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Asilomar Conference on signals, Systems & computers

作者： Divya Mohan Sameeksha Katoch Suren Jayasuriya Pavan Turaga Andreas Spanias Sensor Signal and Information Processing (SenSIP) Center School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

Energy-efficient computer vision is vitally important for embedded and mobile platforms where a longer battery life can allow increased deployment in the field. In image sensors, one of the primary causes of energy expenditure is the sampling and digitization process. Smart subsampling of the image-array in a manner that is task-specific, can result in significant savings of energy. We present an adaptive algorithm for video subsampling, which is aimed at enabling accurate object detection, while saving sampling energy. The approach utilizes objectness measures, which we show can be accurately estimated even from sub-sampled frames, and then uses that information to determine the adaptive sampling for the subsequent frame. We show energy savings of 18 - 67% with only a slight degradation in object detection accuracy in experiments. These results motivated us to further explore energy-efficient subsampling using advanced techniques such as, reinforcement learning and Kalman filtering. The experiments using these techniques are underway and provide ample support for adaptive subsampling as a promising avenue for embedded computer vision in the future.

关键词： Object detection Streaming media computer vision image sensors Optical imaging Task analysis Adaptive algorithms

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Implicit Saliency In Deep Neural Networks

Implicit Saliency In Deep Neural Networks

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IEEE International Conference on image processing

作者： Yutong Sun Mohit Prabhushankar Ghassan AlRegib OLIVES at the Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency. We term this as implicit saliency in deep neural networks. We calculate this implicit saliency using expectancy-mismatch hypothesis in an unsupervised fashion. Our experiments show that extracting saliency in this fashion provides comparable performance when measured against the state-of-art supervised algorithms. Additionally, the robustness outperforms those algorithms when we add large noise to the input images. Also, we show that semantic features contribute more than low-level features for human visual saliency detection. Based on these properties and performances, our proposed method greatly lowers the threshold for saliency detection in terms of required data and bridges the gap between human visual saliency and model saliency.

关键词： Feature extraction Visualization Semantics Saliency detection Convolution Robustness Neural networks

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Multiple-mode Kalman filtering with node selection using bearings-only measurements

Multiple-mode Kalman filtering with node selection using bea...

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Southeastern Symposium on System Theory

作者： Qiang Le L.M. Kaplan J.H. McClellan CTSPS Department of Engineering Clark Atlanta University Atlanta GA USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

This work investigates multiple-mode tracking method with node selection using bearings-only measurements. We combine multiple-mode extended Kalman filter with node resource management to conserve energy while tracking a maneuvering target. Experiments using real data show that the MM adapts quicker to target maneuvers than the realizable single-mode tracker. Additional experiments show that the simplex node selection leads to better geolocation performance compared to the closest node selection when the number of active nodes is set to two.

关键词： Kalman filters Filtering Target tracking Resource management Batteries Military computing Intelligent sensors Direction of arrival estimation Electric variables measurement signal processing

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Expectation propagation based iterative group wise detection for large-scale multiuser MIMO-OFDM systems

Expectation propagation based iterative group wise detection...

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2014 IEEE Wireless Communications and Networking Conference, WCNC 2014

作者： Wu, Sheng Kuang, Linling Ni, Zuyao Lu, Jianhua Huang, Defeng David Guo, Qinghua Department of Electronic Engineering Tsinghua University Beijing China Tsinghua Space Center Tsinghua University Beijing China Research Institute of Information Technology Tsinghua University Beijing China Shenzhen Key Laboratory of Wireless Broadband Communication and Signal Processing Shenzhen China School of Electrical Electronic and Computer Engineering University of Western Australia Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia

ISBN: (纸本)9781479930838

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms. © 2014 IEEE.

关键词： Message passing

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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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Complementary Phase Encoding for Pair-Wise Neural Deblurring of Accelerated Brain MRI 17th

Complementary Phase Encoding for Pair-Wise Neural Deblurrin...

