7778729
A dynamic K-winners-take-all neural network
In this paper, a dynamic K-winners-take-all (KWTA) neural network, which can quickly identify the K-winning neurons whose activations are larger than the remaining ones, is proposed and analyzed. For N competitors, the proposed KWTA network is composed of N feedforward hardlimit neurons and three feedback neurons, which are used to determine the dynamic threshold. From theoretical analysis and simulation results, we found that the convergence of the proposed KWTA network, which requires Log(2)(N+1) iterations in average to complete a KWTA process, is independent of K, the number of the desired winners, and faster than that of the existing KWTA networks.
26719731
Recursive discrete Fourier transform with unified IIR filter structures
In this paper, we propose two unified IIR filters to recursively compute the discrete Fourier transform (DFT). With the advantage of local connection, regularity and modularity, the proposed methods can compute all DFT coefficients with the same filter structure. Based on stability behavior, the proposed IIR filter structures with optimal coefficients can achieve more accurate results than the traditional ones whose filter coefficients should be changed for computing the different DFT coefficients. Simulation results verify the aforementioned theoretical observations.
17739128
Modified Hough Transforms for Object Feature Extraction
In this paper, we propose the use of modified Hough transforms to efficiently extract object feature parameters, which are usually contaminated by heavily noisy corrugation and discontinuity. The modified HT (MHT) is developed by introducing spatial and parameter weighting functions to improve the detection performance for the traditional Hough transform (HT), which generally fails to robustly detect natural object parameters. Using designed test patterns and real images, simulations show that the proposed weighting functions are helpful in detecting noise-corrupted object features. Due to its robustness, the MHT can be easily figured with a coarse-to-fine adaptive search mechanism to reduce the huge amount of computation for feature parameters extraction.
8764549
Efficient rate-distortion estimation for H.264/AVC coders
In video coders, the optimal coding mode decision for each coding block can be achieved by exhaustively calculating the rate-distortion cost, which simultaneously considers the distortion performance and the coding bit consumption of all possible modes. The best mode is chosen from the one with the minimum Lagrange cost. To avoid the expensive computation of Lagrange costs, in this paper, we propose transform-domain bit-rate estimation and distortion measures, based on quantized and inverse quantized integer transform coefficients, for the inter-mode decision in H.264/AVC coders. With the proposed scheme, entropy coding, inverse transform, and pixel-reconstructions are not required in the process. With ignorable degradation in coding performance, simulations demonstrate that the proposed estimation method achieves about 40% reduced computation time of rate-distortion cost for the inter-mode decision and saves about 17% total encoding time while combining with fast motion estimation and fast mode decision algorithms.
36888588
Efficient connected-index finite-length arithmetic codes
In this paper, we propose a connected-index approach to construct efficient finite-length arithmetic codes by splitting the information of the last nonfitting symbol into the current and subsequent codewords. The proposed arithmetic codes, which limit the error propagation in about one block, require neither a post-appended end-of-block symbol, nor pre-affixed side-information, to characterize the number of encoded symbols. Hence, the proposed finite-length arithmetic codes can nearly achieve the coding efficiency attained by infinite-length arithmetic codes. With high coding efficiency, limited error-propagation, and the regular process, the proposed coding approach is suitable for information exchange with small packets in modern high-speed network systems.
3145676
A Genetic Algorithm with Adaptive Mutations and Family Competition for Training Neural Networks
In this paper, we present a new evolutionary technique to train three general neural networks. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to collaborate with each other. Different mutations act as global and local strategies respectively to balance the trade-off between solution quality and convergence speed. Our algorithm is then applied to three different task domains: Boolean functions, regular language recognition, and artificial ant problems. Experimental results indicate that the proposed algorithm is very competitive with comparable evolutionary algorithms. We also discuss the search power of our proposed approach.
40205984
A Robust Evolutionary Algorithm for Training Neural Networks
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
5516437
Compact reachability labeling for graph-structured data
Testing reachability between nodes in a graph is a well-known problem with many important applications, including knowledge representation, program analysis, and more recently, biological and ontology databases inferencing as well as XML query processing. Various approaches have been proposed to encode graph reachability information using node labeling schemes, but most existing schemes only work well for specific types of graphs. In this paper, we propose a novel approach, HLSS(Hierarchical Labeling of Sub-Structures), which identifies different types of substructures within a graph and encodes them using techniques suitable to the characteristics of each of them. We implement HLSS with an efficient two-phase algorithm, where the first phase identifies and encodes strongly connected components as well as tree substructures, and the second phase encodes the remaining reachability relationships by compressing dense rectangular submatrices in the transitive closure matrix. For the important subproblem of finding densest submatrices, we demonstrate the hardness of the problem and propose several practical algorithms. Experiments show that HLSS handles different types of graphs well, while existing approaches fall prey to graphs with substructures they are not designed to handle.
7776820
Scalable continuous query processing by tracking hotspots
This paper considers the problem of scalably processing a large number of continuous queries. We propose a flexible framework with novel data structures and algorithms for group-processing and indexing continuous queries by exploiting potential overlaps in query predicates. Our approach partitions the collection of continuous queries into groups based on the clustering patterns of the query ranges, and then applies specialized processing strategies to those heavily-clustered groups (or hotspots). To maintain the partition dynamically, we present efficient algorithms that maintain a nearly optimal partition in nearly amortized logarithmic time. We show how to use the hotspots to scalably process large numbers of continuous select-join and band-join queries, which are much more challenging than simple range selection queries. Experiments demonstrate that this approach can improve the processing throughput by orders of magnitude. As another application of hotspots, we show how to use them to build a high-quality histogram for intervals in linear time.
207143686
Estimating The Attribute Weights Through Evidential Reasoning And Mathematical Programming
An approach for estimating attribute weight is proposed, which is addressed to the problem inverse to the aggregation assessment based on a hierarchy of attributes. Evidential reasoning framework is employed since the assessment on some basic attributes may be uncertain and incomplete. As a by-product, a simple formula for evidence combination for our special case is obtained. The weights are estimated based on the preference given by the decision-maker, and represented as intervals. The more abundant preference the decision-maker shows, the clearer the relation among the basic attributes.