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- John S. Sadowsky, James A. Bucklew
- IEEE Trans. Information Theory
- 1990

- James A. Bucklew, Gary L. Wise
- IEEE Trans. Information Theory
- 1982

Asymptotic properties of the rth power distortion measure associated with quantized k-dimensional random variables are considered. Subject only to a moment condition, it is shown that the infimum over all N level quantizers of the quantity N râ€™k times the rth power average distortion converges to a finite constant as N + cc Manuscript received January 16,â€¦ (More)

- Daniel J. Sebald, James A. Bucklew
- IEEE Trans. Signal Processing
- 2000

The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristicâ€¦ (More)

- James A. Bucklew, William A. Sethares
- IEEE Transactions on Signal Processing
- 2007

One of the key issues in decentralized beamforming is the need to phase-align the carriers of all the sensors in a wireless sensor network. Recent work in this area has shown the viability of certain methods that incorporate single-bit feedback from a beacon. This paper analyzes the behavior of the method (showing conditions for convergence in distributionâ€¦ (More)

- James A. Bucklew
- IEEE Trans. Information Theory
- 1984

A necessary and sufficient condit ion is presented for the normalized asymptotic rth power distortion of a mismatched multidimensional quantizer to converge to a certtin opt imum constant known as Bennettâ€™s integral. The dimensionality of our results is al lowed to approach infinity in order to make some universal source coding compar isons to quantizationâ€¦ (More)

- Michael G. Rabbat, Robert D. Nowak, James A. Bucklew
- Proceedings. (ICASSP '05). IEEE Internationalâ€¦
- 2005

We present a new approach to localizing an isotropic energy source using measurements from distributed sensors based on kernel averaging techniques. The location estimate is easily and efficiently calculated in a decentralized fashion. Statistical properties are derived for a very general measurement model. Experiments suggest that the proposed estimator isâ€¦ (More)

- Raman Arora, William A. Sethares, James A. Bucklew
- IEEE Journal of Selected Topics in Signalâ€¦
- 2008

A novel approach is presented for the detection of periodicities in DNA sequences. A DNA sequence can be modelled as a nonstationary stochastic process that exhibits various statistical periodicities over different regions. The coding part of the DNA, for instance, exhibits statistical periodicity with period three. Such regions in DNA are modelled asâ€¦ (More)

- James A. Bucklew
- IEEE Trans. Information Theory
- 1981

- James A. Bucklew, Thomas G. Kurtz, William A. Sethares
- IEEE Trans. Information Theory
- 1993

A recursive equation which subsumes several common adaptive filtering algorithms, is analyzed for general stochastic inputs and disturbances by relating the motion of the parameter estimate errors to the behavior of an unforced deterministic ordinary differential equation (ODE). Local stability of the ODE implies long term stability of the algorithm whileâ€¦ (More)

- R. Sharma, William A. Sethares, James A. Bucklew
- IEEE Trans. Signal Processing
- 1996

This paper presents an analysis of stochastic gradient-based adaptive algorithms with general cost functions. The analysis holds under mild assumptions on the inputs and the cost function. The method of analysis is based on an asymptotic analysis of fixed stepsize adaptive algorithms and gives almost sure results regarding the behavior of the parameterâ€¦ (More)