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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Hui Zhi Quan Yuan Jun Zhu Yanjun Hu Key Lab of Intelligent Computing and Signal Processing (Anhui University) Ministry of Education School of Electronics and Information Engineering Anhui University

ISBN: (纸本)9781509038237;9781509038220

A pilot allocation(PA) scheme is propose to reduce pilot contamination for time division duplex(TDD) massive multiple-input multiple-output(MIMO) system enabled twotier heterogeneous cellular network(Het Net). In order to reduce inter-tier interference during channel estimation, proposed scheme assigns mutually orthogonal pilots between macro-cells and small-cells, and pilots are reused among macro-cells with different pilot reuse factors. Comprehensive analyses and simulations of uplink average signal to interference plus noise ratio(SINR) and average achievable ergodic rate for the proposed scheme are developed. Simulation results show that the proposed PA scheme can reduce pilot contamination and improve the SINR and ergodic rate at base stations(BSs). In addition, the average SINR increases when pilot reuse factor grows, which shows the benefit of the proposed scheme. The average ergodic rate first becomes larger and then becomes smaller when pilot reuse factor becomes larger, which means the large number of pilot symbols limits the increase of ergodic rate.

关键词： massive MIMO HetNet pilot allocation scheme SINR ergodic rate

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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A new multilevel reversible bit-planes data hiding technique based on histogram shifting of efficient compressed domain

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Vietnam Journal of Computer Science 2018年第2期5卷 185-196页

作者： Rashid Abbasi Bin Luo Gohar Rehman Haseeb Hassan Muhammad Shahid Iqbal Lixiang Xu School of Computer and Technology Anhui University Hefei People’s Republic of China Key Lab of Intelligent Computing and Signal Processing of MOE and School of Computer and Technology Anhui University Hefei People’s Republic of China College of Computer Science Technology Chongqing University Chongqing People’s Republic of China

In this paper, we proposed a new technique for reversible data hiding based on efficient compressed domain with multiple bit planes. We conducted a sequence of experiments to use block division scheme to appraise the result with different parameters and amended the probability of zero point in every block of histogram. This scheme attained more embedding capacity and high-quality of stego-image. Experimental consequences prove that the proposed method effectively achieved the objective of high embedding capacity and sustaining the quality of image.

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A broadband dual-polarized magneto-electric dipole antenna for 2G/3G/LTE/WiMAX applications

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Progress In Electromagnetics Research C 2017年 73卷 127-136页

作者： Li, Zuming Sun, Yufa Yang, Ming Wu, Zhifeng Tang, Peiquan Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

A novel broadband dual-polarized magneto-electric (ME) dipole antenna is proposed for 2G/3G/LTE/WiMAX applications. The proposed antenna has stair-shaped feeding strips to impart a wide impedance bandwidth to it and a rectangular box-shaped reflector to enhance its stability in radiation patterns and high gain over the operating frequencies. The measured results show that a common impedance bandwidth is 80% with standing-wave ratio (SWR) ≤ 1.5 from 1.68 to 3.92 GHz, and port-to-port isolation larger than 25 dB within the bandwidth. The measured antenna gains vary from 9.2 to 12 dBi and from 9.2 to 11.8 dBi for port 1 and port 2, respectively. The antenna has nearly symmetrical radiation patterns with low back-lobe radiation both in horizontal and vertical planes, and broadside radiation patterns with narrow beam can also be obtained. © 2017, Electromagnetics Academy. All rights reserved.

关键词： Dipole antennas

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A Wideband Dual-Band Magneto-Electric Dipole Antenna With Modified Feed Structure

A Wideband Dual-Band Magneto-Electric Dipole Antenna With Mo...

