IEEE Signal Processing Magazine

This paper describes an integrated neuroinformatics system, called NeuronIQ (neuronal image quantitator), for processing, archiving, and analyzing 3D neuronal images obtained from high resolution optical microscopy. While this paper focuses on single channel images, multichannel images can also be acquired for neurons, glial cells, blood vessels, and other neurological structures by using differen... View full abstract»

The digital nature of genomic information makes it suitable for the application of signal processing techniques to better analyze and understand the characteristics of DNA, proteins, and their interaction. Prediction of genes, protein structure, and protein function greatly utilize pattern recognition techniques, in which hidden Markov models, neural networks, and support vector machines play a ce... View full abstract»

This paper identifies two classes of sensor network problems that arise naturally in many applications. Fusion problem involves correlated data over the sensor network, while search/sensor selection involves selecting sensors that have useful information. The paper also describes an adaptive decision making system, which feeds back the previous decisions of the sensor network to make more informed... View full abstract»

This paper provides several tricks to reduce the complexity and enhance the usefulness of cascaded integrator-comb (CIC) filters. The first trick shows a way to reduce the number of adders and delay elements in a multi-stage CIC interpolation filter. The result is a multiplierless scheme that performs high-order linear interpolation using CIC filters. The second trick shows a way to eliminate the ... View full abstract»

To illustrate the conceptual issues related to sampling, source representation/coding and communication in sensor networks, we review the underlying theory and discuss specific examples. We show how the structure of the distributed sensing and communication problem dictates new processing architectures. The key challenge lies in the discretization of space, time and amplitude, since most of the ad... View full abstract»

This paper describes a variety of TeachWare, i.e., digital signal processing (DSP) education resources that are useful for introducing DSP concepts, operations, and algorithms. The paper provides such information as the type of resource, its status, and format. View full abstract»

This paper provides a signal processing perspective on large-scale sensor networks. It focuses on two characteristics of sensor networks: application specificity and energy constraint. The former requires that network protocols be designed for the application at hand, which is often signal processing in nature, and measured by application-specific metrics. The latter calls for novel distributed si... View full abstract»

This paper describes how Andrew J. Viterbi developed a non-sequential decoding algorithm which proved useful in showing the superiority of convolutional codes over block codes for a given degree of decoding complexity. The Viterbi algorithm is now used in most digital cellular phones and digital satellite receivers as well as in such diverse fields as magnetic recoding, voice recognition, and DNA ... View full abstract»

This paper reviews the classical decentralized decision theory in the light of new constraints and requirements. The central theme that transcends various aspects of signal processing design is that an integrated channel-aware approach needs to be taken for optimal detection performance given the available resources. View full abstract»

This paper provides an overview of distributed estimation-compression problems encountered with wireless sensor networks (WSN). A general formulation of distributed compression-estimation under rate constraints was introduced, pertinent signal processing algorithms were developed, and emerging tradeoffs were delineated from an information theoretic perspective. Specifically, we designed rate-const... View full abstract»

This paper presents an overview of research conducted to bridge the rich field of graphical models with the emerging field of data fusion for sensor networks. Both theoretical issues and prototyping applications are discussed in addition to suggesting new lines of reasoning. View full abstract»

This paper discusses nonparametric distributed learning. After reviewing the classical learning model and highlighting the success of machine learning in centralized settings, the challenges that wireless sensor networks (WSN) pose for distributed learning are discussed, and research aimed at addressing these challenges is surveyed. View full abstract»

This paper addresses the important aspect of compressing and transmitting video signals generated by wireless broadband networks while heeding the architectural demands imposed by these networks in terms of energy constraints as well as the channel uncertainty related to the wireless communication medium. Driven by the need to develop light, robust, energy-efficient, and low delay video delivery s... View full abstract»