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[Front cover] Proc. IEEE (IF 10.252) Pub Date : 2021-01-19
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Proceedings of the IEEE publication information Proc. IEEE (IF 10.252) Pub Date : 2021-01-19
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Table of Contents Proc. IEEE (IF 10.252) Pub Date : 2021-01-19
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Lane-Free Artificial-Fluid Concept for Vehicular Traffic Proc. IEEE (IF 10.252) Pub Date : 2021-01-19 Markos Papageorgiou; Kyriakos-Simon Mountakis; Iasson Karafyllis; Ioannis Papamichail; Yibing Wang
Vehicular traffic has evolved as a crucial means for the transport of persons and goods, and its importance for the economic and social life of modern society cannot be overemphasized. On the other hand, recurrent vehicular traffic congestion, which appears on a daily basis, particularly in metropolitan areas, around the globe, has been a (increasingly) serious, in fact threatening, problem that calls
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Scanning the Issue Proc. IEEE (IF 10.252) Pub Date : 2021-01-19
In this month’s Point-of-View article, the authors propose a novel paradigm for vehicular traffic in the era of connected and automated vehicles (CAVs), which includes two combined principles: lane-free traffic and vehicle nudging; the latter implying that vehicles may be “pushing” from a distance (using communication or sensors) other vehicles in front of them. This traffic paradigm features several
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Multidimensional Index Modulation for 5G and Beyond Wireless Networks Proc. IEEE (IF 10.252) Pub Date : 2020-12-09 Seda Doğan Tusha; Armed Tusha; Ertugrul Basar; Huseyin Arslan
Index modulation (IM) provides a novel way for the transmission of additional data bits via the indices of the available transmit entities compared with classical communication schemes. This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits
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Future Special Issues/Special Sections of the Proceedings Proc. IEEE (IF 10.252) Pub Date : 2021-01-19
Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
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Back Cover Proc. IEEE (IF 10.252) Pub Date : 2021-01-19
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Front Cover Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Presents the front cover for this issue of the publication.
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Proceedings of the IEEE publication information Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of Contents Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Presents the table of contents for this issue of the publication.
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Bridging the Digital Divide: Success Depends on Content Provider and Application Developer Involvement Proc. IEEE (IF 10.252) Pub Date : 2020-12-18 Andrew Lappalainen; Catherine Rosenberg
Global connectivity is at an all-time high, and more users than ever before are participating in the online ecosystem. Despite this exciting phenomenon, opportunities to access the online world are not shared equally among the global population. Socioeconomic, political, and geographic factors all play roles in determining the extent to which one can be an online participant [1] . This resultant digital
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Point of View Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Point of View: Bridging the Digital Divide: Success Depends on Content Provider and Application Developer Involvement by A. Lappalainen and C. Rosenberg Users in isolated, remote communities who rely on satellites to connect to the Internet are limited by long-latency and low-bandwidth connections, and their quality of experience lags far behind others, creating a digital divide. In this month’s opinion
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The first eight years of electric power transmission and distribution—1873–1880 Proc. IEEE (IF 10.252) Pub Date : 2020-12-18 Adam Allerhand
Arc lighting powered from central stations preceded the better-known incandescent electric lighting powered from central stations and thrived well into the 20th century. After its introduction in 1880, incandescent electric lighting began to displace gas lighting in small indoor environments, but it failed to make inroads into arc lighting for streetlights and large indoor areas before high-intensity
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Future Special Issues/Special Sections of the Proceedings Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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TechRxiv: Share Your Preprint Research with the World! Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Call for Special Issue Proposals Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Back Cover Proc. IEEE (IF 10.252) Pub Date : 2020-12-18
Presents the back cover for this issue of the publication.
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2018-2020 Index Proceedings of the IEEE Vol. 106-108 Proc. IEEE (IF 10.252) Pub Date : 2020-11-24
Presents the 2020 subject/author index for this publication.
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Front Cover Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
Presents the front cover for this issue of the publication.
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Proceedings of the IEEE publication information Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of Contents Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
Presents the table of contents for this issue of the publication.
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Approximate Computing: From Circuits to Applications [Scanning the Issue] Proc. IEEE (IF 10.252) Pub Date : 2020-11-19 Weiqiang Liu; Fabrizio Lombardi; Michael Schulte
This special issue explores the technological contributions and developments of approximate computing at disparate levels and provides insight into exciting directions for the future.
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Circuit-Level Techniques for Logic and Memory Blocks in Approximate Computing Systemsx Proc. IEEE (IF 10.252) Pub Date : 2020-09-24 Saba Amanollahi; Mehdi Kamal; Ali Afzali-Kusha; Massoud Pedram
This article presents an overview of circuit-level techniques used for approximate computing (AC), including both computation and data storage units. After providing some background concept and methodology review, this article proceeds to provide a detailed review of prior art in circuit-level approximation techniques for data path and memory. The focus is on identifying key circuit-level approximation
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Security in Approximate Computing and Approximate Computing for Security: Challenges and Opportunities Proc. IEEE (IF 10.252) Pub Date : 2020-10-29 Weiqiang Liu; Chongyan Gu; Máire O’Neill; Gang Qu; Paolo Montuschi; Fabrizio Lombardi
Approximate computing is an advanced computational technique that trades the accuracy of computation results for better utilization of system resources. It has emerged as a new preferable paradigm over traditional computing architectures for many applications where inaccurate results are acceptable. However, approximate computing also introduces security vulnerabilities mainly due to the fact that
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Efficient AI System Design With Cross-Layer Approximate Computing Proc. IEEE (IF 10.252) Pub Date : 2020-11-10 Swagath Venkataramani; Xiao Sun; Naigang Wang; Chia-Yu Chen; Jungwook Choi; Mingu Kang; Ankur Agarwal; Jinwook Oh; Shubham Jain; Tina Babinsky; Nianzheng Cao; Thomas Fox; Bruce Fleischer; George Gristede; Michael Guillorn; Howard Haynie; Hiroshi Inoue; Kazuaki Ishizaki; Michael Klaiber; Shih-Hsien Lo; Gary Maier; Silvia Mueller; Michael Scheuermann; Eri Ogawa; Marcel Schaal; Mauricio Serrano; Joel
Advances in deep neural networks (DNNs) and the availability of massive real-world data have enabled superhuman levels of accuracy on many AI tasks and ushered the explosive growth of AI workloads across the spectrum of computing devices. However, their superior accuracy comes at a high computational cost, which necessitates approaches beyond traditional computing paradigms to improve their operational
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Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing Proc. IEEE (IF 10.252) Pub Date : 2020-11-09 Mingu Kang; Sujan K. Gonugondla; Naresh R. Shanbhag
This article provides an overview of recently proposed deep in-memory architectures (DIMAs) in SRAM for energy- and latency-efficient hardware realization of machine learning (ML) algorithms. DIMA tackles the data movement problem in von Neumann architectures head-on by deeply embedding mixed-signal computations into a conventional memory array. In doing so, it trades off its computational signal-to-noise
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Bell, Watson, Soft Iron, and the Insight That Commercialized the Magneto Telephone Proc. IEEE (IF 10.252) Pub Date : 2020-11-19 Ralph O. Meyer
This month’s history article focuses on the story behind the technological breakthrough that led to the world’s first commercial telephones.
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Future Special Issues/Special Sections of the Proceedings Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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TechRxiv: Share Your Preprint Research with the World! Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
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Call for Special Issue Proposals Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Back Cover Proc. IEEE (IF 10.252) Pub Date : 2020-11-19
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Front Cover Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
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Proceedings of the IEEE publication information Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of Contents Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
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Optimization for Data-Driven Learning and Control Proc. IEEE (IF 10.252) Pub Date : 2020-10-27 Usman A. Khan; Waheed U. Bajwa; Angelia Nedić; Michael G. Rabbat; Ali H. Sayed
This special issue provides a comprehensive overview of modern optimization tools and methods for the purposes of data-driven learning and control.
