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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

关键词： Hyperspectral imaging Support vector machines Diseases signal to noise ratio Hybrid fiber coaxial cables Correlation

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Subaperture image segmentation for lossless compression

Subaperture image segmentation for lossless compression

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Workshops on Image processing Theory, Tools and Applications, IPTA

作者： Ionut Schiopu Moncef Gabbouj Alexandres Iosifidis Bing Zeng Shuaicheng Liu Laboratory of Signal Processing Tampere University of Technology Tampere Finland Institute of Image Processing University of Electronic Science and Technology of China Chengdu China

The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.

关键词： Image segmentation Image edge detection Image coding Prediction algorithms Cameras Context modeling Detectors

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Publisher Correction: Brain imaging with portable low-field MRI

Nature Reviews Bioengineering

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Nature Reviews Bioengineering 2023年 680页

作者： W. Taylor Kimberly Annabel J. Sorby-Adams Andrew G. Webb Ed X. Wu Rachel Beekman Kevin N. Sheth Ritvij Bowry Steven J. Schiff Adam de Havenon Francis X. Shen Gordon Sze Pamela Schaefer Juan Eugenio Iglesias Matthew S. Rosen Department of Neurology and the Center for Genomic Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Department of Radiology Leiden University Medical Center Leiden The Netherlands Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China Division of Neurocritical Care and Emergency Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine Yale Center for Brain & Mind Health New Haven CT USA Departments of Neurosurgery and Neurology McGovern Medical School University of Texas Health Neurosciences Houston TX USA Department of Neurosurgery Yale School of Medicine New Haven CT USA Division of Vascular Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Harvard Medical School Center for Bioethics Harvard law School Boston MA USA Department of Psychiatry Massachusetts General Hospital Boston MA USA Department of Radiology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Division of Neuroradiology Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Centre for Medical Image Computing University College London London UK Computer Science and AI Laboratory Massachusetts Institute of Technology Boston MA USA

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Weighted low-rank tensor recovery for hyperspectral image restoration

arXiv

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arXiv 2017年

作者： Chang, Yi Yan, Luxin Fang, Houzhang Zhong, Sheng Zhang, Zhijun Science and Technology on Multispectral Information Processing Laboratory School of Automation Huazhong University of Science and Technology Wuhan430074 China National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such noises (random noise, HSI denoising), blurs (Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral and spatial down-sample, HSI super-resolution). Previous HSI restoration methods are designed for one specific task only. Besides, most of them start from the 1-D vector or 2-D matrix models and cannot fully exploit the structurally spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this work, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which non-local similarity between spectral-spatial cubic and spectral correlation are simultaneously captured by 3-order tensors. Further, to improve the capability and flexibility, we formulate it as a weighted low-rank tensor recovery (WLRTR) model by treating the singular values differently, and study its analytical solution. We also consider the exclusive stripe noise in HSI as the gross error by extending WLRTR to robust principal component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed WLRTR models consistently outperform state-of-the-arts in typical low level vision HSI tasks, including denoising, destriping, deblurring and super-resolution. Copyright © 2017, The Authors. All rights reserved.

关键词： Image reconstruction

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Multi-factorial evolutionary algorithm based on M2M decomposition 11th

Multi-factorial evolutionary algorithm based on M2M decompos...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Mo, Jiajie Fan, Zhun Li, Wenji Fang, Yi You, Yugen Cai, Xinye Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou515063 China Department of Electronic Engineering Shantou University Shantou515063 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China

ISBN: (纸本)9783319687582

This paper proposes a decomposition-based multi-objective multi-factorial evolutionary algorithm (MFEA/D-M2M). The MFEA/D-M2M adopts the M2M approach to decompose multi-objective optimization problems into multiple constrained sub-problems for enhancing the diversity of population and convergence of sub-regions. An machine learning model augmented version is also been implemented, which utilized discriminative models for pre-selecting solutions. Experimental studies on nine multi-factorial optimization (MFO) problem sets are conducted. The experimental results demonstrated that MFEA/D-M2M outperforms the vanilla MFEA on six MFO benchmark problem sets and achieved comparable results on the other three problem sets with partial intersection of global optimal. © Springer International Publishing AG 2017.

关键词： Evolutionary algorithms

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Specific Emitter Identification based on the Energy Envelope of Transient signal

Specific Emitter Identification based on the Energy Envelope...

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Fei Zhuo Yuanling Huang Jian Chen Science and Technology on Blind Signal Processing Laboratory

Specific Emitter Identification（SEI） is the method to identify the individual radio emitter using the transmitted signals’ characteristic called Radio Frequency Fingerprint（RFF）, which are originated from transmitter imperfectionsA novel SEI approach to extract transient fingerprint features of energy envelope of transient signals is proposed in this paperThe origin of energy envelop is explored, and a nonlinear system model is utilized to explain how the features of energy envelope produceAn iterative least squares identification algorithm is proposed to extract the parameters of model, which can be constructed the fingerprint featuresExperimental results demonstrate that the method is effective, and the method can be applicable any signal of TDMA system.

