Sorry, you need to enable JavaScript to visit this website.

In wireless communication systems, spoofing attack significantly impact the information security, for this attack is not hard to launch with little effort. Although traditional cryptographic authentication can be utilized for node identification, it is not desirable in some low power requirements application scenarios such as Wireless Sensor Networks (WSNs). In this paper, we formulate this problem as one of sparse signal processing. In order to identify the existence of the spoofing attacker, we explore using the feature extraction and fusion

Time-reversal (TR) wideband communication could harvest the energy from the rich-scattering environment and enjoy the unique spatial and temporal focusing effect. However, the performance degrades in the presence of carrier frequency offset (CFO), which introduces an additional phase linear in time. CFO estimators could mitigate its effect. Yet, conventional CFO estimators cannot work well regarding the tiny CFO due to the high sampling rate in wideband TR systems. To address this issue, we propose four CFO estimators which are capable to estimate very small CFO with very high accuracy.

Distributed filter in networks mainly involves two stages, local estimation by private observation and information fusion with neighbor nodes based on the underlying topology. Since Bayesian game is a powerful tool to analyze the interaction equilibrium of multi-player with incomplete information in networks, we combine the recursive LMMSE filter with network game of quadratic utilities under the Bayesian filtering framework. In our algorithm, the nodes update their local beliefs on the unknown state by private observations and historical actions from neighbors in network.

In this work, we propose an improved fast multiple-view image denoising algorithm using 3D focus image stacks. It showed improved computational efficiency and comparable denoising quality compared to conventional methods.

These slides are about the mean-squared error (MSE) characterization of the direction of arrival (DOA) estimation. Also, we study the effect of MSE of DOA estimation on the achievable rate of the Massive MIMO system

With ongoing massive smart energy metering
deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM),
has generated increased interest. However, despite the fact that
NALM was proposed over 30 years ago, there are still many
open challenges. Indeed, the majority of approaches require
training and are sensitive to appliance changes requiring
regular re-training. In this paper, we tackle this challenge by