List of Symposia

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The 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP) will be held at the Disneyland Hotel in Anaheim, CA, on November 26-28, 2018. GlobalSIP focuses on signal and information processing with an emphasis on upand-coming signal processing themes. The conference features world-class plenary speeches, distinguished symposium talks, tutorials, exhibits, oral and poster sessions, and panels. GlobalSIP is comprised of co-located General Symposium and symposia selected based on responses to the call-for-symposia proposals.

Organized by

General Chairs

  • Shuguang Cui, University of California, Davis
  • Hamid Jafarkhani, University of California, Irvine

Technical Program Chairs

  • Dinei Florencio, Microsoft Corporation
  • Amy Reibman, Purdue University
  • Lee Swindlehurst, University of California Irvine
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Society aging is a concerning phenomena in many modern countries. It increases the incidence rate of age-related neuromuscular movement and will result in excessive economic pressures on health-care systems worldwide. One potential solution is to develop smart Medical Cyber-physical Systems (CPSs) that are capable of: (i) Providing safe, optimal, effective and affordable means of neuro-rehabilitation, and; (ii) Assisting patients in performing activities of daily living. Bio-signal processing and machine learning solutions are in the heart of the above-mentioned Medical CPSs, and are essential to guarantee compatible, intelligent, compliant, appropriate, safe, and adaptive interaction between the human-in-need (i.e., patient) and the CPS (e.g., a rehabilitative robotic arm, a powered prosthetic device, or an active exoskeleton). The spirit and wide scope of the bio-signal processing and machine learning applications in Medical CPSs and in particular rehabilitation and assistive systems calls for a focused investigation of state-of-the-art techniques to further advance this field.

Organized by

General Chairs

  • Arash Mohammadi, Concordia University, Canada
  • S. Farokh Atashzar, CSTAR and Western University, Canada

Technical Program Chairs

  • Konstantinos N. Plataniotis, University of Toronto
  • Rajni V. Patel, CSTAR and Western University, Canada
  • Mahdi Tavakoli, University of Alberta
  • Mahya Shahbazi, CSTAR and Western University, Canada
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Witnessing its success in fields including computer vision, speech recognition, bioinformatics, and so on, researchers are considering deep learning for wireless communications. Preliminary results in channel estimation and baseband processing have shown that deep learning can help to understand the wireless contents and produce results comparable and in some cases superior to classic approaches. Though such initiatives have been named, their design, implementation, and optimization are not complete and in infancy. This symposium aims to bring together experts from the design and implementation, computer science as well as communications communities and provide a forum for challenges and solutions of deep learning for wireless communications, with special interests on design and implementation.

Organized by

General Chairs

  • Chuan Zhang, Southeast University
  • Yeong-Luh Ueng, National Tsing Hua University, Taiwan

Technical Program Chairs

  • Yair Be'ery, Tel Aviv University
  • Christoph Studer, Cornell University
  • Warren J. Gross, McGill University
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With the rapid advances in sensing, communication, and storage technologies, distributed data acquisition is now ubiquitous in many areas of engineering, biological, and social sciences. For example, the large-scale implementation of advanced metering systems in the smart grids enables real time collection of a huge amount of distributed data (voltages, phases, etc.), the understanding of which is critical in improving the overall performance of the future power systems. Other examples of distributed data generation include high-resolution videos from a network of surveillance systems, interactions on a social network, and environmental data from sensor networks. Timely and effectively processing of such large amount of distributed, and possibly corrupted and/or online data requires not only novel data processing techniques, but also a deep understanding of the underlying network properties of the physical system that generates the data, e.g., the network topology, the processing capability of each distributed node, the nature of the data, etc. These sophisticated characteristics bring new challenges for the design and analysis of distributed learning and optimization algorithms. This symposium aims to bring together researchers and experts in the fields of signal processing, machine learning, control, optimization, network sciences, cyber-physical systems to address the emerging challenges related to this topic. Emphasis will be given to theory and application of distributed signal processing and cyber-physicalsystems, as well as advanced distributed control and optimization techniques.

Organized by

General Chair

  • Zhi-Quan Luo, University of Minnesota, The Chinese University of Hong Kong, Shenzhen

Technical Program Chairs

  • Necdet Serhat Aybat, Pennsylvania State University
  • Mingyi Hong, University of Minnesota
  • Qing Ling, Sun Yat-Sen University
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Understanding networks and networked behavior has emerged as one of the foremost intellectual challenges of the 21st century. Networks define an underlying notion of proximity and the main object of interest is a signal defined on top of the graph, i.e., data associated with the nodes or edges of the network. This is precisely the focus of graph signal processing, studying the interplay between the underlying network topology and features of signals defined on networks. This symposium aims to bring together researchers and practitioners of graph signal processing to discuss the latest advances in theory, methods, and applications, as well as open problems and challenges.

