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This paper develops a novel algorithm, termed SPARse Truncated Amplitude flow (SPARTA), to reconstruct a sparse signal from a small number of magnitude-only measurements. It deals with what is also known as sparse phase retrieval (PR), which is NP-hard in general and emerges in many science and engineering applications. Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations. SPARTA is a simple yet effective, scalable, and fast sparse PR solver. On the theoretical side, for any n-dimensional k-sparse (k ≪ n) signal x with minimum (in modulus) nonzero entries on the order of (1/√k)||x||2, SPARTA recovers the signal exactly (up to a global unimodular constant) from about k2 log n random Gaussian measurements with high probability. Furthermore, SPARTA incurs computational complexity on the order of k2 n log n with total runtime proportional to the time required to read the data, which improves upon the state of the art by at least a factor of k. Finally, SPARTA is robust against additive noise of bounded support. Extensive numerical tests corroborate markedly improved recovery performance and speedups of SPARTA relative to existing alternatives. [ABSTRACT FROM AUTHOR]

A novel data structure is proposed for magnitude-ordering 3-D wavelet transform coefficients. This data structure consists of temporal 1-D orientation trees followed by spatial 2-D orientation trees. Based on this data structure, a coding algorithm is developed for the embedded coding of 3-D wavelet transform coefficients. Experiment results confirm that the proposed coding algorithm outperforms a conventional algorithm that uses asymmetric 3-D orientation trees. [ABSTRACT FROM AUTHOR]

This paper presents a framework for high-level design validation using an efficient register-transfer level (RTL) automatic test pattern generator (ATPG). The RTL ATPG algorithm first generates a test environment for each validation objective, which includes variable assignments, conditional statements, and arithmetic expressions in the hardware description language (HDL) description. The test environment for a given validation objective is a set of symbolic conditions that allow for full controllability and observability of that objective. After the RTL ATPG terminates, a back-end translator intelligently translates the test environments into validation vectors by filling in the necessary values. Since the observability of error effect is naturally handled by the RTL ATPG algorithm, this approach is superior to most existing validation methods, which only focus on the excitation of HDL constructs. A set of heuristics is proposed to utilize high-level circuit information to enhance the RTL ATPG algorithm and to maximize the validation efficiency. The RTL ATPG algorithm is also coupled with an improved RTL validation-coverage metric, which can help users to gain a higher degree of confidence on the quality of generated validation vectors. The usage of the coverage metric also results in the generation of compact vector sets. Experimental results on academic and industrial benchmark circuits demonstrate that our method is able to obtain very high design-error coverage in short execution times. [ABSTRACT FROM AUTHOR]

This paper deals with disparity estimation and the reconstruction of intermediate views from stereoscopic images. Using block-wise maximum-likelihood (ML) disparity estimation, it was found that the Laplacian model outperformed the Cauchy and Gaussian models in terms of disparity compensation errors and the number of correspondence matches. The disparity values in occluded regions were then determined using both object-based and reliability-based interpolation. Finally, an adaptive technique was used to interpolate the intermediate views. One distinguishing characteristic of this algorithm is that the left and right-eye images were projected onto the plane of the intermediate view to be reconstructed. This resulted in two projected images. The intermediate view was created using a weighted average of these two projected images with the weights based on the quality of the corresponding areas of the projected images. Subjective examination of the reconstructed images indicate that they have high image quality and good stable depth when viewed stereoscopically. An objective evaluation with the test image sequence "Flower Garden" shows that the proposed algorithm can achieve a peak signal-to-noise ratio gain of around 1 dB, when compared to a reference algorithm. [ABSTRACT FROM AUTHOR]