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Recent Advancements in Signal Processing and Machine Learning

Call for Papers

Much of cutting edge technologies rely on the research on signal processing and machine learning, and a great number of algorithms and devices have been developed with respect to various problems, such as signal denoising, image registration and pattern recognition, texture analysis, surveillance, biometrics, human-machine interface, image/video compression/encryption, and image enhancement.

Some of the rich literature pointed out the relation between signal processing and machine learning. Many signal processing problems and machine learning problems are closely related. For example, wavelet transform proposed for signal processing has been used as a preprocessing for many machine learning applications, and random fields and probabilistic graphic models show good results in image segmentations and recognitions. It is the time to motivate these two interactive areas to work together and pay more attention to each area.

All submitted papers will be rigorously reviewed, and we will select papers based on their originality, timeliness, significance, and relevance to this special issue. Potential topics include, but are not limited to:

  • Audio, speech, and language processing (analysis, understanding, denoising, compression, encryption, classification, recognition, synthesis, etc.)
  • Image, video, and multimedia processing (analysis, segmentation, enhancement, compression, encryption, classification, registration, phasing, recognition, etc.)
  • Pattern recognition and machine learning (feature extraction, statistical learning, manifold learning, subspace learning, visual learning, etc.)
  • Information forensics and security (encryption, watermarking, data hiding, surveillance, etc.)
  • Signal processing and machine learning applications in biological and medical areas (registration, classification, correlation analysis, 3D reconstruction, etc.)
  • Mathematical methods and modeling for signal processing and machine learning (data modeling, statistical methods, probabilistic graphical models, sparse representation, and optimization)
  • Signal processing and machine learning system (circuit design, embedded system, and system optimization)

Before submission authors should carefully read over the journal’s Author Guidelines, which are located at http://www.hindawi.com/journals/mpe/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/mpe/spml/ according to the following timetable:

Manuscript DueFriday, 27 September 2013
First Round of ReviewsFriday, 20 December 2013
Publication DateFriday, 14 February 2014

Lead Guest Editor

  • Gelan Yang, College of Information Science and Engineering, Hunan City University, Hunan, China

Guest Editors

  • Su-Qun Cao, Electrical and Electronic Engineering Department, University of Melbourne, Parkville, VIC 3010, Australia
  • Yue (Rex) Wu, Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA