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2009 12th International Conference on Information Fusion, FUSION 2009

作者： Clark, Daniel E. Ba, Tuong Vo Ba, Ngu Vo Joint Research Institute in Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University United Kingdom School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Hwy Crawley WA 6009 Australia Department of Electrical and Electronic Engineering University of Melbourne Parkville VIC 3052 Australia

ISBN: (纸本)9780982443804

The problem of jointly detecting whether a target is present in a scene and, if there is, estimating its state can be viewed as a multi-object estimation problem where there is a maximum of one target. This joint detection and estimation problem can be solved using a special case of the multi-object Bayes filter. In this paper we investigate the joint target detection and estimation problem with forward-backward smoothing and propose a sequential Monte Carlo implementation. Finite Set Statistics not only facilitates the development of appropriate joint detection and estimation filters, but also the direct extension of these filtering solutions to their related smoothing counterparts. Preliminary results indicate that using the smoothing has two distinct advantages over just using filtering: Firstly, we are able to more accurately identify the appearance and disappearance of a target in the scene and secondly, we can provide improved state estimates when the target exists. ©2009 ISIF.

关键词： Monte Carlo methods

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Spectral derivative feature coding for hyperspectral signature analysis

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Pattern Recognition 2009年第3期42卷 395-408页

作者： Chang, Chein-I Chakravarty, Sumit Chen, Hsian-Min Ouyang, Yen-Chieh Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Environmental Restoration and Disaster Reduction Research Center National Chung Hsing University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Radiology China Medical University Hospital Taichung Taiwan

This paper presents a new approach to hyperspectral signature analysis, called spectral derivative feature coding (SDFC). It is derived from texture features used in texture classification to dictate gradient changes among adjacent bands in characterizing spectral variations so as to improve better spectral discrimination and classification. In order to evaluate its performance, two known binary coding methods, spectral analysis manager (SPAM) and spectral feature-based binary coding (SFBC) are used to conduct comparative analysis. Experimental results demonstrate that the proposed SDFC performs more effectively in capturing spectral characteristics than do SPAM and SFBC. © 2008 Elsevier Ltd. All rights reserved.

关键词： Spectrum analysis

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Fetal R-wave detection from multichannel abdominal ECG recordings in low SNR

Fetal R-wave detection from multichannel abdominal ECG recor...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shahrzad Kharabian Mohammad B. Shamsollahi Reza Sameni Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran School of Electrical & Computer Engineering Shiraz University Shiraz Iran

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also, as piCA ranks the components according to their resemblance to maternal ECG, it could be used in automatic fetal ECG extraction algorithms.

关键词： Abdomen Electrocardiography signal processing algorithms Fetal heart Data mining Independent component analysis Magnetic recording Interference Magnetic separation Electrodes

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Real-time processing of simplex growing algorithm

Real-time processing of simplex growing algorithm

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2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

作者： Wu, Chao-Cheng Chang, Chein-I. Ren, Husen Chang, Yang Lang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Center for Space and Remote Sensing Research National Central University Jhong-Li Tao-Yuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781424433957

Simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) which is shown to be a promising endmember extraction technique. This paper further extends the SGA to a real-time processing algorithm, referred to as real-time SGA (RT SGA) that can effectively address four major issues arising in practical implementation for N-FINDR, (1) use of random initial endmembers which causes inconsistent final results, (2) very high computational complexity which results from an exhaustive search for finding all endmembers simultaneously, (3) requirement of dimensionality reduction because of enormous data volumes to be processed and (4) lack of real-time capability. ©2009 IEEE.

关键词： Extraction

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Assessment of depth of anesthesia using principal component analysis

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Journal of Biomedical Science and engineering 2009年第1期2卷 9-15页

作者： Mina Taheri Behzad Ahmadi Rassoul Amirfattahi Mojtaba Mansouri Digital Signal Processing Research Lab Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Correspondence should be addressed to Mina Taheri

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

关键词： Bispectral Index Depth of Anesthesia Eignevalue Decomposition Principal Component Analysis

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New simulation models for non-isotropic scattering mobile-to-Mobile rayleigh fading channels 09

New simulation models for non-isotropic scattering mobile-to...

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2009 ACM International Wireless Communications and Mobile Computing Conference, IWCMC 2009

作者： Cheng, Xiang Laurenson, David I. Salous, Sana Vasilakos, Athanasios V. Joint Research Institute for Signal and Image Processing School of EPS Heriot-Watt University Edinburgh EH14 4AS United Kingdom Center for Communication Systems University of Durham Durham DH1 3LE United Kingdom Department of Computer and Telecommunications Engineering University of Western Macedonia Kozani GR 50100 Greece

ISBN: (纸本)9781605585697

To simulate mobile-to-mobile (M2M) Rayleigh fading channels under more realistic scenario of non-isotropic scattering, we propose one deterministic and one stochastic sumof- sinusoids (SoS) based simulation models. The proposed models extensively consider the distributions of the angle of arrival (AoA) and the angle of departure (AoD), and thus show a good approximation to the desired statistical properties of the reference model. Copyright 2009 ACM.

关键词： Rayleigh fading

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Forward-backward sequential Monte Carlo smoothing for joint target detection and tracking

Forward-backward sequential Monte Carlo smoothing for joint ...

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International Conference on Information Fusion

作者： Daniel E. Clark Ba Tuong Vo Ba Ngu Vo Joint Research Institute in Signal & Image Processing School of Engineering & Physical Sciences Heriot-Watt University UK School of Electrical Electronic & Computer Engineering University of Western Australia Crawley WA Australia Department of Electrical & Electronic Engineering University of Melbourne Parkville VIC Australia

ISBN: (纸本)9780982443804

关键词： Monte Carlo methods Smoothing methods Object detection Target tracking Nonlinear filters Filtering State estimation Layout Current measurement Delay estimation

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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

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Workshop on Hyperspectral image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Xiaoli Jiao Yingzi Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science & Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA) is how to obtain unknown knowledge of target signatures referred to as virtual endmembers (VEs) directly from the data to be processed. This issue has never arisen in supervised LSMA where the VEs are either assumed to be known a priori or can be provided by visual inspection. With the recent advent of hyperspectral sensor technology many unknown and subtle signal sources can be uncovered and revealed without prior knowledge. This paper addresses this issue and develops a component analysis-based unsupervised LSMA where the desired VEs can be extracted by component analysis-based transforms directly from the data to be processed without appealing for prior knowledge. In order to substantiate the utility of the proposed approach extensive experiments are conducted for demonstration.

关键词： image analysis Spectral analysis Hyperspectral imaging Independent component analysis Personal communication networks Inspection Statistics signal analysis Hyperspectral sensors Principal component analysis

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Brain tissue classification using independent vector analysis (IVA) for magnetic resonance image

Brain tissue classification using independent vector analysi...

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2009 9th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769536569

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait Recognition Using Wavelet Packet Silhouette Representat...

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Congress on image and signal processing, CISP

作者： Farzin Dadashi Babak N. Araabi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this paper, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of matching pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, transductive support vector machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.

关键词： Wavelet packets Support vector machines Matching pursuit algorithms Biometrics Humans Machine intelligence Surveillance Pursuit algorithms Support vector machine classification Libraries

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