关键词： specific emitter identification radio frequency fingerprint energy envelope iterative least squares

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Normal-Mode Based MUSIC for Bearing Estimation in Shallow Water Using Acoustic Vector Sensors

Normal-Mode Based MUSIC for Bearing Estimation in Shallow Wa...

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2016 International Conference on Mechatronics Engineering and Information technology(ICMEIT 2016)

作者： Weiwei Ai Jinyu Xiong Xiaoyong Zhang Science and Technology on Blind Signal Processing Laboratory

ISBN: (纸本)9781510829039

To realize unbiased bearing estimates of multiple acoustic sources in a range-independent shallow water, Normal-Mode based MUSIC(NM-MUSIC) method using acoustic vector sensor(AVS) array is proposed in this paper. Comparing to NM-MUSIC method based on scalar array, the method based on AVS array solves the problem of port and starboard ambiguity, and also breaks through the limitation of half wavelength. Meanwhile, the method realizes unbiased bearing estimates, while most of the conventional Direction of Arrival(DOA) methods result in biased bearing estimates in shallow water. Simulation results show that the performance of the new method proposed is better than that of the original method.

关键词： Acoustic vector sensor array Normal-Mode based MUSIC Shallow ocean Unbiased bearing estimates

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An Anti-Eavesdrop Transmission Scheduling Scheme Based on Maximizing Secrecy Outage Probability in Ad Hoc Networks

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China Communications 2016年第1期13卷 176-184页

作者： WANG Yajun LIAO Tongqing WANG Chuanan School of Mathematical and Information Engineering Anhui Science and Technology University Ministry of Educational Key Laboratory of Intelligent Computing and Signal Processing Anhui University

In this paper,we consider a wireless ad hoc network consisting of multiple source nodes transmitting to their respective destinations,where an eavesdropper attempts to intercept their *** propose an optimal transmission scheduling scheme to defend against the eavesdropper,where a source node having the highest secrecy rate is scheduled to access the wireless medium for transmitting to its destination in an opportunistic *** be specific,the secrecy rate between a pair of the source and destination in the presence of an eavesdropper varies temporally due to the wireless fading *** proposed optimal transmission scheduling scheme opportunistically selects a source node with the highest secrecy rate to transmit its data for the sake of maximizing the security of the ad hoc network against eavesdropping *** comparison purposes,we also consider the conventional round-robin scheduling as a benchmark,where multiple source nodes take turns in accessing their shared wireless medium for transmitting to their respective *** derive closed-form secrecy outage probability expressions of both the round-robin scheduling and the proposed optimal scheduling schemes over Rayleigh fading *** results show that the proposed transmission scheduling scheme outperforms the conventional round-robin method in terms of its secrecy outage ***,upon increasing the number of source-destination pairs,the secrecy outage probability of the round-robin scheme keeps unchanged,whereas the secrecy outage performance of the proposed transmission scheduling significantly improves,showing the security benefits of exploiting transmission scheduling for protecting wireless ad hoc networks against eavesdropping.

关键词： transmission scheduling eavesdropping secrecy outage probability secrecy rate rayleigh fading

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Modeling and Robust Integral Sliding Mode Control for a Quadrotor Unmanned Aerial Vehicle

Modeling and Robust Integral Sliding Mode Control for a Quad...

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International Renewable and Sustainable Energy Conference

作者： Moussa Labbadi Mohamed Cherkaoui Yassine El houm M'hammed Guisser Engineering For Smart and Sustainable Systems Research Center Mohammed V University Rabat Morocco Laboratory of Signal Analysis and Information Processing Faculty of Science and Technology Settat Morocco

ISBN: (纸本)9781728111834;9781728111827

In the last years, many research studies have been presented on quadrotors due to their different applications in daily life. In this paper, a complete mathematical model of generalized flight is developed based on the Newton-Euler method. This one takes into account external disturbances (aerodynamic forces and moments) acting on the vehicle. A nonlinear control strategy based on the sliding mode control technique has been designed. This controller is employed to control the drone attitude and position as well as to compensate for external disturbances acting on the quadrotor. The stability of the system in closed-loop is ensured by Lyapunov's theorem and analysis. A control method integral sliding mode controller (ISMC) has been developed to improve the controller performance of the SMC. The problem of chattering in the suggested controllers was treated without degradation of the system performance (precision, rapidity, robustness). Finally, to validate the choice of controllers proposed, several simulations were developed with the presence of aerodynamic disturbances.

关键词： Quadrotor unmanned aerial vehicle (UAV) Sliding mode controller Robust control Newton-Euler Chattering reduction

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