Osama Alkhouli

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In this paper, we derive a "convolution theorem" suitable for the Hirschman optimal transform (HOT), a unitary transform derived from a discrete-time, discrete-frequency version of the entropy-based uncertainty measure first described by Hirschman (1957). We use the result to develop a fast block-LMS adaptive filter which we call the HOT block-LMS adaptive(More)
We present a general convergence analysis of the recently introduced HOT LMS Adaptive filter and show that the autocorrelation matrix in the HOT domain is asymptotically Block diagonal and the HOT LMS adjusts the learning rate of each block to improve the convergence speed of the adaptive filter as compared to LMS. The theoretical findings were verified(More)
In this paper we propose a novel image restoration method that effectively combines a particle filter with wavelet shrinkage to achieve robust performance against inhomogeneous noise mixtures. Specifically, the particle filter acts to suppress outlier-rich components of the noise while, in a subsequent step, the wavelet domain shrinkage attenuates any(More)
In this paper a new block LMS algorithm is introduced. This algorithm is based on a fast HOT convolution developed by our group. We call our algorithm the block HOT-DFT LMS algorithm. Our algorithm uses the premise that the filter size is much smaller than the block size. Our developed algorithm is very similar to the block DFT LMS algorithm, but provides a(More)
We describe a new gust front detection method using functional template correction (FTC) and entropy. The new method provides better boundaries than does a previously developed method by our group (V. DeBrunner and E. Matusiak, 2003). Our proposed method described in this paper requires only one template for detection, thereby reducing the computational(More)
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