Organized by

General Chair

  • Gonzalo Mateos, University of Rochester

Technical Program Chairs

  • Santiago Segarra, Massachusetts Institute of Technology
  • Sundeep Chepuri, Delft University of Technology
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Industry and governments globally are committing greater resources to develop new technologies as part of the overall green strategies against climate change and to reduce greenhouse gas (GHG) emissions. Given the dominance of electricity consumption in ICT sector’s GHG footprint, it is imperative to focus on energy efficiency and to utilize as much renewable power sources as possible. Moreover, green communications and networking technologies play another direct role in facilitating low GHG emissions of other industries, such as electricity grid, transport, buildings, manufacturing, etc. This symposium aims to bring together researchers and practitioners to discuss state-of –the-art developments and open research problems.

Organized by

General Chairs

  • Ender Ayanoglu, University of California Irvine
  • Victor C. M. Leung, University of British Columbia

Technical Program Chairs

  • Zhi Ding, University of California Davis
  • F. Richard Yu, Carleton University
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The energy grid is undergoing a rapid transformation toward a more sustainable, efficient, resilient and secure infrastructure, in which advanced information science and technology play an essential role. New challenges emerge with the increasing penetration of renewable energy sources, extreme weather conditions, and cyber and physical security threats. Meanwhile, promising opportunities arise with nextgeneration monitoring and control devices as well as increasing customer participation. The rich algorithmic and analytical toolsets developed by the classical signal processing community along with contemporary and emerging advancements in the field are actively contributing to the development of innovative solutions for smart energy systems. Conversely, smart grid challenges are accelerating developments in core signal and information processing theory that are also applicable to new domains. This symposium aims to bring together researchers in the fields of information and signal processing, learning, and optimization for smart energy infrastructures.

Organized by

General Chair

  • Deepa Kundur, University of Toronto
  • Yue Zhao, Stony Brook University

Technical Program Chairs

  • Yue Zhao, Stony Brook University
  • Hao Zhu, University of Texas
  • Emiliano Dall'Anese, National Renewable Energy Laboratory
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Diverse Applications, Common Approaches? Multispectral and Hyperspectral imaging methods are widely used in diverse applications including airborne and satellite remote sensing, biological microscopy, and diagnostic biomedical imaging systems. There has been a corresponding growth in algorithms and architectures for processing and analyzing multi- and hyper-spectral images, and there is abundant opportunity for researchers to learn image processing problems, approaches, insights, and algorithm designs from other areas. The goal of this symposium is to provide an opportunity for researchers to share insights across disciplines and application areas. Impact: The impact of multi- and hyper-spectral imaging is already impressive. Satellite-based and airborne platforms are enabling remote sensing of changes, anomalies, and trends in agricultural, mineral, military, and civilian land-use patterns. Similarly, in the biomedical multi- and hyperspectral imaging methods are enabling the spatial distribution and dynamics of multiple molecular markers in a manner that preserves their relative spatial context, in living biological systems (in vivo or in vitro), and/or fixed tissue samples of biomedical interest (e.g., biopsies), and are finding applications in basic scientific discovery, drug screening, and biomolecular tissue profiling. Invitation: Researchers in signal processing and diverse application domains are invited to submit their contributions.

Organized by

General and Technical Program Chairs

  • Saurabh Prasad, University of Houston
  • Badrinath Roysam, University of Houston
  • Paul Gader, University of Florida
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5G systems are not only new protocols, but, more importantly, they embrace a new telecommunication infrastructure. As such, one of the challenges is to achieve a convergence between the terrestrial and the satellite segments. Simultaneously, in the past few years, many satellite operators have been upgrading and/or enlarging their constellations to deliver enhanced functionalities and higher frequency reuse, by means of V/HTS technology, and not only of geostationary orbits, but also non-geostationary ones. These new trends pose interesting challenges regarding new interference-limited scenarios and spectral efficient techniques. This symposium deals with innovative solutions on signal & information processing and optimization for the topics listed in this call for papers.

Organized by

General Chairs

  • Ana PĂ©rez-Neira (CTTC/UPC)
  • Giovanni Giambene (Univ. Siena)
  • Prashant Pillai (University of Wolverhampton, UK)
  • Raed Shubair (UAE Ministry of Education & MIT)
  • Elisabeth de Carvalho (Aalborg University)
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The transition to digital telescope back-ends has opened up advanced computational resources for researchers to explore new parameter spaces with potential for breakthrough discovery in astronomy and the Search for Extra Terrestrial Intelligence {SETI). The flexibility of those systems will additionally enable the community to address the major challenges currently faced in radio astronomy, such as the mitigation of Radio Frequency Interference, the accurate calibration of next generation instrumentation, or even novel methods for data mining and information extraction.  The proposed symposium will provide researchers a platform to overview the state of the art in radio astronomy instrumentation and algorithms, and to define and put forth the challenges currently faced and expected to arise in the near future.

