posted by organizer: gongo || 5992 views || tracked by 2 users: [display]

MLSPWiCSR 2019 : IJCAI Workshop on Machine Learning for Signal Processing in Wireless Communications, Sensing and Radar

FacebookTwitterLinkedInGoogle

Link: https://wmlsp.github.io
 
When Aug 11, 2019 - Aug 11, 2019
Where Macao, China
Abstract Registration Due Jun 15, 2019
Submission Deadline May 17, 2019
Notification Due Jun 3, 2019
Final Version Due Jun 12, 2019
Categories    machine learning   signal processing   communications   sensing
 

Call For Papers

Workshop on Machine Learning for Signal Processing in Wireless Communications, Sensing and Radar At IJCAI 2019, August 10-12, Macao, China

Artificial Intelligence (AI) and Machine Learning (ML) approaches, well known from Computer Science disciplines, are beginning to emerge in the RF Signal Processing, Communications and Networking domains. However, there are various challenges arising in the application of Machine Learning to RF signals, such as inherently high data rates, sensitivity to environmental effects (noise, multi-path, interference etc), presence of multi-scale features in both frequency and time domains, to name a few. Also, in contrast to the image and text processing domains, the scarcity of large public repositories of standardized RF signal data makes it harder for academic and industry researchers to test and validate their algorithms in a robust, reproducible, and scalable fashion. The goal of this workshop is to bring together researchers from the RF Signal Processing and Machine Learning communities, showcase state-of-the-art Machine Learning approaches applicable in the RF domain, and provide a forum for discussing cross-disciplinary ideas to address present and future challenges.

Topics of interest include, but are not limited to:
* Machine Learning for blind channel and signal characterization
* Machine Learning for source separation
* Machine Learning for RF signal classification
* Machine Learning for cognitive radio communications, for instance spectrum awareness, or optimization of spectrum usage dynamics and spectrum access control
* Quality of unsupervised learning with corrupted, censored and missing spectrum sensing samples
* Privacy-preserving Machine Learning for cognitive radio communications, for instance in 5G cellular networks
* Machine Learning for RF-based geo-location
* Distributed learning in collaborative autonomous networked multi-agent systems
* Adversarial Machine Learning techniques applied to RF systems
* Machine Learning techniques for RF systems security
* Reinforcement learning in wireless communication and sensor networks
* Transfer Learning for wireless communication and sensor networks
* Visual analysis of learned features in Deep Learning for RF signal processing
* Machine Learning techniques for communications and sensing convergence

Important Dates: April 12, 2019 (paper submission) | May 10, 2019 (acceptance notification)

Workshop website: http://wmlsp.github.io

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
CVPR 2022   Computer Vision and Pattern Recognition
CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
IJCAI 2021   30th International Joint Conference on Artificial Intelligence
MLDM 2022   18th International Conference on Machine Learning and Data Mining
JCRAI 2021-Ei Compendex & Scopus 2021   2021 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2021)
DL-ASAP 2022   Pattern Recognition Letters - Deep Learning for Acoustic Sensor Array Processing
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
NeCoM 2022   14th International Conference on Networks & Communications
IEEE MAPE--EI Compendex, Scopus 2022   2022 IEEE the 9th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (IEEE MAPE 2022)--EI Compendex, Scopus