Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Editorial Advances in Subspace-Based Techniques for Signal Processing and Communications | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 48612 3 pages doi 2007 48612 Editorial Advances in Subspace-Based Techniques for Signal Processing and Communications Kostas Berberidis 1 Benoit Champagne 2 George V. Moustakides 3 H. Vincent Poor 4 and Peter Stoica5 1 Department of Computer Engineering and Informatics University of Patras 26500 Patras Greece 2 Department of Electrical and Computer Engineering McGill University 845 Sherbrooke Street W Montreal QC Canada H3A2T5 3 Department of Computer and Communication Engineering University of Thessaly 38221 Volos Greece 4 Department of Electrical Engineering Princeton University Olden Street Princeton NJ 08544 USA 5 Department of Information Technology Uppsala University 751 05 Uppsala Sweden Received 21 June 2006 Accepted 21 June 2006 Copyright 2007 Kostas Berberidis et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Research in subspace-based techniques for signal processing was initiated more than three decades ago and there has been considerable progress in the area. Thorough studies have shown that the estimation and detection tasks in many signal processing and communications applications can be significantly improved by using the subspace-based methodology. Over the past few years new potential applications have emerged and subspace methods have been adopted in several diverse fields such as smart antennas sensor arrays multiuser detection system identification time delay estimation blind channel estimation image segmentation speech enhancement learning systems magnetic resonance spectroscopy and radar systems. Subspace-based methods not only provide new insight into many such problems but they also offer a good tradeoff between achieved performance and computational complexity. In .
