This paper presents comparisons of two learning-based super-resolution algorithms as well as standard interpolation methods. To allow quality assessment of results, a comparison of a variety of image quality measures ...
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Overconstrained systems refer to sets of soft constraints which do not permit a solution satisfying all the constraints. The overconstrainedness in such soft constraint systems can be manifested in a wide variety of s...
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Overconstrained systems refer to sets of soft constraints which do not permit a solution satisfying all the constraints. The overconstrainedness in such soft constraint systems can be manifested in a wide variety of structures including weighted constraints, partially ordered constraints, constraint hierarchies and references. The simplest among these formalisms is the maximal constraint satisfaction problem, where a solution is sought which satisfies the maximum number of constraints. In this treatise, backtracking algorithms and their intelligent versions used in ordinary constraint satisfaction (CSP) context, are studied in context of maximal constraint satisfaction problem. The algorithms by Freuder and Wallace in [1] for depth-first branch and bound and backjumping, are extended to conflict-directed backjumping. A theoretical analysis of the problem of application of intelligent backtracking algorithms for maximal CSP is provided.
In this paper, we present a uniform approach to simulate and visualize distributed algorithms encoded by graph relabelling systems. In particular, we use the distributed applications of local relabelling rules to auto...
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This research paper explores the transformative impact of machine learning techniques in the field of computer vision. With the rapid evolution of machine learning algorithms, computer vision has witnessed remarkable ...
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The most popular algorithm for blind equalization of QAM signals is the constant modulus algorithm (CMA). However, because the CMA cost function is not matched to constellations with signal points on multiple radii, C...
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The most popular algorithm for blind equalization of QAM signals is the constant modulus algorithm (CMA). However, because the CMA cost function is not matched to constellations with signal points on multiple radii, CMA can exhibit excessive misadjustment for QAM signals. This paper develops an alternative to CMA, the multiple modulus algorithm (MMA), which is based on stochastic gradient minimization of a cost function that goes to zero at each radius of the QAM constellation. For the applicable QAM constellations, MMA exhibits significantly reduced misadjustment compared to CMA. A lattice implementation of MMA is also presented.
During critical situations, the precise digital processing of medical signals such as heartbeats is essential. Outside noise introduced into this data can lead to misinterpretation. Thus, it is important to be able to...
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During critical situations, the precise digital processing of medical signals such as heartbeats is essential. Outside noise introduced into this data can lead to misinterpretation. Thus, it is important to be able to detect and correct the signal quickly and efficiently using digital filtering algorithms. With filtering, the goal is to remove noise locations by correctly identify the corrupted data points and replacing these locations with acceptable estimations of the original values. However, one has to be careful throughout the filtering process not to also eliminate other important detailed information from the original signal. If the filtered output is to be analyzed post-filtering, say for feature recognition, it is important that both the structure and details of the original clean signal remain. This paper presents an original algorithm and two variations, all using the logical transform, that strive to do this accurately and with low levels of computation. Using real heartbeat signals as test sets, the output is compared to that produced by median type filters, and results demonstrated over a variety of noise levels.
Scheduling m high-level synthesis consists of assigning the operations contained within a circuit specification to clock cycles. An operational semantics of a simulation model for the design representation enables the...
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Scheduling m high-level synthesis consists of assigning the operations contained within a circuit specification to clock cycles. An operational semantics of a simulation model for the design representation enables the semantics preserving properties of transformational algorithms to be established. This semantics and a simple scheduling algorithm have been mechanized in an automated theorem prover. The correctness of this algorithm has been proven.
We present two techniques for approximating probability distributions. The first is a simple method for constructing the small-bias probability spaces introduced in [21]. This construction can be efficiently combined ...
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ISBN:
(纸本)0897916638
We present two techniques for approximating probability distributions. The first is a simple method for constructing the small-bias probability spaces introduced in [21]. This construction can be efficiently combined with the method of conditional probabilities to yield improved NC algorithms for many problems such as set cover, set discrepancy, finding large cuts in graphs etc. The second is a construction of small probability spaces approximating general independent distributions, which is of smaller size than the constructions of [13].
We address the problem of efficient out-of-core code generation for a special class of imperfectly nested loops encoding tensor contractions. These loops operate on arrays too large to fit in physical memory. The prob...
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ISBN:
(纸本)0769521320
We address the problem of efficient out-of-core code generation for a special class of imperfectly nested loops encoding tensor contractions. These loops operate on arrays too large to fit in physical memory. The problem involves determining optimal tiling and placement of disk I/O statements. This entails a search in an explosively large parameter space. We formulate the problem as a non-linear optimization problem and use a discrete constraint solver to generate optimized out-of-core code. Measurements on sequential and parallel versions of the generated code demonstrate the effectiveness of the proposed approach.
This paper is concerned with the development of new split algorithms for the design of linear least squares prediction filters with linear phase. The proposed fast algorithm, which fully expresses the inherent symmetr...
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This paper is concerned with the development of new split algorithms for the design of linear least squares prediction filters with linear phase. The proposed fast algorithm, which fully expresses the inherent symmetry of the problem, requires lower computational complexity than other existing ones. Moreover, unlike other existing ones, the new recurrences involve only the order updates, which lend themselves to more efficient hardware implementations. For parallelization consideration, a new split Schur-like algorithm is also proposed to overcome the nonparallelizable inner product. Some numerical simulation results are provided to verify the proposed fast algorithms and highlight possible applications.
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