Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on twenty research projects and a list of publications.Lockheed Sanders, Inc. Contract BZ4962U.S. Army Research Laboratory Grant QK-8819U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-95-1-0834U.S. Navy - Office of Naval Research Grant N00014-96-1-0930U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072National Science Foundation Graduate Research Fellowship Grant MIP 95-02885Lockheed Sanders, Inc. Grant N00014-93-1-0686National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/ARL Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on sixteen research projects and a list of publications.Bose CorporationMIT-Woods Hole Oceanographic Institution Joint Graduate Program in Oceanographic EngineeringAdvanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-93-1-0686Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral Support ProgramAdvanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation FellowshipMaryland Procurement Office Contract MDA904-93-C-4180U.S. Navy - Office of Naval Research Grant N00014-91-J-162

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on twenty-two research projects and a list of publications.Sanders, a Lockheed-Martin Corporation Contract BZ4962U.S. Army Research Laboratory Contract DAAL01-96-2-0001U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-96-1-0930National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Science Foundation Graduate Research FellowshipAT&T Bell Laboratories Graduate Research FellowshipU.S. Army Research Laboratory Contract DAAL01-96-2-0002National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on twenty-one research projects and a list of publications.U.S. Navy - Office of Naval Research Grant N00014-93-1-0686Lockheed Sanders, Inc. Contract P.O. BY5561U.S. Air Force - Office of Scientific Research Grant AFOSR 91-0034National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-95-1-0834MIT-WHOI Joint Graduate Program in Oceanographic EngineeringAT&T Laboratories Doctoral Support ProgramDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489Lockheed Sanders/U.S. Navy - Office of Naval Research Grant N00014-91-C-0125U.S. Navy - Office of Naval Research Grant N00014-89-J-1489National Science Foundation Grant MIP 95-02885Defense Advanced Research Projects Agency/U.S. Navy Contract DAAH04-95-1-0473U.S. Navy - Office of Naval Research Grant N00014-91-J-1628University of California/Scripps Institute of Oceanography Contract 1003-73-5

Software Environment For The Development Of Embedded Signal Processing Systems

A new environment for the rapid development of embedded signal processing software is described. The environment encourages incremental design via modular and hierarchical structuring of applications and additional features are included which support the prototyping, testing, implementation, and integration stages of the system design cycle. Written in C++ , the environment is comprised of a scripting language for the definition of system components and a class library which includes a basic application framework. Support is provided for incorporating both numeric and symbolic signal representations, as well as integrating multiple signal processing techniques within a single application. A sophisticated control mechanism allows dynamic scheduling of signal processing operations according to algorithmically defined schema. Signal processing applications developed in this environment are themselves objects and are suitable for embedding within a larger overall system. 1. INTRODUCTION T..

Probabilistic Complexity Analysis for a Class of Approximate DFT Algorithms

We present a probabilistic complexity analysis of a class of multi-stage algorithms which incrementally refine DFT approximations. Each stage of any algorithm in this class improves the results of the previous stage by a fixed increment in one of three dimensions: SNR, frequency resolution, or frequency coverage. However, the complexity of each stage is probabilistically dependent upon certain characteristics of the input signal. Assuming that an algorithm has to be terminated before its arithmetic cost exceeds a given limit, we have formulated a method for predicting the probability of completion of each of the algorithm's stages. This analysis is useful for low-power and real-time applications where FFT algorithms cannot meet the specified limits on arithmetic cost. I. Introduction While the palette of transforms available to the DSP system designer continues to broaden, the utility of the DFT across a broad range of applications remains unparalleled. This fact can be attributed in p..

Incremental Refinement Of Dft And Stft Approximations

We present a class of multi-stage algorithms for carrying out incremental refinement of DFT and STFT approximations. Each stage is designed to improve the previous stage's approximation in terms of frequency coverage, frequency resolution, and SNR. These algorithms rely almost exclusively on vector summation operations and they can be designed to exhibit a variety of tradeoffs between improvement in approximation quality and computational cost per stage. The performance of incremental STFT refinement on real data serves to illustrate the relevance of such algorithms to application systems with dynamic real-time constraints. I. Introduction Algorithms which can quickly produce approximate results and then refine those results in an incremental manner are useful for achieving graceful degradation of performance as available resources diminish in systems with dynamic real-time constraints [1, 2]. Results have previously been reported [3]-[5] on the design and evaluation of algorithms for..

Probabilistic Complexity Analysis Of Incremental DFT Algorithms

We present a probabilistic complexity analysis of a class of multi-stage algorithms for computing successive approximations to the DFT. While the quality of the approximate spectra obtained after any stage of these algorithms can be readily quantified in terms of commonly used inputindependent metrics of spectral quality, each stage's arithmetic complexity is dependent on the nature of the input signal. Modeling the input signal as a stationary Gaussiandistributed random process, we obtain estimates of the distribution of the number of arithmetic operations required to complete any algorithm stage. This enables the derivation of important design information such as the probability with which a desired quality of approximation is achieved within a given arithmetic bound. Our results are verified using a Monte Carlo analysis. 1. INTRODUCTION We have recently introduced a class of algorithms for computing approximations to the DFT which we refer to as DFT incremental-refinement (DFT-IR)..

Approximate Signal Processing Using Incremental Refinement And Deadline-Based Algorithms

A framework for approximate signal processing is introduced which can be used to design novel classes of algorithms for performing DFT and STFT calculations. In particular, we focus on the derivation of multi-stage incremental refinement algorithms that meet a variety of design criteria on the tradeoff achieved at each stage between solution quality and computational cost. 1. INTRODUCTION In any given problem-solving domain, an approximation to a given algorithm may be defined as an algorithm which offers a reduced computational cost but produces a lower quality answer according to some standard of accuracy, certainty, and/or completeness. The approximate algorithm may be said to carry out approximate processing in the domain under consideration. Such algorithms have previously been studied in the context of various applications, including real-time vehicular tracking [1, 2] and real-time database query processing [3]. In real-time applications, any individual task must generally be ..

IPUS: An Architecture for the Integrated Processing and Understanding of Signals

The IntegratedProcessing and Understanding of Signals #IPUS# architecture is presented as a framework that exploits formal signal processing models to structure the bidirectional interaction between front-end signal processing and signal understanding processes. This architecture is appropriate for complex environments, which are characterized byvariable signal to noise ratios, unpredictable source behaviors, and the simultaneous occurrence of objects whose signal signatures can distort each other. A key aspect of this architecture is that front-end signal processing is dynamically modi#able in response to scenario changes and to the need to re-analyze ambiguous or distorted data. The architecture tightly integrates the search for the appropriate front-end signal processing con#guration with the search for plausible interpretations. In our opinion, this dual search, informed by formal signal processing theory, is a necessary component of perceptual systems that must interact with complex environments. To explain this architecture in detail, we discuss examples of its use in an implemented system for acoustic signal interpretation