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International Waveform Diversity and Design Conference

作者： R. C. Qiu M. C. Wicks L. Li Z. Hu S. J. Hou P. Chen J. P. Browning Cognitive Radio Institute Department of Electrical and Computer Engineering Center for Manufacturing Research Tennessee Technological University Cookeville TN USA Air Force Research Laboratory Rome NY USA Department of Mathematics Syracuse University Syracuse NY USA Radar Signal Processing Branch Air Force Research Laboratory Dayton OH USA

Wireless tomography, a novel approach to remote sensing, is proposed in Part I of this series. The methodology, literature review, related work, and system engineering are presented. Concrete algorithms and hardware platforms are implemented to demonstrate this concept. Self-cohering tomography is studied in depth. More research will be reported, following this initiative.

关键词： Tomography Wireless communication Wireless sensor networks Sensors Image reconstruction Radar imaging

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Techniques for Automatic Magnetic Resonance Image Classification

Techniques for Automatic Magnetic Resonance Image Classifica...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hsian-Min Chen Shih-Yu Chen Jyh Wen Chai Clayton Chi-Chang Chen Chao-Cheng Wu Yen-Chieh Ouyang Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions.

关键词： Classification algorithms Training Image segmentation Brain Skull Software algorithms Support vector machines

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Volume-based Magnetic Resonance Brain Image Classification

Volume-based Magnetic Resonance Brain Image Classification

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.

关键词： Training Support vector machine classification Image classification Vectors Volume measurement Magnetic resonance

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Multiple-atlas-based automatic sementation of hippocampus for lateralization in temporal lobe epilepsy

Multiple-atlas-based automatic sementation of hippocampus fo...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi-Asl Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Image Analysis Laboratory Department of Diagnostic Radiology Henry Ford Health System Detroit MI USA School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424441259

We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.

关键词： Hippocampus Temporal lobe Epilepsy Image segmentation Principal component analysis Shape control Probability density function Level set Skull Algorithm design and analysis

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A Bayesian Perspective on Sparse Regularization for STAP Post-processing

A Bayesian Perspective on Sparse Regularization for STAP Pos...

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IEEE Radar Conference

作者： Jason T. Parker Lee C. Potter Radar Signal Processing Branch Sensors Directorate US Air Force Research Laboratory Department of Electrical and Computer Engineering The Ohio State University

ISBN: (纸本)9781424458110

Traditional Space Time Adaptive processing (STAP) formulations cast the problem as a detection task which results in an optimal decision statistic for a single target in colored Gaussian noise. In the present work, inspired by recent theoretical and algorithmic advances in the field known as compressed sensing, we impose a Laplacian prior on the targets themselves which encourages sparsity in the resulting reconstruction of the angle/Doppler plane. By casting the problem in a Bayesian framework, it becomes readily apparent that sparse regularization can be applied as a post-processing step after the use of a traditional STAP algorithm for clutter estimation. Simulation results demonstrate that this approach allows closely spaced targets to be more easily distinguished.

关键词： Bayesian inference Space time adaptive processing Targets Gaussian noise Compressed sensing

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Iterative Design of MIMO Radar Transmit Waveforms and Receive Filter Bank

Iterative Design of MIMO Radar Transmit Waveforms and Receiv...

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Y.-C. Wang Hongwei Liu Z.-Q. Luo State Key Laboratory on Integrated Service Networks Xidian University Xi'an 710071 China. Currently Y.-C Wang is a post doctorial fellow at the University of Minnesota National Lab of Radar Signal Processing Xidian University Xi'an 710071 China Department of Electrical Computer Science Engineering University of Minnesota Minneapolis 55455 USA

ISBN: (纸本)9781424442959

In this paper, we propose an iterative design approach to jointly optimize probing signal waveforms and a receive filter bank for a multiple-input multiple-output (MIMO) radar under a constant modulus constraint. The design goals are to approximate a desired beampattern and to minimize the auto/cross- correlation levels of the probing signal waveforms for different time lags and between different spatial angles. Since the overall design problem is nonconvex, we propose to optimize the transmit probing signals and receive filter bank separately and alternately. The optimization of receive filter bank is a standard least squares problem, while the optimization of the constant modulus transmit signal waveforms is a norm-constrained least squares problem which can be approximately solved using a low-rank semidefinite relaxation procedure. We demonstrate the effectiveness of our proposed approach through a simulation example.

关键词： Multiple-input multiple-output (MIMO) radar Beampattern Mismatched filter bank Constant modulus probing signal Auto correlation peak sidelobes Cross correlation Level signal waveform Filter banks design target Banks MIMO radar TRANSMITTING Exploration with a probe Iterative design problems

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A comparative study of hierarchical matching algorithms for face recognition

A comparative study of hierarchical matching algorithms for ...

