Our main focuses are on Transportation, Energy, and Health.
We studied the problem of data acquisition in wireless sensor networks (WSNs). A recently revitalized technique called compressive sensing (CS) has presented a new method to capture sparse signals at a rate below Nyquist. There are drawbacks to directly applying the existing CS algorithm to WSNs, which are mainly due to the fact that CS requires a large number of inter-communications for generating each projection.
In wireless networks, multicast is an elementary service that is used in many applications. We proposed a new algorithm for constructing a multicast subgraph and a distributed source coding-based Multicast (DSCM) protocol for multihop wireless networks with emphasis on reliability, rate optimality, and energy efficiency.
Distributed Source Coding using Finite-Length Rate-Compatible LDPC Codes: The Entire Slepian-Wolf Rate Region
We proposed a technique to reduce the transmission power usage in the wireless sensor networks (WSNs) exploting the spatial correlations between sensor readings in a network. Each node compresses its data without communicating with other nodes and sends the compressed data to the base station. Such a system requires distributed source coding (DSC), since the encoders are distributed and the signals are compressed independently.
There are many data transmission and storage systems with two-dimensional (2-D) data structures that suffer from 2-D bursts of error and erasures. 2-D codes can be used to combat such errors and erasures. We introduced two-dimensional wavelet codes (TDWCs).