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作者： Hashemi, Seyyed Ali Mondelli, Marco Hassani, S. Hamed Condo, Carlo Urbanke, Rudiger L. Gross, Warren J. Department of Electrical and Computer Engineering McGill University MontrealQCH3A 0E9 Canada Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Mathematical and Algorithmic Sciences Lab Paris Research Center Huawei Technologies Co. Boulogne-Billancourt92100 France School of Computer and Communication Sciences EPFL Lausanne1015 Switzerland

Polar codes represent one of the major recent breakthroughs in coding theory and, because of their attractive features, they have been selected for the incoming 5G standard. As such, a lot of attention has been devoted to the development of decoding algorithms with good error performance and efficient hardware implementation. One of the leading candidates in this regard is represented by successive-cancelation list (SCL) decoding. However, its hardware implementation requires a large amount of memory. Recently, a partitioned SCL (PSCL) decoder has been proposed to significantly reduce the memory consumption. In this paper, we consider the paradigm of PSCL decoding from a practical standpoint, and we provide several improvements. First, by changing the target signal-to-noise ratio and consequently modifying the construction of the code, we are able to improve the performance at no additional computational, latency, or memory cost. Second, we bridge the performance gap between SCL and PSCL decoding by introducing a generalized PSCL decoder and a layered PSCL decoder. In this way, we obtain almost the same performance of the SCL decoder with a significantly lower memory requirement, as testified by hardware implementation results. Third, we present an optimal scheme to allocate cyclic redundancy checks. Finally, we provide a lower bound on the list size that guarantees optimal maximum a posteriori performance for the binary erasure channel. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Social-feature enabled communications among devices towards smart IoT community

arXiv

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arXiv 2018年

作者： Du, Qinghe Song, Houbing Zhu, Xuejie Department of Information and Communications Engineering Xi'An Jiaotong University China Shaanxi Smart Networks and Ubiquitous Access Research Center Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States

Future IoT is expected to get ubiquitous connection and access in a global scale. In the meantime, with the empowered communications capability for IoT devices, IoT will evolve to be highly autonomous, even under the help from infrastructure, and thus will gradually establish the smart IoT community. One of the major challenges imposed on the smart IoT communities is the socialization of IoT communications, because massive IoT accesses make centralized control very hard and also face the shortage of spectrum resources. Towards these issues, we in this article first present the overview and discussions on social features affecting connections among devices. Then, we motivate studies on the statistical characteristics of social features for connections among devices towards smart IoT. We further propose the queuing model under unified asymptotic analyses framework to characterize the statistical social features, with emphases on typical social metrics such as credit, reputation, centrality, etc. How to apply these features for network optimization is further suggested. Finally, we share our opinion on the open problems of social-aware design towards future smart IoT. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Waveguide for vortex mode generation in HVAC cloud management communication

引用

AIP Conference Proceedings 2020年第1期2203卷

作者： N. Ali A. Amphawan M. Hariz Hafizalshah S. A. Aljunid R. Endut C. B. M. Rashidi M. N. M. Yasin M. I. Shapiai Semiconductor Photonics & Integrated Lightwave Systems SPILS School of Microelectronic Engineering Universiti Malaysia Perlis Pauh Putra Main Campus 02600 Arau Perlis Malaysia Smart 5G Optical Lab University Malaysia of Computer Science & Engineering 3410 Jalan Teknokrat 3 Cyber 4 63000 Cyberjaya Selangor Malaysia Advanced Communication Engineering Centre of Excellence Universiti Malaysia Perlis 02600 Arau Perlis Malaysia Bioelectromagnetics Research Group School of Microelectronic Engineering Universiti Malaysia Perlis Pauh Putra Main Campus 02600 Arau Perlis Malaysia Department of Electronic Systems Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Optical modes allow for the transmission of data by propagating light in a singular coherent form along the channel. By constructing a special waveguide structure, a unique mode may be formed in a plane perpendicular to the transmission axis. This paper elucidates on the design of a waveguide to generate unique vortex modes and analyses the properties of the generated modes.

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Decentralized controller design in flexure-linked gantry by H2 guaranteed cost optimization approach 18

Decentralized controller design in flexure-linked gantry by ...

引用

18th International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2018

作者： Ma, Jun Chen, Si-Lu Liang, Wenyu Teo, Chek Sing Tay, Arthur Al Mamun, Abdullah Tan, Kok Kiong SIMTech-NUS Joint Lab. on Precision Motion Systems Department of Electrical and Computer Engineering National University of Singapore Singapore117582 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Agency for Science Technology and Research Singapore Institute of Manufacturing Technology Singapore138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315201 China

ISBN: (纸本)9780995775121

Dual-drive H-gantry is widely used in many industrial processes that require high-speed and high-precision Cartesian motion. Unlike the rigid-linked gantry design, the flexure-linked design is able to prevent the damage of the cross-arm because a small degree of rotation angle is possible. However, the flexure-linked design may induce the resonant modes of the gantry because of high frequency control signals. To maintain the precision tracking of two carriages and minimize the chattering of control efforts, we aim to seek the most suitable flexure joints among the available ones while optimizing the decentralized feedback controller parameters under model uncertainties. In this way, good tracking performance is achieved without costly system re-design. In this work, we formulate the mechatronic design problem as an H2 guaranteed cost control problem. All the stabilizing feedback controller gains are parameterized over a convex set, and the global optimum is obtained by means of an outer-linearization-based algorithm. Experiments are conducted and the results successfully validate the effectiveness of the proposed design approach. © 2018 CURRAN-CONFERENCE. All rights reserved.