Organized by

General Chairs

  • Gregory Hellbourg, University of California Berkeley
  • Ian Morrison, Swinburne University
  • Richard Prestage, Green Bank Observatory

Technical Program Chairs

  • Nicolo Antoniette, SETI Permanent Committee
  • Amit Mishra, University of Cape Town
  • Gelu Nita, New Jersey Institute of Technology
  • Subramaniam Sadasivan, Independent Consultant
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Recent studies have highlighted the lack of robustness in state-of-the-art machine learning models. For instance, carefully crafted adversarial perturbations to natural images can easily cause modern classifiers trained by deep convolutional neural networks to yield incorrect predictions, while these adversarial examples can be made visually similar to the natural images, resulting in critical safety and security concerns of services and applications supported by machine learning models. Signal processing and black-box optimization techniques, such as manifold analysis, data transformation and zerothorder optimization, are becoming the core components in the research of adversarial machine learning. They are widely used to generate powerful adversarial examples to deceive target machine learning models and evade detection, as well as to provide robust and effective machinery against adversarial examples. This symposium aims to bring together researchers and practitioners from both academia and industry to report novel advances and to publish high-quality papers, in order to foster the field of signal processing for adversarial machine learning.

Organized by

General Chairs

  • Pin-Yu Chen, IBM Research AI
  • Sijia Liu, IBM Research AI
  • Bo Li, University of Illinois

Technical Program Chairs

  • Jinfeng Yi, JD.com
  • Cho-Jui Hsieh, University of California Davis
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Vehicular communications is an area of significant importance in our increasingly connected and mobile world. With 5G-enabled vehicular communications, intelligent connected vehicles have been envisioned to significantly enhance transportation efficiency, reduce incidents, improve safety, and mitigate the impacts of traffic congestion. However, vehicular environments are inherently challenging, e.g., due to doubly-selective physical channels and ever-changing network connectivity and topologies. To address the challenges in 5G-based intelligent vehicular communications, signal processing is playing an increasingly substantial role in this area. Examples include topics such as signal processing techniques for vehicular communications, vehicle control and localization, and image/video processing for autonomous vehicles. In this symposium, we aspire to provide a venue for open discussions on various signal processing research and applications enabling and exploiting intelligent vehicular communications.

Organized by

General and Technical Program Chairs

  • Xiang Cheng, Peking University
  • Rongqing Zhang, Colorado State University
  • Mounir Ghogho, International University of Rabat
  • Ender Ayanoglu, University of California Irvine
  • Liuqing Yang, Colorado State University
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Wireless systems are increasingly supporting larger and more diverse applications. To meet this demand, cellular providers need to have access to more bandwidth, which is their primary capital expenditure. They could reduce such costs-and introduce potentially far reaching improvements to cellular access, affordability, and coverage-by making better use of available spectrum in the 30-300 GHz millimeter-wave band. The aim of the millimeter wave symposium of GlobaSIP is to provide a forum that brings together scientists and researchers to present their cutting-edge innovations in all aspects of the field. Papers on practical applications and R&D results from industry and academic/industrial collaborations are particularly encouraged.

Organized by

General and Technical Program Chairs

  • Hani Mehrpouyan, Boise State University
  • David Matolak, University of South Carolina
  • Ismail Guvenc, North Carolina State University
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With billions of people worldwide accustomed to daily, hourly or even constant use of wireless devices for a myriad of activities in their lives, wireless network security is among few areas of paramount importance in modern civilization. The Symposium on Signal Processing for Wireless Network Security is aimed to attract researchers from all backgrounds to come together to share their latest ideas and findings on this theme, and to promote rapid development of truly useful technologies for wireless network security. All topics of signal processing methods and theories for wireless network security issues, such as authenticity, confidentiality, integrity and availability, are included.

Organized by

General and Technical Program Chairs

  • Yingbo Hua, University of California, Riverside
  • Zygmunt Haas, Cornell University and University of Texas at Dallas
  • Mounir Ghogho, University of Leeds
  • Ananthram Swami, Army Research Lab
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Tensor methods have been attracting increasing documented interest over the past decade, finding a plethora of important signal processing, data analysis, and machine learning applications. Tensors have been extensively employed in several research areas such as computer vision, chemometrics, bioinformatics, communications, array processing, network analysis, data mining, and deep learning, among others. The wide success of tensor methods can be attributed to their inherent ability to better model, analyze, predict, recognize, and learn from multi-modal data. This symposium wishes to serve as a global forum for researchers to meet, exchange ideas, and present new theoretical and algorithmic findings related to tensor methods. Based on broad signal processing and machine learning foundations, this symposium aspires to foster interdisciplinary discussions and bring together researchers from both academia and the industry.

Organized by

General Chairs

  • Panos P. Markopoulos, Rochester Institute of Technology
  • Evangelos E. Papalexakis, University of California Riverside

Technical Program Chairs

  • Fauzia Ahmad, Temple University
  • Andre L. F. de Almeida, Federal University of Cesara, Brazil
  • Xiao Fu, Oregon State University
  • Dimitris A. Pados, Florida Atlantic University
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