Parallel Nonbinary LDPC Decoding on GPU

Nonbinary Low-Density Parity-Check (LDPC) codes are a class of error-correcting codes constructed over the Galois field GF(q) for q > 2. As extensions of binary LDPC codes, nonbinary LDPC codes can provide better error-correcting performance when the code length is short or moderate, but at a cost of higher decoding complexity. This paper proposes a massively parallel implementation of a nonbinary LDPC decoding accelerator based on a graphics processing unit (GPU) to achieve both great flexibility and scalability. The implementation maps the Min-Max decoding algorithm to GPU’s massively parallel architecture. We highlight the methodology to partition the decoding task to a heterogeneous platform consisting of the CPU and GPU. The experimental results show that our GPUbased implementation can achieve high throughput while still providing great flexibility and scalability.National Science Foundation (NSF

Dual guidance in evolutionary multi-objective optimization by localization

In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front using the guided dominance technique in the objective space. With this dual guidance, we can easily guide spheres towards different parts of the Pareto front while also exploring the decision space efficiently

Evolutionary Multi-objective Optimization for Simultaneous Generation of Signal-Type and Symbol-Type Representations

It has been a controversial issue in the research of cognitive science and artificial intelligence whether signal-type representations (typically connectionist networks) or symbol-type representations (e.g., semantic networks, production systems) should be used. Meanwhile, it has also been recognized that both types of information representations might exist in the human brain. In addition, symbol-type representations are often very helpful in gaining insights into unknown systems. For these reasons, comprehensible symbolic rules need to be extracted from trained neural networks. In this paper, an evolutionary multi-objective algorithm is employed to generate multiple models that facilitate the generation of signal-type and symbol-type representations simultaneously. It is argued that one main difference between signal-type and symbol-type representations lies in the fact that the signal-type representations are models of a higher complexity (fine representation), whereas symbol-type representations are models of a lower complexity (coarse representation). Thus, by generating models with a spectrum of model complexity, we are able to obtain a population of models of both signal-type and symbol-type quality, although certain post-processing is needed to get a fully symbol-type representation. An illustrative example is given on generating neural networks for the breast cancer diagnosis benchmark problem. © Springer-Verlag Berlin Heidelberg 2005

Evaluation of the television programme `Eftah Ya Simsim' from the children's point of view

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