621625
Image noise removal in Nakagami fading channels via Bayesian estimator
A maximum likelihood for Bayesian estimator based on alpha-stable was discussed in our previous papers. It is in terms of closer to a realistic situation, and unlike previous methods used for Bayesian estimator, for the case discussed here it is not necessary to know the variance of the noise. The Bayesian estimator here is based on in a Nakagami fading channel. Our previous research results has been extended to that Bayesian estimator that we investigated is still working well for the image noise removal in Nakagami fading channels. As an example, an improved Bayesian estimator (soft and hard threshold methods), is illustrated in our discussion
6974885
Image multi-noise removal by wavelet-based Bayesian estimator
Images are in many cases degraded even before they are encoded. The major noise sources, in terms of distributions, are Gaussian noise, Poisson noise and impulse noise. Noise acquired by images during transmission would be Gaussian in distribution, while images such as emission and transmission tomography images, X-ray films, and photographs taken by satellites are usually contaminated by quantum noise, which is Poisson distributed. Poisson shot noise is a natural generalization of a compound Poisson process when the summands are stochastic processes starting at the points of the underlying Poisson process. Unlike additive Gaussian noise, Poisson noise is signal-dependent and consequently separating signal from noise is more difficult. In our previous papers we discussed a wavelet-based maximum likelihood for Bayesian estimator that recovers the signal component of wavelet coefficients in original images using an alpha-stable signal prior distribution. In this paper, it is demonstrated that the method can be extended to multi-noise sources comprising Gaussian, Poisson, and impulse noise. Results of varying the parameters of the Bayesian estimators of the model are presented after an investigation of /spl alpha/-stable simulations for a maximum likelihood estimator. As an example, a colour image is processed and presented to illustrate the effectiveness of this method.
42418095
An Improved ALOHA Algorithm for RFID Tag Identification
One of problems that we faced for Radio frequency identification (RFID) systems is that the collision between tags which lowers the efficiency of the RFID systems. One of the popular anti-collision algorithms is ALOHA-type algorithms, which are simple and have good performance when the number of tags to be read is reasonable small. In this paper, an enhanced dynamic framed slotted ALOHA algorithm for RFID is extended to a more general close form, hence reference [9] becomes only one special case described in our paper. The improved ALOHA algorithm for RFID tag identifications has much larger capacity to handle the cases when the tag numbers increasing largely while every frame keeping the optimum system efficiency 35.5%. Simulation results show that the proposed algorithm improves the efficiency nicely in comparison with the conventional algorithms keeping >80% as the previous paper shown.
5775998
Identification of clusters in the Web graph based on link topology
The Web graph has recently been used to model the link structure of the Web. The studies of such graphs can yield valuable insights into Web algorithms for crawling, searching and discovery of Web communities. This paper proposes a new approach to clustering the Web graph. The proposed algorithm identifies a small subset of the graph as "core" members of clusters, and then incrementally constructs the clusters by a selection criterion. Two qualitative criteria are proposed to measure the quality of graph clustering. We have implemented our algorithm and tested a set of arbitrary graphs with good results. Applications of our approach include graph drawing and Web visualization.
43590220
Lower Bounds and Parameterized Approach for Longest Common Subsequence
In this paper, different parameterized versions of the longest common subsequence (LCS) problem are extensively investigated and computational lower bound results are derived based on current research progress in parameterized computation. For example, with the number of sequences as the parameter k, the problem is unlikely to be solvable in time f(k)no(k), where n is the length of each sequence and f is any recursive function. The lower bound result is asymptotically tight in consideration of the dynamic programming approach of time O(nk). Computational lower bounds for polynomial-time approximation schemes (PTAS) for the LCS problem are also derived. It is shown that the LCS problem has no PTAS of time f(1/ e)no(1/ e) for any recursive function f, unless all SNP problems are solvable in subexponential time. Compared with former results on this problem, this result has its significance. Finally a parameterized approach for the LCS problem is discussed, which is more efficient than the dynamic programming approach, especially when applied to large scale sequences.
2832131
Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval
In this paper, we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of labeled passages obtained from the blind feedback of a retrieval system. The data mining results are used to expand query terms and to re-estimate some of the parameters used in a probabilistic weighting function. We evaluate the data mining based feedback method on the TREC HARD data set. The results show that data mining can be successfully applied to improve the text retrieval performance. We report our experimental findings in detail.
153966
Compiling for the Impulse memory controller
The Impulse memory controller provides an interface for remapping irregular or sparse memory accesses into dense accesses in the cache memory. This capability significantly increases processor cache and system bus utilization, and previous work shows performance improvements from a factor of 1.2 to 5 with current technology models for hand-coded kernels in a cycle-level simulator. To attain widespread use of any specialized hardware feature requires automating its use in a compiler. We present compiler cost models using dependence and locality analysis that determine when to use Impulse to improve performance based on the reduction in misses, the additional cost for misses in Impulse, and the fixed cost for setting up a remapping. We implement the cost models and generate the appropriate Impulse system calls in the Scale compiler framework. Our results demonstrate that our cost models correctly choose when and when not to use Impulse. We also combine and compare Impulse with our implementation of loop permutation for improving locality. If loop permutation can achieve the same dense access pattern as Impulse, we prefer it, since it has no overheads, but we show that the combination can yield better performance.
43055665
Automatic Abstraction of Graphs Based on Node Similarity for Graph Visualization