引用

IEEE International Conference on Microwave and Millimeter Wave Technology

作者： Fan Li Yufa Sun Haoran Zhu Hongyu Fang Ministry of Education Anhui University Hefei China Ministry of Education Key Lab of Intelligent Computing & Signal Processing Anhui University Hefei China

In this paper, a novel wideband dual-band magneto-electric dipole antenna with modified feed structure is proposed. Double-layer patches are utilized to design a pair of folding metal. The double-layer patches dipole can generate two resonant frequencies, which are employed to form a dual-band magneto-electric dipole antenna. A stair-shaped feeding strip with a hexagonal structure is designed to modify the impedance matching. It is found from simulated results that two operation bands with bandwidths of 60% from 1.61 to 2.99 GHz and 31.9% from 4.99 to 6.89 GHz under the return loss level of 10 dB were achieved. The gain of the antenna ranged from 7.56 to 10.56 dBi. Furthermore, low cross-polarization level and low back-radiation are also obtained with the proposed antenna.

关键词： Dipole antennas Dual band Antenna feeds Broadband antennas Magnetoelectric effects Impedance matching Magnetic resonance

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Texture classification in extreme scale variations using GANet

arXiv

引用

arXiv 2018年

作者： Liu, Li Chen, Jie Zhao, Guoying Fieguth, Paul Chen, Xilin Pietikäinen, Matti Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China

Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change in scale can have a dramatic impact on texture appearance, to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are amongst the hardest to handle. In this work we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a Genetic Algorithm to change the units in the hidden layers during network training, in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a FV-CNN (Fisher Vector pooling of a Convolutional Neural Network filter bank) feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing superior performance compared to the state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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An Improved FTRLS Filtering Algorithm and Its Simulation Analysis

An Improved FTRLS Filtering Algorithm and Its Simulation Ana...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Jun Zhu Jingjing Zhang Qiang Chen Key Lab of Intelligent Computing and Signal Processing Ministry of Education China School of Electronics and Information Engineering Anhui University

Due to the large error of LMS algorithm and the slow convergence rate, the recursive least squares(RLS) algorithm is proposed. Although the recursive estimation error is greatly reduced, the convergence rate is one order of magnitude higher than that of the general LMS filter. When the order N increases, the amount of calculation for a single iteration of the RLS algorithm is increased significantly. Aiming at these problems, this paper proposes an improved FTRLS filtering algorithm, which is to find out the amount of large error and accumulate the error, and then make the error feedback to make the algorithm more stable. The analysis of MATlab simulation results show that the improved algorithm can improve the convergence speed and stability of the algorithm, and effectively reduce the convergence of the noise.

关键词： adaptive filtering Least Mean Squares(LMS) Recursive Least Squares(RLS) Fast Transverse Recursive Least Squares(FTRLS) improved FTRLS algorithm convergence rate

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Low-complexity linear precoding for pilot contamination mitigation in multi-cell massive MIMO systems

Low-complexity linear precoding for pilot contamination miti...

引用

IEEE International Symposium on intelligent signal processing and Communication Systems (ISPACS)

作者： Manman Feng Yaohua Xu Ministry of Education Key Lab of Intelligent Computing and Signal Processing China School of Electronics and Information Engineering Anhui University Hefei China

The pilot contamination is caused by non-orthogonal pilot sequences reuse in uplink, which affects the performance of Massive MIMO systems seriously, so it is necessary to mitigate pilot contamination. In this paper, we propose a pilot contamination precoding scheme to mitigate multi-cell pilot contamination. In the uplink, we propose a cell-defined training scheme, where the same pilot sequence is used in the same cell, and different cells use orthogonal pilot sequences, which eliminates inter-cell interference and introduces intra-cell interference artificially. In the downlink, we adopt Truncated Polynomial Expansion (TPE) precoding to reduce intra-cell interference, since the truncated polynomial of TPE precoding can replace the matrix inversion of Regularized Zero-Forcing (RZF) precoding, which reduces the complexity of RZF precoding and approximates the performance of RZF precoding by suitable truncation orders. Simulation results show the effectiveness of the proposed scheme.

关键词： Precoding Base stations Contamination Interference Partial transmit sequences Antennas Complexity theory

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