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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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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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DC MUX PUF: A Highly Reliable Feed-Back MUX PUF Based on Measuring Duty Cycle

DC MUX PUF: A Highly Reliable Feed-Back MUX PUF Based on Mea...

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IEEE International Conference on ASIC

作者： Yunhao Xu 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 Nanjing Uni. Aero. & Astro Nanjing China

Ring oscillator physical unclonable function (RO PUF) and multiplexer physical unclonable function (MUX PUF) are the two mostly used structures that make use of gate delay, but their reliability is not satisfactory enough. In this paper, a new structure, duty cycle multiplexer physical unclonable function (DC MUX PUF) is proposed to improve the reliability. A feed-back structure is added to original MUX PUF and decision method is also changed by measuring duty cycle. Then a simplified model is built to calculate the reliability of DC MUX PUF, a primary Monte Carlo simulation with this model is accomplished. The reliability of DC MUX PUF and the original MUX PUF are compared, and it is suggested that DC MUX PUF performs better than original MUX PUF considering noise variation. Then temperature variation is considered, and simulated by HSPICE. It is suggested that reliability of them are both above 95% when temperature varies. Uniqueness and randomness are all more than 90%. Then the complexity of two kinds of PUF are also taken into consideration. When challenge length becomes long, complexity of this design is relatively lower than original MUX PUF.

关键词： Physical unclonable function feed-back MUX PUF Monte Carlo model reliability

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5G ENTERING INTO A NEW ERA

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China communications 2017年第12期14卷 I0002-I0004页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinqhua UniversityBeiiingChina the State Key Laboratory of Integrated Services Networks Xidian University china communications ieee communications magazine ieee signal processing letters national digital switching system engineering&technological research center the college of information engineering shenzhen university the school of information and electronics beijing institute of technology bupt itu-r imt-2020 channel model ieee comm the school of computer and communication engineering in the university of science and technology beijing(ustb) state key lab.of integrated ser vice networks xidian university the nanjing university of posts and telecommunications(nupt)

The ever increasing wireless data services, such asimaging, video, audio, multimedia, etc., have de-mands for the very high speed wireless communicationsand network, which are unfortunately a bottleneck whencombining with the wireline core network. Users＇ arenow expecting high quality of experience with low-costdevices, ubiquitous connectivity, energy efficiency, highreliability, or even ultra-low latency if a vehicle terminalis applied.

关键词： 5G ENTERING A NEW ERA

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Data adaptive filtering approach to improve the classification accuracy of motor imagery for BCI 9

Data adaptive filtering approach to improve the classificati...

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9th International Conference on electrical and computer engineering, ICECE 2016

作者： Saha, Sanjoy Kumar Ali, Sujan Department of Computer Science and Engineering Begum Rokeya University Rangpur Bangladesh Department of Computer Science and Engineering University of Rajshahi Signal Processing and Computational Neuroscience Lab Rajshahi Bangladesh Department of Computer Science and Engineering Jatiya Kabi Kazi Nazrul Islam University Mymensingh Bangladesh

ISBN: (纸本)9781509029631

This paper presents a data adaptive pre-filtering method to enhance the motor imagery (MI) classification for brain computer interface (BCI) implementation electroencephalography (EEG). The filtering is implemented using multivariate empirical mode decomposition (MEMD). The MEMD decomposes multivariate EEG signal into a finite set of time varying basis functions called intrinsic mode functions (IMFs). Several IMFs are suppressed to improve the intelligibility of the EEG for better classification. The features from pre-filtered EEG are extracted by using common spatial pattern (CSP) which is a spatial filtering approach. The two class MI classification is performed by support vector machine (SVM). The proposed method is evaluated by BCI competition III of dataset IVa. Its performance is higher compared to that of the recently developed algorithm. © 2016 IEEE.

关键词： Empirical mode decomposition

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The VLSI architecture for channel estimation based on ADMA

The VLSI architecture for channel estimation based on ADMA

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International Conference on ASIC

作者： Xiaozhen Liu Hongxiang Xie Jin Sha Feifei Gao Shi Jin Xiaohu You Chuan Zhang National Mobile Communications Research Laboratory Southeast University Nanjing China Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS) School of Electronic Science and Engineering Nanjing University Nanjing China Department of Automation Tsinghua University Beijing China

Massive multiple-input multiple-output (M-MIMO) is a key technology of 5G wireless communication. The performance of an M-MIMO system is highly dependent on the speed and accuracy of obtaining the channel state information (CSI). The computational complexity of channel estimation for an M-MIMO system can be reduced by taking advantage of the sparsity of the M-MIMO channel. In this paper, we propose the hardware architecture for channel estimation based on angle division multiple access (ADMA) for the first time, including preamble, uplink (UL) and downlink (DL) training. For high-efficiency and low-complexity hardware implementation, quantization and approximation strategies have been discussed to better map the algorithm to VLSI architecture. Implementation techniques such as pipelining and systolic processing are also introduced for further improvement.

