By Bingo Wing-Kuen Ling, Charlotte Yuk-Fan Ho (auth.), Amitava Chatterjee, Hadi Nobahari, Patrick Siarry (eds.)
There were major advancements within the layout and alertness of algorithms for either one-dimensional sign processing and multidimensional sign processing, particularly photograph and video processing, with the hot concentration altering from a step by step approach of designing the set of rules first and following up with in-depth research and function development to as a substitute using heuristic-based the way to clear up signal-processing difficulties.
In this booklet the contributing authors display either general-purpose algorithms and people geared toward fixing really good software difficulties, with a unique emphasis on heuristic iterative optimization equipment utilizing sleek evolutionary and swarm intelligence established concepts. The purposes thought of are in domain names comparable to communications engineering, estimation and monitoring, electronic clear out layout, instant sensor networks, bioelectric sign type, snapshot denoising, and snapshot function monitoring.
The booklet offers attention-grabbing, cutting-edge methodologies for fixing real-world difficulties and it's a appropriate reference for researchers and engineers within the components of heuristics and sign processing.
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Extra resources for Advances in Heuristic Signal Processing and Applications
Therefore, considering all the possible signal paths and target velocities, which represent themselves as varying Doppler shifts at the radar receiver, we convert the OFDM measurement model to an equivalent sparse model. The nonzero components of the sparse vector in this model correspond to the scattering coefficients of the target at the true signal paths and target velocity. The formulation of a sparse-measurement model transforms a target-detection problem into a task of estimating the nonzero coefficients of a sparse signal.
49(3), 457–469 (2001) 23. : Adaptive reduced-rank interference suppression based on the multistage Wiener filter. IEEE Trans. Commun. 50(6) (2002) 24. : On low rank MVDR beamforming using the conjugate gradient algorithm. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (2004) 25. : Data record-based criteria for the selection of an auxiliary vector estimator of the MMSE/MVDR filter. IEEE Trans. Commun. 51(10), 1700–1708 (2003) 26. : Reduced-rank adaptive filtering based on joint iterative optimization of adaptive filters.
A hybrid method for the design of oversampled uniform DFT filter banks. Signal Process. (2006) 33. : New filled functions for nonsmooth global optimization. Appl. Math. Model. (2009) 34. : A new norm-relaxed SQP algorithm with global convergence. Appl. Math. Lett. (2010) Chapter 2 Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters Rodrigo C. de Lamare Abstract This chapter presents robust reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on the concept of joint iterative optimization of parameters.
Advances in Heuristic Signal Processing and Applications by Bingo Wing-Kuen Ling, Charlotte Yuk-Fan Ho (auth.), Amitava Chatterjee, Hadi Nobahari, Patrick Siarry (eds.)