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A General Framework for Decentralized Optimization With First-Order Methods Proc. IEEE (IF 10.252) Pub Date : 2020-10-27 Ran Xin; Shi Pu; Angelia Nedić; Usman A. Khan
Decentralized optimization to minimize a finite sum of functions, distributed over a network of nodes, has been a significant area within control and signal-processing research due to its natural relevance to optimal control and signal estimation problems. More recently, the emergence of sophisticated computing and large-scale data science needs have led to a resurgence of activity in this area. In
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Stochastic Quasi-Newton Methods Proc. IEEE (IF 10.252) Pub Date : 2020-09-28 Aryan Mokhtari; Alejandro Ribeiro
Large-scale data science trains models for data sets containing massive numbers of samples. Training is often formulated as the solution of empirical risk minimization problems that are optimization programs whose complexity scales with the number of elements in the data set. Stochastic optimization methods overcome this challenge, but they come with their own set of limitations. This article discusses
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Distributed Optimization, Averaging via ADMM, and Network Topology Proc. IEEE (IF 10.252) Pub Date : 2020-09-22 Guilherme França; José Bento
There has been an increasing necessity for scalable optimization methods, especially due to the explosion in the size of data sets and model complexity in modern machine learning applications. Scalable solvers often distribute the computation over a network of processing units. For simple algorithms, such as gradient descent, the dependence of the convergence time with the topology of this network
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Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization Proc. IEEE (IF 10.252) Pub Date : 2020-10-27 Hassan Jaleel; Jeff S. Shamma
Recent advances in sensing, communication, and computing technologies have enabled the use of multirobot systems for practical applications such as surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time-implementable, require local information only, and guarantee some desired global performance. Distributed optimization
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Variance-Reduced Methods for Machine Learning Proc. IEEE (IF 10.252) Pub Date : 2020-10-16 Robert M. Gower; Mark Schmidt; Francis Bach; Peter Richtárik
Stochastic optimization lies at the heart of machine learning, and its cornerstone is stochastic gradient descent ( SGD ), a method introduced over 60 years ago. The last eight years have seen an exciting new development: variance reduction for stochastic optimization methods. These variance-reduced ( VR ) methods excel in settings where more than one pass through the training data is allowed, achieving
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Scaling-Up Distributed Processing of Data Streams for Machine Learning Proc. IEEE (IF 10.252) Pub Date : 2020-09-28 Matthew Nokleby; Haroon Raja; Waheed U. Bajwa
Emerging applications of machine learning in numerous areas—including online social networks, remote sensing, Internet-of-Things (IoT) systems, smart grids, and more—involve continuous gathering of and learning from streams of data samples. Real-time incorporation of streaming data into the learned machine learning models is essential for improved inference in these applications. Furthermore, these
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Advances in Asynchronous Parallel and Distributed Optimization Proc. IEEE (IF 10.252) Pub Date : 2020-10-08 By Mahmoud Assran; Arda Aytekin; Hamid Reza Feyzmahdavian; Mikael Johansson; Michael G. Rabbat
Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational
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Graph Learning Under Partial Observability Proc. IEEE (IF 10.252) Pub Date : 2020-08-25 Vincenzo Matta; Augusto Santos; Ali H. Sayed
Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There is a large body of literature examining the effect of the graph structure on the performance of decentralized processing strategies. In this article
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The bell versus gray telephone dispute: resolving a 144-year-old controversy [scanning our past] Proc. IEEE (IF 10.252) Pub Date : 2020-10-27 Benjamin Lathrop Brown
On March 7, 1876, Alexander Graham Bell was granted U.S. Patent No. 174,465 for the speaking telegraph, or telephone, one of the most valuable patents in history. Attorneys for Bell and Elisha Gray had submitted their respective ideas for the telephone to the U.S. Patent Office three weeks earlier, incredibly, on the same day, “Valentine’s Day, February 14.” (The year 1876 should be assumed hereafter
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Future Special Issues/Special Sections of the Proceedings Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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TechRxiv: Share Your Preprint Research with the World! Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Call for Special Issue Proposals Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Back Cover Proc. IEEE (IF 10.252) Pub Date : 2020-10-27
Presents the back cover for this issue of the publication.
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A Survey of Test and Reliability Solutions for Magnetic Random Access Memories Proc. IEEE (IF 10.252) Pub Date : 2020-10-27 Patrick Girard; Yuanqing Cheng; Arnaud Virazel; Weisheng Zhao; Rajendra Bishnoi; Mehdi B. Tahoori
Memories occupy most of the silicon area in nowadays’ system-on-chips and contribute to a significant part of system power consumption. Though widely used, nonvolatile Flash memories still suffer from several drawbacks. Magnetic random access memories (MRAMs) have the potential to mitigate most of the Flash shortcomings. Moreover, it is predicted that they could be used for DRAM and SRAM replacement
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Front Cover Proc. IEEE (IF 10.252) Pub Date : 2020-09-29
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Magnet-Free Nonreciprocity Proc. IEEE (IF 10.252) Pub Date : 2020-09-28 Andrea Alù
Electromagnetic waves, from radio signals to light, generally obey a strict symmetry in transmission: if they can travel from point A to point B, they can also travel backward from B to A with the same properties. This general symmetry, rooted in the fact that Maxwell’s equations are symmetric with respect to time and known as reciprocity, has profound implications on a variety of technologies and
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Tutorial on Electromagnetic Nonreciprocity and its Origins Proc. IEEE (IF 10.252) Pub Date : 2020-08-10 Viktar S. Asadchy; Mohammad Sajjad Mirmoosa; Ana Díaz-Rubio; Shanhui Fan; Sergei A. Tretyakov
This tutorial provides an intuitive and concrete description of the phenomena of electromagnetic nonreciprocity that will be useful for readers with engineering or physics backgrounds. The notion of time reversal and its different definitions are discussed with special emphasis on its relationship with the reciprocity concept. Starting from the Onsager reciprocal relations that are generally applicable
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