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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Efficient Heuristic Search Algorithms For Soft-Decision Decoding Of Linear Block Codes, Ching-Cheng Shih, C. R. Wulff, Carlos R.P. Hartmann, Chilukuri K. Mohan
Efficient Heuristic Search Algorithms For Soft-Decision Decoding Of Linear Block Codes, Ching-Cheng Shih, C. R. Wulff, Carlos R.P. Hartmann, Chilukuri K. Mohan
Electrical Engineering and Computer Science - Technical Reports
This paper deals with maximum-likelihood soft-decision decoding as well as suboptimal soft-decision decoding of linear block codes. In this paper we present a novel and efficient hybrid decoding algorithm for (n, k) linear block codes. This algorithm consists of three new decoding algorithms: M A*, H*, and Directed Search. It hybridizes these three algorithms to take advantage of their strengths and make the decoding more efficient. The first algorithm, M A*, is a modified Algorithm A* that conducts a heuristic search through a code tree of the transmitted code when the decoding problem is transformed into a problem of graph-search …
Probabilistic Analysis Of The Median Rule: Asymptotics And Applications, Anil Ravindran Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Probabilistic Analysis Of The Median Rule: Asymptotics And Applications, Anil Ravindran Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Electrical Engineering and Computer Science - Technical Reports
The solution of integer optimization problems by relaxation methods consists of three parts. First, the discrete problem is converted into a continuous optimization problem, which is generally more tractable. Second, the relaxed problem is solved efficiently, yielding a optimal solution in the continuous space. Finally, an assignment procedure is used to map this solution to a "suitable" discrete solution. One heuristic - we call it the relaxation heuristic - that often guides the choice and design of assignment algorithms is: "given a continuous optimal solution, the corresponding integer optimal solution is likely to be nearby" (with respect to some well …
Unsupervised Algorithms For Learning Emergent Spatio-Temporal Correlations, Chaitanya Tumuluri
Unsupervised Algorithms For Learning Emergent Spatio-Temporal Correlations, Chaitanya Tumuluri
Electrical Engineering and Computer Science - Technical Reports
Many applications require the extraction of spatiotemporal correlations among dynamically emergent features of non-stationary distributions. In such applications it is not possible to obtain an a priori analytical characterization of the emergent distribution. This paper extends the Growing Cell Structures (GCS) network and presents two novel (GIST and GEST) networks, which combine unsupervised feature-extraction and Hebbian learning, for tracking such emergent correlations. The networks were successfully tested on the challenging Data Mapping problem, using an execution driven simulation of their implementation in hardware. The results of the simulations show the successful use of the GIST and GEST networks for extracting …