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A Subpixel Target Detection Approach to Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2017年第9期55卷 5093-5114页

作者： Xue, Bai Yu, Chunyan Wang, Yulei Song, Meiping Li, Sen Wang, Lin Chen, Hsian-Min Chang, Chein-I Center for Hyperspectral Imaging in Remote Sensing Information and Technology College Dalian Maritime University Dalian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Key Laboratory of Spectral Imaging Technology Chinese Academy of Sciences Xi'an China State Key Laboratory of Integrated Services Networks Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science and Information Management Providence University Taichung02912 Taiwan

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. © 1980-2012 IEEE.

关键词： Support vector machines

Efficient and Robust Machine Learning for Real-World Systems

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arXiv 2018年

作者： Pernkopf, Franz Roth, Wolfgang Zöhrer, Matthias Pfeifenberger, Lukas Schindler, Günther Fröning, Holger Tschiatschek, Sebastian Peharz, Robert Mattina, Matthew Ghahramani, Zoubin Department of Electrical Engineering Laboratory of Signal Processing and Speech Communication Graz University of Technology Austria Institute of Computer Engineering Ruperts Karls University Heidelberg Germany Microsoft Research Cambridge United Kingdom Machine Learning Group Department of Engineering University of Cambridge United Kingdom Machine Learning Group Department of Engineering University of Cambridge United Kingdom Uber AI Labs CA United States Arm Research Arm Ltd. Cambridge United Kingdom

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen tradeoff between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being "aware" of its limits, i.e. the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day’s applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

A survey of physical layer security techniques for 5G wireless networks and challenges ahead

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arXiv 2018年

作者： Wu, Yongpeng Khisti, Ashish Xiao, Chengshan Caire, Giuseppe Wong, Kai-Kit Gao, Xiqi Department of Electrical Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China Signal Multimedia and Security Lab University of Toronto Bahen Building TorontoON Canada Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Institute for Telecommunication Systems Technical University Berlin Einsteinufer 25 Berlin10587 Germany Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsi c randomness of the transmission channel to guarantee the security in physical layer. The evolution towards 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, etc. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Improving efficiency in convolutional neural network with multilinear filters

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arXiv 2017年

作者： Tran, Dat Thanh Iosifidis, Alexandros Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Aarhus Denmark

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary Patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary Patterns (LBPs)

Secure quantum communication in the presence of phase- and polarization-dependent loss

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arXiv 2018年

作者： Li, Chenyang Curty, Marcos Xu, Feihu Bedroya, Olinka Lo, Hoi-Kwong Center for Quantum Information and Quantum Control Department of Electrical & Computer Engineering Department of Physics University of Toronto TorontoONM5S 3G4 Canada Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain Shanghai Branch Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of China Shanghai201315 China

Silicon photonics holds the promise of the miniaturization of quantum communication devices. Recently, silicon chip optical transmitters for quantum key distribution (QKD) have been built and demonstrated experimentally. Nonetheless, these silicon chips suffer substantial phase- and polarization-dependent loss (PDL) which, if unchecked, could compromise the security of QKD systems because of overestimating the secret key rate. Here, we first restore the security by regarding the single photons without phase and polarization dependence as untagged and secure qubits. Next, by using a post-selection technique, one could implement a secure QKD protocol that provides a high key generation rate even in the presence of severe phase and polarization dependent loss. Our solution is simple to realize in a practical experiment as it does not require any hardware modification. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

Delta-Connected Static Var Compensator (SVC) based Hybrid Active Power Filter (SVC-HAPF) and Its Control Method

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Delta-Connected Static Var Compensator (SVC) based Hybrid Ac...

Annual Conference of the IEEE Industrial Electronics Society

作者： Lei Wang Keng-Weng Lao Chi-Seng Lam Man-Chung Wong Department of Electrical and Computer Engineering University of Macau Macau China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China

In this paper, a new structure and the coordinated control method of a delta connected static var compensator (SVC) based hybrid active power filter (SVC-HAPF) for reactive current, harmonic current and unbalanced current compensation are proposed. The proposed delta-connected SVC-HAPF has the desirable characteristics of wide compensation range with low voltage/current rating requirement in the active inverter part. Simulation results are provided to verify the effectiveness of the proposed structure and control method.

关键词： Harmonic analysis Inverters Static VAr compensators Active filters Power harmonic filters Reactive power Firing

Tensor representation in high-frequency financial data for price change prediction

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arXiv 2017年

作者： Tran, Dat Thanh Magris, Martin Kanniainen, Juho Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Department of Engineering Electrical & Computer Engineering Aarhus University Aarhus Denmark

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high-frequency traders. In order to take advantage of the rapid, subtle movement of assets in High-Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large-scale dataset which contains more than 4 million limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

Temporal attention augmented bilinear network for financial time-series data analysis

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arXiv 2017年

作者： Tran, Dat Thanh Iosifidis, Alexandros Kanniainen, Juho Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical & Computer Engineering Aarhus University Aarhus Denmark Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations. Copyright © 2017, The Authors. All rights reserved.

关键词： Time series