Thomas A. Baran

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The Digital Signal Processing Group develops signal processing algorithms that span a wide variety of application areas including speech and image processing, sensor networks, communications, radar and sonar. Our primary focus is on algorithm development in general, with the applications serving as motivating contexts. Our approach to new algorithms(More)
In designing discrete-time filters, the length of the impulse response is often used as an indication of computational cost. In systems where the complexity is dominated by arithmetic operations, the number of nonzero coefficients in the impulse response may be a more appropriate metric to consider instead, and computational savings are realized by omitting(More)
Oversampled A-to-D converters commonly rely on sharp-cutoff, discrete-time filters that operate at fast input sampling rates. Filter design techniques for such filters typically use the length of the impulse response as an indicator of computational cost, assuming that each filter tap requires a multiplier.[5][6] This paper describes methods for designing(More)
This paper is concerned with the inversion of implementations for systems that may generally be nonlinear and time-varying. Specifically, techniques are presented for modifying an implementation of a forward system, represented as an interconnection of subsystems, in such a way that an implementation for the inverse system is obtained. We focus on a class(More)
A rapidly advancing field, image compression has seen many recent developments. A number of well-known compression algorithms dominate the world of lossy compression, but in the realm of lossless compression, fewer techniques have been explored. The most successful lossy compression methods are based on transform methodologies, commonly involving the KLT,(More)
ACNE plays an important role in many aspects of signal processing and time series analysis. The recursive solution to these equations presented in this article is developed independently of any specific application context and utilizes the basic principles of LTI systems, resulting in the lattice filter structure as a by-product.
This paper presents a framework for designing a class of distributed, asynchronous optimization algorithms, realized as signal processing architectures utilizing various conservation principles. The architectures are specifically based on stationarity conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems.(More)
This paper provides examples of various synchronous and asynchronous signal processing systems for performing optimization, utilizing the framework and elements developed in a preceding paper. The general strategy in that paper was to perform a linear transformation of stationarity conditions applicable to a class of convex and nonconvex optimization(More)
This primary purpose of this paper is to succinctly state a number of verifiable and tractable sufficient conditions under which a particular class of conservative signal processing structures may be readily used to solve a companion class of constraint satisfaction problems using both synchronous and asynchronous implementation protocols. In particular,(More)
Conservation principles have played a key role in the development and analysis of many existing engineering systems and algorithms. In electrical network theory for example, many of the useful theorems regarding the stability, robustness, and variational properties of circuits can be derived in terms of Tellegen’s theorem, which states that a wide range of(More)