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The transformation of traditional energy networks to smart grids can assist in revolutionizing the energy industry in terms of reliability, performance and manageability. However, increased connectivity of power grid assets for bidirectional communications presents severe security vulnerabilities. In this paper, we investigate Chi-square detector and cosine similarity matching approaches for attack detection in smart grids where Kalman filter estimation is used to measure any deviation from actual measurements.

Presentation slides for the 2015 IEEE GlobalSIP paper entitled "Image Transmission over Cognitive Radio Systems with Channel and Sensing Uncertainty" by Chuang Ye, Gozde Ozcan, M. Cenk Gursoy, and Senem Velipasalar.

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.

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.