关键词： Channel estimation Hardware Training computer architecture Quantization (signal) Indexes Very large scale integration

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IDTxl: The information dynamics toolkit xl: A python package for the efficient analysis of multivariate information dynamics in networks

arXiv

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arXiv 2018年

作者： Wollstadt, Patricia Lizier, Joseph T. Vicente, Raul Finn, Conor Martinez-Zarzuela, Mario Mediano, Pedro Novelli, Leonardo Wibral, Michael MEG Unit Brain Imaging Center Goethe-University Frankfurt Fankfurt am Main Germany Centre for Complex Systems Faculty of Engineering and IT University of Sydney SydneyNSW Australia Computational Neuroscience Lab Institute of Computer Science Tartu Estonia Data61 CSIRO EppingNSW Australia Communications and Signal Theory and Telematics Engineering University of Valladolid Valladolid Spain Computational Neurodynamics Group Department of Computing Imperial College London London United Kingdom Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Campus Institute for Dynamics of Biological Networks Georg-August Universität Göttingen Germany

We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://***/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006;MacKay, 2003;Shannon, 1948) is the mathematical theory of information and its transmission over communication channels. Information theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels. Transfer entropy (TE) (Schreiber, 2000) is an extension of mutual information that measures the directed information transfer between time series of a source and a target variable. TE has become popular in many scientific disciplines to infer dependencies and whole networks from data. Notable application domains include neuroscience (Wibral, Vicente, & Lindner, 2014) and dynamical systems analysis (Lizier, Prokopenko, & Zomaya, 2014) (see Bossomaier, Barnett, Harré, & Lizier (2016) for an introduction to TE and a comprehensive discussion of its application). In the majority of the applications, TE is used in a bivariate fashion, where information transfer is quantified between all sourcetarget pairs. In a multivariate setting, however, such a bivariate analysis may infer spurious or redundant interactions, where multiple sources provide the same information about the target. Conversely, bivariate analysis may also miss synergistic

关键词： High level languages

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Cross-modal subspace learning for fine-grained sketch-based image retrieval

arXiv

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arXiv 2017年

作者： Xu, Peng Yin, Qiyue Huang, Yongye Song, Yi-Zhe Ma, Zhanyu Wang, Liang Xiang, Tao Bastiaan Kleijn, W. Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Communications and Signal Processing Group Victoria University of Wellington New Zealand

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

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Glossokinetic Potentials for a Tongue-Machine Interface: How Can We Trace Tongue Movements with Electrodes?

引用

IEEE Systems, Man and Cybernetics Magazine 2016年第1期2卷 6-13页

作者： Nam, Yunjun Koo, Bonkon Cichocki, Andrzej Choi, Seungjin Lab. for Advanced Brain Signal Processing RIKEN Wako Saitama351-0198 Japan School of Interdisciplinary Bioscience and Bioengineering POSTECH Pohang Gyeongbuk Korea Republic of Brain Science Institutde RIKEN Wako Saitama Japan Department of Computer Science and Engineering POSTECH Pohang Gyeongbuk Korea Republic of

Glossokinetic potentials (GKPs) refer to electrical responses involving tongue movements that are measured at electrodes placed on the scalp when the tip of the tongue touches tissue inside the mouth. GKP has been considered an electroencephalography (EEG) artifact that is removed to minimize the interference with signals from a cerebral region for successful EEG analysis. In this article, we emphasize a different side of GKP where we analyze its spatial patterns to trace tongue movements developing tongue-machine interfaces. We begin with a brief overview of GKP and its spatial patterns and then describe its potential applications to man-machine interfaces. First, we describe the spatial pattern of GKP for horizontal tongue movements and explain how it can be utilized to identify the position of the tongue. We also introduce a tongue-rudder system where this technique enables smooth control of an electric wheelchair. Then we describe GKP patterns for vertical and frontal tongue movements that are closely related to speech production. Based on this pattern, we discuss its application to silent speech recognition, which allows speech communication without using a sound. © 2015 IEEE.

关键词： Electroencephalography

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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