Wireless Future in the New Decade


Multimodal Signal Compression, Communication and Analysis

Organizers and Chairs:

Milo¸«Ž Radosavljevi¸««, InterDigital, Rennes, France
Vladimir Stankovic, University of Strathclyde, Department of Electronic and Electrical Engineering, Glasgow, UK

Scope of the Papers

With proliferation of low-cost sensors available anytime and anywhere, e.g., through the deployment of wireless sensor networks and Internet of Things or through crowdsourcing data from mobile phone users, massive streams of multi-modal data are becoming available. Such data may include images or video content captured by cameras, sounds collected from microphones, environmental data from various environmental sensors, mobility data emerging from GPS sensors, accelerometers and inertial measurement units, illumination data, and many other sources of information, and it may originate from different urban or rural environments.

How to efficiently process multi-modal data represents an important research challenge. Designing intelligent systems that could provide efficient multimodal signal analysis, inference and decision making require novel tools in the domain of signal processing, machine learning and data analysis. In addition, to design and support such a multi-modal data analysis system, novel techniques for multi-modal signal compression and communication are needed.

With the deployment of 5G networks, edge and fog storage and computing architectures, network function virtualization and software defined networking, we are interested in the design of flexible and efficient infrastructures for large-scale multi-modal data analytics. Integration of multi-modal data analytics with novel and emerging network architectures, including novel vehicular networks based on drones or self-driving vehicles, will provide support for future applications in smart cities, smart grids, smart agriculture, e-health and other areas.

Prospective authors are cordially invited to submit their original manuscripts on topics related to multimodal signal processing and machine learning systems including but not limited to:

  • Multi-modal data acquisition and compression techniques
  • Communication architectures, protocols and infrastructures for future multi-modal data analytics
  • Machine learning and artificial intelligence methods for multi-modal data
  • Multi-modal signal processing algorithms
  • Sensor networks, Internet of Things and multi-modal data analytics
  • Multi-modal data analytics system architectures, real-world demonstrations and deployments
  • Multi-modal data analytics in smart cities, smart grids, smart agriculture, e-health and other applications

Submission Guidelines

This Special Session will feature both invited papers and papers from open call. Full papers and short papers/extended abstracts as defined below can be submitted.

Full Papers: Full paper submissions of original work (not previously published, or under review at another conference or journal) must not be longer than five pages and will be published in the conference proceedings.

Short Papers and Extended Abstracts: Submissions must not be longer than two pages. They should convince the reader that the author(s) would give an exciting presentation and stimulate lively discussion (will be published in the conference proceedings). Note that it is fully expected that extended abstract papers accepted for the session will eventually be extended as full papers suitable for formal academic publication and presentation at other conferences/publications.

Please use the IEEE template as described here. Accepted papers will be published in the conference proceedings and submitted to IEEE Xplore.

Submissions are now accepted through EDAS: [Start a new submission here]

Important Dates

Paper submission deadline:   July 15, 2021 August 8, 2021
Notification of acceptance:     August 30, 2021
Camera-ready papers due:    September 11, 2021


Contact Us

Milo¸«Ž Radosavljevi¸««, InterDigital, Rennes, France, Email: milos.radosavljevic@interdigital.com
Vladimir Stankovic, University of Strathclyde, Department of Electronic and Electrical Engineering, Glasgow, UK, Email: vladimir.stankovic@strath.ac.uk

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