Model based spectral estimation techniques consist of essentially two steps. The first step is the estimation of a parameter set from the data and the second step consists of extracting the relevant information from the parameter set. A numerical evaluation of the overall procedure can be performed by conducting a perturbation analysis of the two steps separately. We demonstrate this by studying the linear prediction approach for estimating the frequencies of sinusoids in white noise. It is shown that in the first step the continuity of the generalized inverse and the concept of angle between subspaces play an important role. The continuity concept helps explain the need for a low rank approximation and the quality of the approximant is appraised by using the notion of angle between subspaces. For the second step the sensitivity of the zeros of the predictor polynomial becomes an important consideration and is examined.

In this paper, we examine the sensitivity of state space digital filters to coefficient quantization errors. A cost function using a Lpnorm criteria is used to measure the deviation in frequency response of the filter. The cost function is used to indicate the connection between roundoff noise and filter sensitivity to coefficient quantization errors. Then based on this cost function it is shown that low roundoff noise state space digital filters and filters in balanced coordinates have low sensitivity to coefficient quantization errors. Furthermore, the sensitivity of the filters is shown to depend on the second order modes thereby exhibiting certain robustness and immunity to filter bandwidth.

In this paper, a State Space approach to the 2-D harmonic retrieval problem is presented. Under certain assumptions, it is shown that the data and covariance matrices have finite rank and also possess a desirable algebraic structure. Then methods employing the Principal components algorithm are developed to estimate the state space parameters and the sinusoid parameters directly from the data and from the covariance information. Simulation results to support the methods are also provided.

This paper enhances some theoretical and implementation aspects of a constrained autoregressive moving average model, the notch filter model developed in [1] for the estimation of sinusoidal signals in additive, uncorrelated noise, colored or white. This model is shown to approximate the actual signal plus noise model. In addition, the parameter estimates obtained by minimization of the output power of the notch filter approximate the maximum likelihood estimate of the model parameters. The relationship of the notch filtering approach to the existing autoregressive and Pisarenke methods is established. Next, a scheme to combine fast convergence and unbiased estimation is suggested. Lastly, certain implementation aspects of the filter are considered and the method is shown to be amenable to parallel processing.

This paper describes the development of a wavefront-based language and architecture for a programmable special-purpose multiprocessor array. Based on the notion of computational wavefront, the hardware of the processor array is designed to provide a computing medium that preserves the key properties of the wavefront. In conjunction, a wavefront language (MDFL) is introduced that drastically reduces the complexity of the description of parallel algorithms and simulates the wavefront propagation across the computing network. Together, the hardware and the language lead to a programmable wavefront array processor (WAP). The WAP blends the advantages of the dedicated systolic array and the general-purpose data-flow machine, and provides a powerful tool for the high-speed execution of a large class of matrix operations and related algorithms which have widespread applications.

An important impact of the fast growing VLSI device technology will be the massive capability of parallel processing which will in turn greatly affect the trend of modern signal processing technology. For the new trend it will be a necessity to have revolutionary architectural design concepts such as topological mapping between algorithm and architecture, simple and regular data flow etc [1]. In this paper, based on a natural topology of the computing structure, a novel "computational wave-front" notion is introduced for describing and and validating data flow in locally connected networks. The parallel architectures include the linear system, with and without pivoting, and the least-square solver using Givens method. We believe that this set of linear system architectures will play a central role in modern signal processing.