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The time-recursive computation model has been proven as a particularly useful tool in audio, video, radar and sonar real- time data processing architectures. Unlike the FFT based architectures, the time-recursive ones ...

In this dissertation the basic techniques for designing more sophisticated adaptive array systems are first developed. Then several systolic architectures based on numerically stable and computationally efficient algorithms ...

The problems of unified efficient computations of the discrete cosine transform (DCT), discrete sine transform (DST), discrete Hartley transform (DHT), and their inverse transforms are considered. In particular, a new ...

In this paper, we propose two multi-phase systolic algorithms to solve the spectral decomposition problem based on the QR algorithm. The spectral decomposition is one of the most computationally intensive modern signal ...

High-resolution spectral estimation is an important subject in many applications of modern signal processing. The fundamental problem in applying various high-resolution spectral estimation algorithms is the computational ...

Despite the well established benefits of using finite element methods, commercially available finite element packages have not received wide-spread application to the analysis of highway bridges. This is because they do ...

In this dissertation, we address the problem of developing efficient VLSI algorithms and architectures for discrete sinusoidal transforms in real-time applications for video communication systems. The major difficulty of ...

In this paper, a new computationally efficient algorithm for recursive least-squares (RLS) filtering is presented. The proposed Split RLS algorithm can perform the approximated RLS with O(N) complexity for signals having ...

In this paper we describe a variant of A* search designed to run on the massively parallel, SIMD Connection Machine. The algorithm is designed to run in a limited memory by use of a retraction technique which allows nodes ...

We propose a new class of hyperbolic Gram-Schmidt methods to simultaneously update and downdate the Cholesky factor of a sample covariance matrix efficiently with applications to sliding window recursive least squares (RLS) ...