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The problem of optimal likelihood based modulation classification (MC) for optimal detection in 2x2 multiuser MIMO (MU-MIMO) receivers is considered. The optimal Log-MAP classifier is computationally exhaustive, and its sub-optimal Max-Log-MAP version poses remarkable degradation in performance. Between these two extremes, we propose four computationally simplified methods for MC, by taking special subsets of Euclidean distance computations that constitute the decision metric.

Key-frame extraction has been a focused problem of human action recognition due to its effectiveness, efficiency and importance in action understanding. According to the characteristics of human motion perception, this paper proposes a new key-frame extraction framework based on motion change points. And in order to detect motion change points robustly, a hysteresis extrema seeking algorithm has been developed. Experimental results have demonstrated the good performance of the proposed methods.

In this paper, dual layer multiple-input multiple-output (2x2 MIMO) systems that use the 1024-QAM modulation are studied. We build on the layered orthogonal lattice detector (LORD), which achieves optimal maximum-likelihood (ML) performance, but which has high complexity with 1024-QAM, and argue that the low-complexity version of LORD (LC-LORD) introduces a significant performance degradation, especially with high channel correlation. We propose several approaches that outperform LC-LORD at a lower complexity.

Advancing over up-to-date information theoretic results that assume symmetric correlation models, in this work we consider the problem of lossy binary source coding with side information, where the correlation is expressed by a generic binary asymmetric channel. Specifically, we present an in-depth analysis of rate distortion with side information available to both the encoder and decoder (conventional predictive), as well as the Wyner-Ziv problem for this particular setup.