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Group communication is a programming abstraction that allows a distributed group of processes to provide a reliable service in spite of the possibility of failures within the group. The goal of the project was to improve the state of the art of group commu ...
The signal processing community is increasingly interested in using information theoretic concepts to build signal processing algorithms for a variety of applications. A general theory on how to apply the mathematical concepts of information theory to the ...
We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be recon structed at a sink within specified distortion bounds wh ...
Group communication provides one-to-many communication primitives that simplify the development of highly available services. Despite advances in research and numerous prototypes, group communication stays confined to small niches. To facilitate the accept ...
The Karhunen-Loeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve {\em distributed} signal processing where it is not generally possible to apply ...
Linear transforms and expansions are fundamental mathematical tools of signal processing. In particular, the wavelet transform has played an important role in several signal processing tasks, compression being a prime example. A signal can be represented i ...
Video signals are sequences of natural images, where images are often modeled as piecewise-smooth signals. Hence, video can be seen as a 3D piecewise-smooth signal made of piecewise-smooth regions that move through time. Based on the piecewise-smooth model ...
We consider large sensor networks where the cost of collecting data from the network nodes to the data gathering sink is critical.~We propose several algorithms that use limited local communication and distributed signal processing to make communication mo ...
Over the last decade or so, wavelets have had a growing impact on signal processing theory and practice, both because of the unifying role and their successes in applications. Filter banks, which lie at the heart of wavelet-based algorithms, have become st ...
This correspondence deals with multiwavelets, which are a recent generalization of wavelets in the context of time-varying filter banks and with their applications to signal processing and especially com- pression. By their inherent structure, multiwavelet ...