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11th IAPR Conference on Machine Vision Applications, MVA 2009

作者： Momeni, Hajar Sadeghi, Mohammad T. Abutalebi, Hamid R. Signal Processing Research Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran

ISBN: (纸本)9784901122092

The similarity measure based nearest neighbour classifier is commonly used in object recognition or retrieval systems. The result of a query in such a system is in general the images from the database whose descriptors are closest to the descriptor of the query image. An important issue in large scale recognition systems is the computational burden caused by measuring the similarity of the features derived from the query image with the templates of the database objects. Hierarchical Agglomerative Clustering (HAC) algorithms are considered as a solution to this problem. In these algorithms, a dendrogram is formed down to top in the training stage. In the recognition phase, the query image moves on the dendrogram from the highest level to lower levels in order to find the best matched object. One of the most affecting issues in these algorithms is the strategy used for building the dendrogram. In this paper, different techniques adopted for this purpose are studied and compared within the framework of a face recognition system. Our experimental results demonstrate that using an appropriate merging technique, the average recognition time reduces while the performance of the system is not highly degraded.

关键词： Face recognition

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Quasimonoenergetic Proton Bunch Generation by Dual-Peaked Electrostatic-Field Acceleration in Foils Irradiated by an Intense Linearly Polarized Laser

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Physical Review Letters 2010年第6期105卷 065003-065003页

作者： H. B. Zhuo (卓红斌) Z. L. Chen (陈正林) W. Yu (余玮) Z. M. Sheng (盛政明) M. Y. Yu (郁明阳) Z. Jin (金展) R. Kodama (兒玉了祐) Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 People’s Republic of China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 People’s Republic of China Department of Physics Shanghai Jiao Tong University Shanghai 200240 People’s Republic of China Beijing National Laboratory for Condensed Matter Physics Institute of Physics CAS Beijing 100080 People’s Republic of China Institute for Fusion Theory and Simulation Zhejiang University Hangzhou 310027 People’s Republic of China Institute for Theoretical Physics I Ruhr University Bochum D-44780 Germany Institute of Laser Engineering Osaka University 2-6 Yamada-oka Suita Osaka 565-0871 Japan

It is found that stable proton acceleration from a thin foil irradiated by a linearly polarized ultraintense laser can be realized for appropriate foil thickness and laser intensity. A dual-peaked electrostatic field, originating from the oscillating and nonoscillating components of the laser ponderomotive force, is formed around the foil surfaces. This field combines radiation-pressure acceleration and target normal sheath acceleration to produce a single quasimonoenergetic ion bunch. A criterion for this mechanism to be operative is obtained and verified by two-dimensional particle-in-cell simulation. At a laser intensity of ∼5.5×1022 W/cm2, quasimonoenergetic GeV proton bunches are obtained with ∼100 MeV energy spread, less than 4° spatial divergence, and ∼50% energy conversion efficiency from the laser.

关键词： Germanium compounds

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Surveillance of pedestrian bridge traffic using neural networks

Surveillance of pedestrian bridge traffic using neural netwo...

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Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009

作者： Watkins, Steve E. Stanley, R. Joe Gopal, Anand Moss, Randy H. Department of Electrical and Computer Engineering Applied Optics Laboratory Missouri University of Science and Technology Rolla MO 65409 United States Department of Electrical and Computer Engineering Signal Processing Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9780819475527

A computer-vision monitoring system is demonstrated that automatically detects the presence and location of people. The approach investigated the potential for real-time, automated surveillance and tracking in a realistic environment. Economy was obtained by the use of gray-scale, fixed perspective images and efficiency was obtained by the use of selected object features and a neural-network-processing algorithm. The system was applied to pedestrian traffic on an outdoor bridge and consequently had to handle complex images. Image sequences of single and multiple people were used with differences in clothing, position, lighting, season, etc. A two-stage algorithm was implemented in which (1) new objects were identified in a highly variable scene and (2) the objects were classified with a back-propagation neural network. The image processing techniques included segmentation and filtering and the neural network used fourteen object features as inputs. The implementation had excellent people-discrimination accuracy despite the noise in the images and had low computational complexity with respect to alternative techniques. © 2009 SPIE.

关键词： Neural networks

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A robust face detection method using intrinsic images and an energy minimization technique under the near-IR light spectrum

A robust face detection method using intrinsic images and an...

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2009 International Conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2009

作者： Lahlou, Mouncef Adjouadi, Malek Barreto, Armando Center for Advanced Technology and Education Department of Electrical and Computer Engineering Florida International University Miami FL United States Digital Signal Processing Laboratory Department of Electrical and Computer Engineering Florida International University Miami FL United States

ISBN: (纸本)9781601321190

Detecting faces accurately under natural lighting conditions remains a challenging task for advancing the performance of face recognition systems. Most face detection algorithms are associated with visible spectrum imagery which compromises their efficiency when image acquisition is subject to variable illumination. In this study, we propose a simple yet powerful solution that uses multiple cues to detect and locate faces accurately in near infrared (NIR) imaging systems. Specific objectives include (I) extract optimally the illumination and reflectance components of the original image using an intrinsic images decomposition technique, (2) modify the segmentation process by using an energy minimization procedure in an adaptive fashion, and (3) propose an enhanced face detection process on the basis of pupil localization. The evaluation of our face detector on image sequences of 45 different subjects from the CBSR NIR face dataset demonstrated a highly competitive accuracy with less than 5% error rate.

关键词： Face recognition

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