关键词： Convex optimization Dual-drive H-gantry Flexure Mechatronic design Optimal control Parallel mechanism

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Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks

arXiv

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arXiv 2019年

作者： Kleyko, Denis Kheffache, Mansour Frady, E. Paxon Wiklund, Urban Osipov, Evgeny The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States Intelligent Systems Lab Research Institutes of Sweden Kista164 40 Sweden Netlight Consulting AB Stockholm111 53 Sweden The Department of Radiation Sciences Biomedical Engineering Umeå University Umeå901 87 Sweden The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå971 87 Sweden

The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this article, we focus on resource-efficient randomly connected neural networks known as Random Vector Functional Link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world datasets from the UCI Machine Learning Repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small n-bits integers, which results in a computationally efficient architecture. Finally, through hardware Field-Programmable Gate Array (FPGA) implementations, we show that such an approach consumes approximately eleven times less energy than that of the conventional RVFL. © 2019, CC BY.

关键词： Network coding

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Driving skill modeling using neural networks for performance-based haptic assistance

arXiv

引用

arXiv 2018年

作者： Lee, Hojin Kim, Hyoungkyun Choi, Seungmoon Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Department of Mechanical Engineering Samsung Research Seoul Korea Republic of Smart Device Team Samsung Research Seoul Korea Republic of

This paper addresses a data-driven framework, modeling expert driving skills for performance-based haptic assistance using neural networks (NNs). We have built a haptic driving training simulator to collect expert driving data and to provide proper haptic feedback. We establish an expert driving skill model by training NNs with the collected data. Then, the skill model is applied to performance-based haptic assistance to provide optimized references of the steering/pedaling movements. We evaluate the skill model and its application to performance-based haptic assistance in two user experiments. The results of the first experiment demonstrate that our skill model has appropriately captured experts’ steering/pedaling skills. The results of the second experiment show that our performance-based haptic assistance can help novice drivers perform steering as expert drivers, but cannot assist their pedaling performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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ITSO: A novel Inverse Transform Sampling-based Optimization algorithm for stochastic search

arXiv

引用

arXiv 2019年

作者： Bakas, Nikolaos P. Plevris, Vagelis Langousis, Andreas Chatzichristofis, Savvas A. Computation-based Science and Technology Research Center The Cyprus Institute 20 Konstantinou Kavafi Street Aglantzia Nicosia2121 Cyprus Department of Civil Engineering and Energy Technology OsloMet—Oslo Metropolitan University Pilestredet 35 Oslo0166 Norway Department of Civil Engineering University of Patras Patras265 04 Greece Intelligent Systems Lab Department of Computer Science Neapolis University Pafos 2 Danais Avenue Pafos8042 Cyprus

Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods, as well as engineering and Business applications. Following recent works on the theoretical deficiencies of AI, a rigor context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of a presumed inspiration, thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is by definition satisfied. A theoretical proof is provided, concluding that only falling into the proposed framework, either directly or incidentally, any optimization algorithm converges in the fastest possible time. The numerical experiments, verify the theoretical results on the efficacy of the algorithm apropos reaching the optimum, as fast as possible. © 2019, CC BY.

关键词： Optimization algorithms

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Corrigendum to “Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models” [Renew Sustain Energy Rev 179 (2023) 113273]

引用

Renewable and Sustainable Energy Reviews 2023年 185卷

作者： J.M. Schwidtal P. Piccini M. Troncia R. Chitchyan M. Montakhabi C. Francis A. Gorbatcheva T. Capper M.A. Mustafa M. Andoni V. Robu M. Bahloul I.J. Scott T. Mbavarira J.M. España L. Kiesling Department of Industrial Engineering University of Padua Via Giovanni Gradengio 6/a Padova 35131 Italy Centre for Environment and Sustainability University of Surrey Guildford Surrey GU2 7XH United Kingdom Institute for Research in Technology (IIT) Comillas Pontifical University Madrid 28015 Spain Department of Computer Science University of Bristol Bristol BS8 1UB United Kingdom Imec-SMIT Vrije Universiteit Brussel Pleinlaan 9 Brussels 1050 Belgium Garwood Center for Corporate Innovation Haas School of Business University of California Berkeley 2220 Piedmont Ave Berkeley CA 94720 United States Imperial College Business School Imperial College London London SW7 2AZ UK Center of Energy Technologies (CET) Department of Business Development and Technology (BTECH) Aarhus University Birk Centerpark 15 7400 Herning Denmark School of Engineering University of Edinburgh Edinburgh EH9 3FB United Kingdom Energy Institute University College London 14 Upper Woburn Place London WC1H 0NN United Kingdom Tyndall Centre for Climate Change Research School of Engineering University of Manchester Oxford Road Manchester M13 9PL United Kingdom Department of Computer Science University of Manchester Oxford Road Manchester M13 9PL United Kingdom imec-COSIC KU Leuven Kasteelpark Arenberg 10 Bus 2452 Leuven-Heverlee B-3001 Belgium Smart Systems Group School of Engineer and Physical Sciences Heriot-Watt University Edinburgh EH14 4AS United Kingdom James Watt School of Engineering University of Glasgow Glasgow G12 8QQ United Kingdom CWI National Research Institute for Mathematics and Computer Science Amsterdam 1098XG Netherlands International Energy Research Centre Tyndall National Institute Cork Ireland NOVA Information Management School (NOVA IMS) Universidade Nova de Lisboa Campus de Campolide Lisboa 1070-312 Portugal Institute for Innovation and Technology Management Lucerne University of Applied Sciences & Arts Horw 6048 Switzerland Universidad

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