87-87页

作者： Ahani, Asieh Wahbeh, Helane Nezamfar, Hooman Miller, Meghan Erdogmus, Deniz Oken, Barry Northeastern Univ Cognit Syst Lab Boston MA 02115 USA Oregon Hlth & Sci Univ Dept Neurol Portland OR 97201 USA Oregon Hlth & Sci Univ Dept Behav Neurosci Portland OR 97201 USA Oregon Hlth & Sci Univ Dept Biomed Engn Portland OR 97201 USA

Background: This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods: EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results: Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion: support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.

关键词： support vector machine classifier Mindfulness Meditation Meditation Practice Respiration Signal Meditation State

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PCA-SVM based CAD System for Focal Liver Lesions using B-Mode Ultrasound Images

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DEFENCE SCIENCE JOURNAL 2013年第5期63卷 478-486页

作者： Virmani, Jitendra Kumar, Vinod Kalra, Naveen Khandelwal, Niranjan Indian Inst Technol Roorkee Roorkee 247667 Uttar Pradesh India Postgrad Inst Med Educ & Res Chandigarh 160012 India

The contribution made by texture of regions inside and outside of the lesions in classification of focal liver lesions (FLLs) is investigated in the present work. In order to design an efficient computer-aided diagnostic (CAD) system for FLLs, a representative database consisting of images with (1) typical and atypical cases of cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small as well as large hepatocellular carcinoma (HCC) lesions and (3) normal (NOR) liver tissue is used. Texture features are computed from regions inside and outside of the lesions. Feature set consisting of 208 texture features, (i.e. 104 texture features and 104 texture ratio features) is subjected to principal component analysis (PCA) for finding the optimal number of principal components to train a support vector machine (SVM) classifier for the classification task. The proposed PCA-SVM based CAD system yielded classification accuracy of 87.2% with the individual class accuracy of 85%, 96%, 90%, 87.5% and 82.2% for NOR, Cyst, HEM, HCC and MET cases respectively. The accuracy for typical, atypical, small HCC and large HCC cases is 87.5%, 86.8%, 88.8%, and 87% respectively. The promising results indicate usefulness of the CAD system for assisting radiologists in diagnosis of FLLs.

关键词： Focal liver:lesions B-mode ultrasound principal component analysis support vector machine classifier computer-aided diagnostic system

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Identification of germ cell-specific genes in mammalian meiotic prophase

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BMC BIOINFORMATICS 2013年第1期14卷 1-13页

作者： Li, Yunfei Ray, Debjit Ye, Ping Washington State Univ Sch Mol Biosci Pullman WA 99164 USA Washington State Univ Pullman WA 99164 USA Washington State Univ Ctr Reprod Biol Pullman WA 99164 USA

Background: Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies. Results: We developed a machine-learning method for identifying germ cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically during meiotic initiation and prophase. At 10% recall, the method detected spermatocyte genes and oocyte genes with 90% and 94% precision, respectively. Our method outperformed gonadal expression levels and gonadal expression correlations in predicting germ cell-specific expression. Top-predicted spermatocyte and oocyte genes were both preferentially localized to the X chromosome and significantly enriched for essential genes. Also identified were transcription factors and microRNAs that might regulate germ cell-specific expression. Finally, we experimentally validated Rps6ka3, a top-predicted X-linked spermatocyte gene. Protein localization studies in the mouse testis revealed germ cell-specific expression of RPS6KA3, mainly detected in the cytoplasm of spermatogonia and prophase spermatocytes. Conclusions: We have demonstrated that, through the use of machine-learning methods, it is possible to detect germ cell-specific expression from gonadal microarray data. Results from this study improve our understanding of the transition from germ cells to meiocytes in the mammalian gonad. Further, this appr

关键词： Germ Cell support vector machine classifier Essential Gene Meiotic Prophase Pachytene Spermatocyte

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