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A new wavelet algorithm for on-line improvement of an existing polynomial model of nonlinearity in a Hammerstein system is proposed and its properties are examined. The algorithm employs wavelet bases on interval. Convergence of the resulting assembly, comprising the parametric polynomial model and a nonparametric wavelet add-on, to the system nonlinearity is shown. Rates of convergence for uniformly smooth and piecewise smooth nonlinearities with discontinuities are both established. Index Terms--Hammerstein system, nonlinear system identification, order statistics, polynomial models, semiparametric approach, wavelet bypass, wavelet regression estimate.

In this letter, we investigate the error resilience properties of JPEG2000. We identify the dependencies among coding passes of a codeblock codestream, and determine the sections of the codestream that can be salvaged in the presence of errors. In our analysis, we consider the effects of mode variations provided by the standard. The proposed methods are derived using the existing dependency structure of coding passes and do not require a substantial increase in computational complexity of the decoder. Experimental results indicate that the proposed methods can improve the error resilience performance substantially. Index Terms--Decoding dependency, error resilience, JPEG2000.

The fast rate-optimal rate allocation algorithm used selects a subchannel and channel code rate based on signal-to-noise ratios of the subchannels and gives unequal error protection.

A joint source-channel decoding scheme is proposed that is based on JPEG2000 codestreams and LDPC channel codes. The results have shown that this method needs very less iterations and has improved overall system performance.

An important issue in wireless sensor networks is the limited energy and bandwidth resources. A novel theory is developed here for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto-optimal (PO) solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not PO, a second iteration is performed which maximizes the second minimum lifetime, subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n - 1)th minimum lifetime being maximum, subject to the (n - 2)th minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. The presented solution assumes static network conditions. Index Terms--Communication system routing, routing, sensor networks.

We consider a network of imaging sensors. We address the problem of energy-efficient communication of the measurements of the sensors. A novel algorithm is presented for the purpose of exploiting intersensor and intrasensor correlation, which is inherent in a network of imaging sensors. The collaborative algorithm is used in conjunction with a cooperative multihop routing strategy to maximize the lifetime of the network. The algorithm is demonstrated to achieve an average gain in the lifetime as high as 3.2 over previous methods. OCIS codes: 100.0100, 200.3050.

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint. Index Terms--Code-block, discrete wavelet transform (DWT), embedded coding, JPEG2000, JPIP, precinct, scalable compression.

An algorithm is presented that efficiently exploits inter- and intrasensor correlation for the purpose of power-constrained distributed transmission of sensor-network imagery. Results demonstrate that gains in network lifetime up to 114e obtained when using the suggested algorithm with lossless compression, and much larger gains are achieved when lossy compression is employed.

A joint source/channel coding algorithm is proposed for the transmission of multiple image sources over memoryless channels. The proposed algorithm uses a quality scalable image coder to optimally allocate a limited bit budget among all the sources to achieve the optimal overall distortion reduction for the multiple reconstructed images. In addition to the conventional un gain, it provides channel multiplexing gain, which can be much more significant. Furthermore, an extended scheme is proposed to provide flexibility between the optimization performance and complexity. Index Terms--Joint source/channel coding, scalable source coding, un (UEP).

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression. OCIS codes: 100.6890, 100.200, 110.6880, 200.3050, 200.4560.