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Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"

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arXiv 2018年

作者： Khan, Shujaat Wahab, Abdul Naseem, Imran Moinuddin, Muhammad Bio-imaging and Signal Processing Laboratory Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Daejeon Yuseong-gu34141 Korea Republic of Department of Mathematics University of Education Attock Campus43600 Pakistan School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia College of Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering Department King Abdulaziz University Jeddah Saudi Arabia

The purpose of this note is to highlight some critical flaws in recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in "Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization" [Non-linear Dyn. 88(2), 839-858 (2017)]. It is substantiated that these algorithms do not always converge whereas they have no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning supported by simulations. Copyright © 2018, The Authors. All rights reserved.

关键词： Equalizers

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

Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

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IET communications 2017年第12期11卷 1961-1969页

作者： Lei, Hongjiang Zhang, Jianming Park, Ki-Hong Ansari, Imran Shafique Pan, Gaofeng Alouini, Mohamed-Slim Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering (ECEN) Texas A and M University at Qatar (TAMUQ) Education City Doha Qatar Chongqing Key Laboratory of Non-linear Circuits and Intelligent Information Processing Southwest University Chongqing 400715 China

This study investigates the secrecy outage performance of a single-input multiple-output underlay cognitive radio network (CRN) with outdated channel state information (CSI). The confidential messages are transmitted from transmitter to the destination, while a multi-antenna eavesdropper exists. The maximal ratio combining and selection combining schemes are utilised at the receivers to improve the quality of the received signal-to-noise ratio. The exact and asymptotic closed-form expressions of secrecy outage probability are derived, and simulation results are provided to verify the authors' proposed analytical results. The results reveal that imperfect CSI of main channels deteriorates the secrecy outage performance while that of eavesdropping and interfering channels has contrary effect, and only a unity diversity order can be obtained in underlay CRNs with imperfect CSI. © The Institution of engineering and Technology.

关键词： antenna arrays cognitive radio diversity reception probability radio networks telecommunication security

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)

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

A sampling framework for solving physics-driven inverse source problems

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arXiv 2017年

作者： Murray-Bruce, John Luigi Dragotti, Pier Department of Electrical and Computer Engineering Boston University MA02215 United States Communications and Signal Processing Group Department of Electrical and Electronic Engineering Imperial College LondonSW7 2AZ United Kingdom

Partial differential equations are central to describing many physical phenomena. In many applications these phenomena are observed through a sensor network, with the aim of inferring their underlying properties. Leveraging from certain results in sampling and approximation theory, we present a new framework for solving a class of inverse source problems for physical fields governed by linear partial differential equations. Specifically, we demonstrate that the unknown field sources can be recovered from a sequence of, so called, generalised measurements by using multidimensional frequency estimation techniques. Next we show that-for physics-driven fields-this sequence of generalised measurements can be estimated by computing a linear weighted-sum of the sensor measurements;whereby the exact weights (of the sums) correspond to those that reproduce multidimensional exponentials, when used to linearly combine translates of a particular prototype function related to the Green's function of our underlying field. Explicit formulae are then derived for the sequence of weights, that map sensor samples to the exact sequence of generalised measurements when the Green's function satisfies the generalised Strang-Fix condition. Otherwise, the same mapping yields a close approximation of the generalised measurements. Based on this new framework we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify their performance. Copyright © 2017, The Authors. All rights reserved.

关键词： Inverse problems

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

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