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17th European Conference on computer Vision, ECCV 2022

作者： Hod, Gali Green, Michael Waserman, Mark Konen, Eli Shrot, Shai Nelkenbaum, Ilya Kiryati, Nahum Mayer, Arnaldo School of Electrical Engineering Tel Aviv University Tel Aviv Israel School of Computer Sciences Ben Gurion University Beersheba Israel Diagnostic Imaging Department at Sheba Medical Center Affiliated with the Beverly Sackler School of Medicine Tel Aviv University Tel Aviv Israel Klachky Chair of Image Processing School of Electrical Engineering Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9783031250651

MRI has become an invaluable tool for diagnostic brain imaging, providing unrivalled qualitative and quantitative information to the radiologist. However, due to long scanning times and capital costs, access to MRI lags behind CT. Typical brain protocols lasting over 30 min set a clear limitation to patient experience, scanner throughput, operation profitability, and lead to long waiting times for an appointment. As image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration, significant scanning acceleration must successfully address challenging image degradation. In this work, we consider the scan acceleration scenario of a strongly anisotropic acquisition matrix. We propose a neural approach that jointly deblurs scan pairs acquired with mutually orthogonal phase encoding directions. This leverages the complementarity of the respective phase encoded information as blur directions are also mutually orthogonal between the scans in the pair. The proposed architecture, trained end-to-end, is applied to T1w scan pairs consisting of one scan with contrast media injection (CMI), and one without. Qualitative and quantitative validation is provided against state-of-the-art deblurring methods, for an acceleration factor of 4 beyond compressed sensing acceleration. The proposed method outperforms the compared methods, suggesting its possible clinical applicability for this challenging task. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Direction-of-arrival estimation of wideband sources using arbitrary shaped multidimensional arrays

Direction-of-arrival estimation of wideband sources using ar...

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

作者： Yeo-Sun Yoon L.M. Kaplan J.H. McClellan Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA CTSPS Department of Engineering Clark Atlanta University Atlanta GA USA

We generalize a new incoherent wideband direction-of-arrival (DOA) estimation algorithm that provides higher resolution than traditional incoherent techniques. The new method is able to better adjust its beam response when multiple sources are present than incoherent MUSIC. The original algorithm was designed to work strictly for an uniform linear array. In this paper, we demonstrate how to generalize the algorithm to work over arbitrary 1D or 2D arrays. We demonstrate the higher resolution of the new algorithm against incoherent MUSIC for 1D and 2D arrays using simulations of two source signals.

关键词： Direction of arrival estimation Wideband Multidimensional systems Frequency estimation Matrix decomposition Narrowband Multiple signal classification Sensor arrays signal resolution Wireless communication

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Error analysis of the pruned multi-angle resolution beamformer

Error analysis of the pruned multi-angle resolution beamform...

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IEEE Digital signal processing Workshop

ISBN: (纸本)0780381165

Recently, we proposed the pruned fast beamformer that is able to detect coherent signals during beamforming. The fast beamformer is a multistage approximation to delay and sum beamforming. Using a coherent signal detector, the pruned fast beamformer is able to reduce the computational load by eliminating processing to resolve steering directions that do not contain a target-like source. This paper analyzes the performance of the detector and discusses how approximation errors in the fast beamformer effects detection performance. The results indicate that the coherence detectors is effective under the approximation errors.

关键词： Error analysis Array signal processing Sensor arrays signal processing Detectors Approximation error signal resolution signal detection Delay Performance analysis

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Superiority of the 3rd order approximation model to the 2nd order in Diffusion Kurtosis Imaging

Superiority of the 3rd order approximation model to the 2nd ...

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作者： Iraji, Armin Davoodi-Bojd, Esmaeil Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam-Ali Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI 48202 United States

ISBN: (纸本)9789644634284

For estimating the kurtosis parameter in Diffusion Kurtosis Imaging (DKI), usually second order expansion of the diffusion signal is used in conventional acquisition data. However, in this work, we show that this is not sufficient and should be reconsidered. Instead of using the second order expansion, we propose to use the third order expansion. At first, we show that the third order term of the Taylor's expansion has considerable values compared to second and first terms for the conventional b-values used in DKI. Then, for proving our claim empirically, we use real data to compare the proposed model with the previous one. To this end, five regions of the brain are extracted and for each region the fitting errors of both models are computed. The results confirm that the fitting error of the third order model is always (for all regions) less than the second order model. Although the improvement of the fitting error seems to be small, but considering the sensitivity analysis, this improvement can enhance the estimation of kurtosis. The resulting maps of the kurtosis parameter show this enhancement. The white matter in the map of third order model is more distinguishable than in the other model. © 2011 AmirKabir Univ of Tech.

关键词： Diffusion

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