Browsing by Author "Wagner, Raymond"

Now showing 1 - 8 of 8
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    An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks
    (2006-04-01) Wagner, Raymond; Baraniuk, Richard G.; Du, Shu; Johnson, David B.; Cohen, Albert
    Distributed wavelet processing within sensor networks holds promise for reducing communication energy and wireless bandwidth usage at sensor nodes. Local collaboration among nodes de-correlates measurements, yielding a sparser data set with significant values at far fewer nodes. Sparsity can then be leveraged for subsequent processing such as measurement compression, de-noising, and query routing. A number of factors complicate realizing such a transform in real-world deployments, including irregular spatial placement of nodes and a potentially prohibitive energy cost associated with calculating the transform in-network. In this paper, we address these concerns head-on; our contributions are fourfold. First, we propose a simple interpolatory wavelet transform for irregular sampling grids. Second, using ns-2 simulations of network traffic generated by the transform, we establish for a variety of network configurations break-even points in network size beyond which multiscale data processing provides energy savings. Distributed lossy compression of network measurements provides a representative application for this study. Third, we develop a new protocol for extracting approximations given only a vague notion of source statistics and analyze its energy savings over a more intuitive but naive approach. Finally, we extend the 2-dimensional (2-D) spatial irregular grid transform to a 3-D spatio-temporal transform, demonstrating the substantial gain of distributed 3-D compression over repeated 2-D compression.
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    Distributed Image Compression for Sensor Networks using Correspondence Analysis and Super-Resolution
    (2003-09-01) Wagner, Raymond; Nowak, Robert David; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We outline a distributed coding technique for images captured from sensors with overlapping fields of view in a sensor network. First, images from correlated views are roughly registered (relative to a sensor of primary interest) via a low-bandwidth data-sharing method involving image feature points and feature point correspondence. An area of overlap is then identified, and each sensor transmits a low-resolution version of the common image block to the receiver, amortizing the coding cost for that block among the set of sensors. Super-resolution techniques are finally employed at the receiver to reconstruct a high-resolution version of the common block. We discuss the registration and super-resolution techniques used and present examples of each step in the proposed coding process. A numerical analysis illustrating the potential coding benefit follows, and we conclude with a brief discussion of the key issues remaining to be resolved on the path to coder robustness.
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    Distributed Image Compression in Camera Networks
    (2004-05-01) Wagner, Raymond; Digital Signal Processing (http://dsp.rice.edu/)
    Dense networks of wireless, battery-powered sensors are now feasible thanks to recent hardware advances, but key issues such as power consumption plague widespread deployment. Fortunately, in a dense network of sensors, cross-sensor correlation can be exploited to reduce the communication power consumption. In this thesis, we examine a novel technique for distributed image compression in sensor networks. First, sensors are allowed to share low-bandwidth descriptors of their fields of view as image feature points, allowing sensors to identify a common region of overlap. The region is then compressed via spatial downsampling, and image super-resolution techniques are employed at the receiver to reconstruct an original-resolution estimate of the common area from the set of low-resolution sensor images. We demonstrate the feasibility of such an algorithm via a prototype implementation, and we evaluate the effectiveness of the proposed technique using a set of real sensor images gathered with an off-the-shelf digital camera.
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    Distributed Multiscale Data Analysis and Processing for Sensor Networks
    (2005-02-01) Wagner, Raymond; Sarvotham, Shriram; Choi, Hyeokho; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    While multiresolution data analysis, processing, and compression hold considerable promise for sensor network applications, progress has been confounded by two factors. First, typical sensor data are irregularly spaced, which is incompatible with standard wavelet techniques. Second, the communication overhead of multiscale algorithms can become prohibitive. In this paper, we take a first step in addressing both shortcomings by introducing two new distributed multiresolution transforms. Our irregularly sampled Haar wavelet pyramid and telescoping Haar orthonormal wavelet basis provide efficient piecewise-constant approximations of sensor data. We illustrate with examples from distributed data compression and in-network wavelet de-noising.
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    Distributed Wavelet Transform for Irregular Sensor Network Grids
    (2005-07-01) Wagner, Raymond; Choi, Hyeokho; Baraniuk, Richard G.; Delouille, Veronique; Digital Signal Processing (http://dsp.rice.edu/)
    Wavelet-based distributed data processing holds much promise for sensor networks; however, irregular sensor node placement precludes the direct application of standard wavelet techniques. In this paper, we develop a new distributed wavelet transform based on lifting that takes into account irregular sampling and provides a piecewise-planar multiresolution representation of the sensed data. We develop the transform theory; outline how to implement it in a multi-hop, wireless sensor network; and illustrate with several simulations. The new transform performs on par with conventional wavelet methods in a head-to-head comparison on a regular grid of sensor nodes.
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    Image Super-Resolution for Improved Automatic Target Recognition
    (2004-04-01) Wagner, Raymond; Waagen, Donald; Cassabaum, Mary; Digital Signal Processing (http://dsp.rice.edu/)
    Infrared imagers used to acquire data for automatic target recognition are inherently limited by the physical properties of their components. Fortunately, image super-resolution techniques can be applied to overcome the limits of these imaging systems. This increase in resolution can have potentially dramatic consequences for improved automatic target recognition (ATR) on the resultant higher-resolution images. We will discuss super-resolution techniques in general and specifically review the details of one such algorithm from the literature suited to real-time application on forward-looking infrared (FLIR) images. Following this tutorial, a numerical analysis of the algorithm applied to synthetic IR data will be presented, and we will conclude by discussing the implications of the analysis for improved ATR accuracy.
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    A Multiscale Data Representation for Distributed Sensor Networks
    (2005-03-01) Wagner, Raymond; Sarvotham, Shriram; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    Though several wavelet-based compression solutions for wireless sensor network measurements have been proposed, no such technique has yet appreciated the need to couple a wavelet transform tolerant of irregularly sampled data with the data transport protocol governing communications in the network. As power is at a premium in sensor nodes, such a technique is necessary to reduce costly communication overhead. To this end, we present an irregular wavelet transform capable of adapting to an arbitrary, multiscale network routing hierarchy. Inspired by the Haar wavelet in the regular setting, our wavelet basis forms a tight frame adapted to the structure of the network. We demonstrate results highlighting the approximation capabilities of such a transform and the clear reduction in communication cost when transmitting a compressed snapshot of the network to an outside user.
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    A Multiscale Data Representation for Distributed Sensor Networks: Proofs of Basis Characteristics and Error Bounds
    (2004-09-01) Sarvotham, Shriram; Wagner, Raymond; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    Provides proofs of Parseval tight-frame membership and approximation properties for the basis proposed in "A Multiscale Data Representation for Distributed Sensor Networks" by R. Wagner, S. Sarvotham, and R. Baraniuk (ICASSP 2005).