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Bayesian Spatiotemporal Multitask Learning for Radar HRRP Target Recognition

IEEE Transactions on signal processing 2011年第7期59卷 3182-3196页

作者： Lan Du Penghui Wang Hongwei Liu Mian Pan Feng Chen Zheng Bao Department of Electrical and Computer Engineering Duke University Durham NC USA National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai China

A Bayesian dynamic model based on multitask learning (MTL) is developed for radar automatic target recognition (RATR) using high-resolution range profile (HRRP). The aspect-dependent HRRP sequence is modeled using a truncated stick-breaking hidden Markov model (TSB-HMM) with time-evolving transition probabilities, in which the spatial structure across range cells is described by the hidden Markov structure and the temporal dependence between HRRP samples is described by the time evolution of the transition probabilities. This framework imposes the belief that temporally proximate HRRPs are more likely to be drawn from similar HMMs, while also allowing for possible distant repetition or “innovation”. In addition, as formulated the stick-breaking prior and MTL mechanism are employed to infer the number of hidden states in an HMM and learn the target-dependent states collectively for all targets. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale problems. To validate the formulation, example results are presented for an illustrative synthesized dataset and our main application-RATR, for which we consider the measured HRRP data. For the latter, we also make comparisons to the model with the independent state-transition statistics and some other existing statistical models for radar HRRP data.

关键词： Hidden Markov models Data models Radar Bayesian methods Target recognition Analytical models

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On gas detection and concentration estimation via mid-IR-based gas detection system analysis model

On gas detection and concentration estimation via mid-IR-bas...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Yi Xin Joel M. Morris Communications and Signal Processing Laboratory (CSPL) Computer Science and Electrical Engineering Department University of Maryland Baltimore County Catonsville MD USA

In recent decades, Laser-based spectroscopy (LAS) has been used in a wide range of research and application fields due to developments in laser technology and infrared spectroscopy. A particular application of interest is mid-IR laser-based gas detection systems for health and environment assessment. In this paper, we use our statistical analysis model for a generic mid-IR pulsed-laser gas detection system to predict trace gas detection and concentration estimation performance, and their sensitivity to system parameters. Based on PNNL data and the Beer-Lambert law, we use the three main spectral peaks of a trace gas, as the basis for gas detection, and use the relationship between gas transmittance β, molar absorptivity ε, concentration, and the sample-mean measurement, x N , from the photodetector, as the basis for concentration estimation using a standard confidence interval method.

关键词： Gas lasers Estimation Optical pulses Photodetectors Laser modes Cavity resonators

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Iterative feature normalization for emotional speech detection

Iterative feature normalization for emotional speech detecti...

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International Conference on Acoustics, speech, and signal processing (ICASSP)

作者： Carlos Busso Angeliki Metallinou Shrikanth S. Narayanan Multimodal Signal Processing (MSP) Department of Electrical Engineering University of Texas Dallas USA Speech Analysis and Interpretation Laboratory Viterbi School of Engineering University of Southern California USA

Contending with signal variability due to source and channel effects is a critical problem in automatic emotion recognition. Any approach in mitigating these effects however has to be done so as to not compromise emotion-relevant information in the signal. A promising approach to this problem has been through feature normalization using features drawn from non-emotional ("neutral") speech samples. This paper considers a scheme for minimizing the inter-speaker differences while still preserving the emotional discrimination of the acoustic features. This can be achieved by estimating the normalization parameters using only neutral speech, and then applying the coefficients to the entire corpus (including emotional set). Specifically, this paper introduces a feature normalization scheme that implements these ideas by iteratively detecting neutral speech and normalizing the features. As the approximation error of the normalization parameters is reduced, the accuracy of the emotion detection system increases. The accuracy of the proposed iterative approach, evaluated across three databases, is only 2.5% lower than the one trained with optimal normalization parameters, and 9.7% higher than the one trained without any normalization scheme.

关键词： speech Accuracy Databases Hidden Markov models Acoustics Emotion recognition Feature extraction

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Social focus of attention as a time function derived from multimodal signals

Social focus of attention as a time function derived from mu...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Danil Korchagin Hamid Reza Abutalebi Idiap Research Institute Martigny Switzerland Speech Processing Research Laboratory (SPRL) Electrical and Computer Engineering Department Yazd University Yazd Iran

In this paper, we present the results of a study on the social focus of attention as a time function derived from the multisource multimodal signals, recorded by different personal capturing devices during social events. The core of the approach is based on fission and fusion of multichannel audio, video and social modalities to derive the social focus of attention. The results achieved to date on 16+ hours of real-life data prove the feasibility of the approach.

关键词： Media Estimation signal processing Correlation Delay effects Conferences Synchronization

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Localization of multiple simultaneous sound sources in reverberant conditions using blind source separation methods

Localization of multiple simultaneous sound sources in rever...

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International Symposium on Artificial Intelligence and signal processing (AISP)

作者： Hedieh Heli Hamid Reza Abutalebi Speech Processing Research Laboratory (SPRL) Electrical and Computer Engineering Department Yazd University Yazd Iran Elec and Computer Engineering Department Yazd University Yazd Iran Idiap Research Institute Martigny Switzerland

In this paper, we address the problem of multiple simultaneous sources localization by means of Blind Source Separation (BSS)-based algorithms. Considering BSS demixing filters as some blind null beamformer and producing an acoustical map from them, source localization can then be achieved by identifying the local minima of this acoustical map. To improve the performance of this method in reverberant environments, we have proposed to replace the demixing filter with one corresponds to only the direct path. This is done by keeping only the largest coefficient in each demixing filter and neglecting the other coefficients. Furthermore, the proposed method reduces the computational complexity. Our experiments demonstrate the efficiency of the proposed method in the localization of multiple simultaneous sound sources in reverberant environments.

