Paper

A novel supervised energy disaggregation method uses dictionary-based modelling of appliance states to accurately identify and quantify energy consumption patterns in real-world settings.

This research develops an adaptive completion algorithm for wireless sensor networks, preserving privacy while accurately estimating the full-rank correlation matrix.

This paper introduces a novel matrix completion-based approach to distributed beamforming, enabling efficient maximization of receiver SNR while minimizing transmit power and interference.

This paper presents a low-complexity OSIC equalization algorithm for OFDM-based vehicular communications, significantly reducing computational complexity while maintaining robust ICI mitigation performance.

This research develops a novel regularized receiver windowing technique that adapts to non-stationary V2V channel conditions, significantly improving ICI cancellation performance in vehicular environments.

Researchers develop novel transmitter techniques to mitigate interference and fading effects in wireless communication systems, enhancing reliability and efficiency.

This paper presents a decentralized approach to generalized power system state estimation, enabling efficient and scalable joint estimation of network topology and power flow states in large-scale grids.

This paper proposes a regularized MMSE ICI equalization scheme for OFDM systems over doubly selective channels, achieving linear complexity and improved performance compared to banded approximations.

This novel scheme leverages sparse representations to denoise X-ray medical images by decomposing noisy images into dictionaries, offering superior denoising results over existing methods.

A distributed, adaptive beamforming approach is developed to maximize fairness in multi-cell relay networks with multiple receive antennas, achieving significant throughput gains.