Research in the communications and signal processing area focuses on issues regarding the efficient processing and transmission of data. Some examples of sources of data include sound, images, and sensor output signals. Much of modern statistical and adaptive signal processing relies on learning algorithms of one form or another. More commonly, this method is more significant in communication and signal processing. However, large amount of audio, video and text data have been generated by modern connected devices. Hence, there is a need for efficient ML algorithms in terms of accuracy and speed becomes progressively important for signal processing. At a practical level, machine learning and signal processing are frequently combined. The most common relationship is that signal processing is used as a pre-processing step before the application of machine learning. This significant combination will solve digital signal processing and communication problems: from computational efficiency, online adaptation, and learning with limited supervision, to their ability to combine heterogeneous information, to incorporate prior knowledge about the problem, or to interact with the user to achieve improved performance. Many machine learning techniques have already been applied to address the relevant problems. For example, convolutional neural networks have demonstrated superior performance on large-scale image classification. Semi- and weakly-supervised learning methods have significantly improved the performance when only small amount of annotated data is available. Correlation analysis, transfer learning, and multi-task learning have shown the potential in integrating severely heterogeneous data. Sparse representation and clustering approaches have been exploited in denoising and selecting of exemplary samples from the raw data. This special issue aims to demonstrate the contribution of machine learning techniques to the research and development of advance signal processing and communication. The special issue seeks high-quality, original technical papers from academia, government, and industry. Topics of interest include, but are not limited to:
Papers must describe original research that advances state-of-the-art in the area of cybersecurity and must not be simultaneously submitted to a journal or a conference with proceedings. Papers must be written in excellent English and should not exceed 10 pages. Previously published or accepted conference papers must contain at least 40% new material to be considered for the special issue. A covering letter to the Guest editors clearly describing the extensions made must accompany these types of submissions. All submissions must be made using the instructions available at:
The authors can directly submit their papers at: https://www.editorialmanager.com/ante/ and must select the menu “Choose Article Type” and then the item “CfP: Machine Learning Algorithms for Signal Processing and Communication”.
(c) GdR 720 ISIS - CNRS - 2011-2018.