关键词： Source separation Sensors Microphones Arrays Finite impulse response filter Reverberation Frequency domain analysis

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Adaptive variable degree-κ zero-trees for re-encoding of perceptually quantized wavelet packet transformed audio and high-quality speech

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ISRN signal processing 2011年第1期2011卷

作者： Ghahabi, Omid Savoji, Mohammad Hassan Department of Electrical and Computer Engineering Shahid Beheshti University Evin Sq. Tehran *** Iran Audio and Speech Processing Group Research Center of Intelligent Signal Processing (RCISP) Tehran *** Iran

A fast, efficient, and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-κ zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising (1) embedded zero-tree wavelet (EZW) and (2) the set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times. Copyright © 2011 O. Ghahabi and M. H. Savoji.

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Effect of Different Diffusion Maps on Registration Results

Effect of Different Diffusion Maps on Registration Results

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Iranian Machine Vision and Image processing (MVIP)

作者： Maryam Afzali Emad Fatemizadeh Hamid Soltanian-Zadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and EAR indices are superior to those obtained using only one of them alone.

关键词： Ear Diffusion tensor imaging Tensile stress Registers Eigenvalues and eigenfunctions Anisotropic magnetoresistance Image registration

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Design, fabrication and Helium Ion Microscope patterning of suspended nanomechanical graphene structures for NEMS applications

Design, fabrication and Helium Ion Microscope patterning of ...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： M. Annamalai S. Mathew V. Viswanathan C. Fang D. S. Pickard M. Palaniapan Centre of Integrated Circuit Failure Analysis & Reliability Singapore Plasmonics & Advanced Imaging Technology Laboratory Department of Electrical and Computer Engineering National University of Singapore Singapore Signal Processing & VLSI Lab Singapore

This paper investigates the potential uses of graphene nanomechanical devices for NEMS applications fabricated using Helium Ion Microscope (HIM). Suspended nanomechanical graphene drum structures of diameter (~2 - 3 μm) and thickness ranging from few atomic layers (~10 Å) to about 11 nm have been fabricated and characterized using Atomic Force Microscopy (AFM). The initial suspension profile and the thickness of the fabricated structures have been obtained using AFM. FEM results suggest that these structures can vibrate in the range of several MHz. Their small size and the higher operating frequencies make them well suitable for mass sensing applications with sensitivities greater than 10 -21 g Hz -1 which is more than three orders of magnitude improvement over silicon based structures. The initial suspended graphene membranes have been patterned using HIM to obtain multiple z-axis flexures with sub-10nm feature sizes.

关键词： Silicon Nanoelectromechanical systems Helium Microscopy Nanostructures Gold Fabrication

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Upper Limb Joint Angle Tracking with Inertial Sensors

Upper Limb Joint Angle Tracking with Inertial Sensors

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

作者： Mahmoud El-Gohary Lars Holmstrom Jessie Huisinga Edward King James McNames Fay Horak Biomedical Signal Processing Laboartory in the Department of Electrical and Computer Engineering at Portland State University (PSU) Chief Information Officer at APDM Inc. Balance Disorders Laboratory in the Department of Neurology at Oregon Health & Science University (OHSU)

ISBN: (纸本)9781424441211

Wearable inertial systems have recently been used to track human movement in and outside of the laboratory. Continuous monitoring of human movement can provide valuable information relevant to individual's level of physical activity and functional ability. Traditionally, orientation has been calculated by integrating the angular velocity from gyroscopes. However, a small drift in the measured velocity leads to large integration errors that grow with time. To compensate for that drift, complementary data from accelerometers are normally fused into the tracking systems using the Kalman or extended Kalman filter (EKF). In this study, we combine kinematic models designed for control of robotic arms with the unscented Kalman filter (UKF) to continuously estimate the angles of human shoulder and elbow using two wearable sensors. This methodology can easily be generalized to track other human joints. We validate the method with an optical motion tracking system and demonstrate correlation consistently greater than 0.9 between the two systems.

关键词： inertia sensor Human locomotion TRACKING SYSTEM robotic arm kinematics joint angle accelerometers Elbow Upper Extremity inertial systems Motor Activity

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Detection of Collagenous Colitis Based on Histopathology Image Segmentation of Colon

Detection of Collagenous Colitis Based on Histopathology Ima...

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Iranian Machine Vision and Image processing (MVIP)

作者： Vahid Malekian Mojgan Mokhtari Saeid Sadri Rasoul Amirfattahi Alireza Aghaie Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran School of Medicine Isfahan University of Medical Sciences Isfahan Iran Isfahan University of Technology Isfahan Isfahan IR

Collagenous colitis is prevalent among pathological diseases. Because of normal radiological and endoscopic appearances, the microscopic image of colon tissue is necessary and important material for investigation of this disease. In this paper, a computerized system is developed for collagenous colitis diagnosis. First, colon tissues are colored with Mason trichrome to make sub-epithelial collagen band to be appeared in blue. Second, preprocessing is done on the microscopic image of colon tissue, and then Color histogram features in HSV color space and Haar Wavelet energy in detail coefficients of sub-band images are implemented to characterize the histological structure representation for tissue classification. KNN Classifier is selected for this classification, which segments the sub-epithelial collagen band from the image, and finally, thickness of sub-epithelial collagen band is determined by morphological operations. Abnormal case will be recognized if the thickness of sub-epithelial collagen band be more than 10 μm. Fifty clinical image samples were employed in the training and testing procedures for the evaluation of system performance. Through the applied classification it was revealed that collagenous colitis can be diagnosed accurately.

关键词： Microscopy Image color analysis Colon Feature extraction Image segmentation Training Histograms

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