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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

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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)

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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

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

arXiv

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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

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

arXiv

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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

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

Tensor representation in high-frequency financial data for p...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Dat Thanh Tran Martin Magris Juho Kanniainen Moncef Gabbouj Alexandros Iosifidis 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 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.

关键词： Tensile stress Mathematical model Recurrent neural networks Feature extraction Heuristic algorithms Prediction algorithms

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Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods

arXiv

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arXiv 2017年

作者： Ntakaris, Adamantios Magris, Martin Kanniainen, Juho Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Korkeakoulunkatu 1 Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Korkeakoulunkatu 8 Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Inge Lehmanns Gade 10 Aarhus Denmark

Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ≈4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Regression analysis

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Biological Modeling Challenges in a Multiphysics Approach

Biological Modeling Challenges in a Multiphysics Approach

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IEEE Pulsed Power Conference

作者： Jessy Mathault Hamza Landari Frederic Tessier Paul Fortier Amine Miled LABioTRON Bioengineering Research Laboratory Electrical and Computer Engineering Department Universite Laval Radiocommunications and Signal Processing Laboratory (LRTS) Electrical and Computer Engineering Department Universite Laval

ISBN: (纸本)9781509063901

Modeling biological behaviour requires a multidisciplinary approach and very large computational resources. In this paper, we present an overview of our research work with regards to biological neuron network (NN) modeling, electrochemical reaction emulation for neurotransmitters and finite element modeling (FEM) of a very low pressure micropump for neurotransmitter concentration modulations. Three different approaches have been explored, namely MATLAB simulations of biological NN, MATLAB simulations of electro-chemical reactions and the FEM approach. Our objective through biological NN modeling is to investigate the need to develop a dedicated chip for a NN implementation or to use a strictly software approach. The objective of the electrochemical emulator is to provide a tool which can help us to understand how to decompose cyclic voltammetry curves into more conventional and easy to interpret graphs. However, although the three approaches appear independent, they are in fact all related as we investigate the computer performances needed for biological modeling.

关键词： Modeling Biological Neurotransmitters Finite element method interdisciplinary approach Biological Models simulating Research work MATLAB Cyclic voltammetry chemical reactions

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Notice of Violation of IEEE Publication Principles: Application of LDPC codes on PUF error correction based on code-offset construction

Notice of Violation of IEEE Publication Principles: Applicat...

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International Conference on ASIC

作者： Kai Sun Yingjie Lao Weiqiang Liu Xiaohu You Chuan Zhang National Mobile Communications Research Laboratory Southeast University Nanjing China Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS) Department of Electrical and Computer Engineering Clemson University Clemson SC USA College of Electronic and Information Engineering NUAA Nanjing China

Notice of Violation of IEEE Publication Principles "Application of LDPC Codes on PUF Error Correction Based on Code-offset Construction" by Kai Sun, Yingjie Lao, Weiqiang Liu, Xiaohu You, and Chuan Zhang Proceedings of the IEEE 12th International Conference on ASIC (ASICON), October 2017, pp. 867-870 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper copies original text from the papers cited below. The original text was copied with references to the original author(s) and/or paper titles, but did not delineate sufficiently to identify the reused material. The first author, Kai Sun, was solely responsible for the misconduct, which was done without the knowledge of the other authors "On Error Correction for Physical Unclonable Functions" by Sven Puchinger, Sven Muelich, Martin Bossert, Matthias Hiller, Georg Sigl in the Proceedings of the International ITG Conference on Systems, Communications and Coding, February 2015 "A New Error Correction Scheme for Physical Unclonable Functions" by Sven Muelich, Martin Bossert in the Proceedings of the International ITG Conference on Systems, Communications and Coding, February 2017 As a newly emerging technology, physical unclonable functions (PUFs) have drawn a lot of attentions. For their cryptographic applications, error correction is an indispensable component to fight against environmental factors which influence PUFs' performance. Recently, BCH codes have been applied to the error correction part to enhance the stability of an cryptographic system using PUFs. In this paper, low-density parity-check (LDPC) codes are first used to correct errors caused by environmental factors and shown to perform better than BCH codes in some conditions. In addition, normalized minsum algorithm has been considered to balance the performance and complexity of LDPC decoding.

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Reconfigurable accelerator design platform for ultrasonic signal processing and imaging applications

Reconfigurable accelerator design platform for ultrasonic si...

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IEEE International Ultrasonics Symposium

作者： Paul Gal Jafar Saniie Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago U.S.A.

ISBN: (纸本)9781467398985

Ultrasonic testing and imaging systems have evolved from a basic single transducer system to arrays capable of 3-dimensional scans. These systems need to be flexible and also reconfigurable in order to acquire and process a vast amount of data. The system must also allow for a variety of parameters like the data acquisition rate, signal lengths and processing characteristics which need to be reconfigurable for adaptability to different applications. This study presents a reconfigurable hardware/software co-design system including specific hardware accelerators to acquire and perform ultrasonic signal processing efficiently. For this study, the architecture of the developed ultrasonic system is comprised of two different functional components. One component is specially designed to process the data while the other component gathers, compresses and transfers data which can later be restored and analyzed. For data transfer a simple and consequently efficient real-time operating system and Ethernet connective is developed. This is all implemented on the ZEDBoard Zynq SoC for maximum flexibility and performance. The data handling component is built with support for gathering, compressing, reconstructing, and transferring data to other platforms via Ethernet communication. The different variations of the designs allow the system designers the choice of applying the best solution for their specific applications.

关键词： Acoustics Discrete wavelet transforms Adders signal processing Field programmable gate arrays Real-time systems Image reconstruction Field programmable gate arrays discrete wavelet transform adders signal processing configurability real-time systems image reconstruction Acoustics ultrasonic signal ethernet communication hardware accelerator

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