12332460
Investigating the Transport Communicating Rates of Wireless Networks over Fading Channels
The large wireless networks under fading, mobility, and delay constraints are further considered under more generic conditions comparison with previous investigating are discussed. The previous implicit restrictions are relaxed, together with more realistic assumptions, make the conclusions, the transport capacity of wireless networks over fading channels more useful for the relevant protocol designs for the wireless networks.
10876880
Multimedia materials for teaching signal processing
Rapidly advancing capabilities in PC-based multimedia technology are providing new opportunities for delivery of educational material. Multimedia technology is being introduced at all levels of the degrees in Electronics and Communications at the University of New England (UNE). In this paper attention is drawn the use of multimedia technology through the example of a fourth-year education package on signal processing. We have used this multimedia education package for teaching and learning during formal class periods and to encourage students to use the technology in their own personal study and projects in order to increase their engineering generic skills. The success of the venture has encouraged us to extend the technology to other selected units in the UNE engineering programs.
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.
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.
15675165
Maximum Common Subgraph: Upper Bound and Lower Bound Results
In bio informatics, the biological structure matching problems can be formulated as the problem of finding the maximum common subgraph. Among the many different variants of the maximum common subgraph problem, the maximum common induced subgraph of two graphs is of special interest. In this paper, first we derive the lower bound result for the exact algorithms of the maximum common induced subgraph of two graphs, based on the current research progress in the area of parameterized computation. Then we investigate the upper bound and design the approaches for addressing this problem. Basically, this problem can be reduced to find a maximum clique in the product graph of the two given graphs. In consideration of the upper bound result, the obtained lower bound result is asymptotically tight. We would like to point out that our lower bound result presented here is currently best-known
15090488
The Islands Approach to Nearest Neighbor Querying in Spatial Networks
Much research has recently been devoted to the data management foundations of location-based mobile services. In one important scenario, the service users are constrained to a transportation network. As a result, query processing in spatial road networks is of interest. We propose a versatile approach to k nearest neighbor computation in spatial networks, termed the Islands approach. By offering flexible yet simple means of balancing re-computation and pre-computation, this approach is able to manage the trade-off between query and update performance. The result is a single, efficient, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance, for both queries and updates.
121152
Dynamic programming algorithms for restriction map comparison
For most sequence comparison problems there is a corresponding map comparison algorithm. While map data may appear to be incompatible with dynamic programming, we show in this paper that the rigor and efficiency of dynamic programming algorithms carry over to the map comparison algorithms. We present algorithms for restriction map comparison that deal with two types of map errors: (i) closely spaced sites for different enzymes can be ordered incorrectly, and (ii) closely spaced sites for the same enzyme can be mapped as a single site. The new algorithms are a natural extension of a previous map comparison model. Dynamic programming algorithms for computing optimal global and local alignments under the new model are described. The new algorithms take about the same order of time as previous map comparison algorithms. Programs implementing some of the new algorithms are used to find similar regions within the Escherichia coli restriction map of Kohara et al.
11235220
Probability-Based Chinese Text Processing and Retrieval
We discuss the use of probability-based natural language processing for Chinese text retrieval. We focus on comparing different text extraction methods and probabilistic weighting methods. Several docu- ment processing methods and probabilistic weighting functions are presented. A number of experiments have been conducted on large standard text collections. We present the experimental results that com- pare a word-based text processing method with a character-based method. The experimental results also compare a number of term-weighting functions including both single-unit weighting and compound-unit weighting functions. Modern information retrieval (IR) systems should be able to take natural language queries and retrieve documents written in natural languages. One way to process nat- ural language texts in IR is statistical. In this approach, linguistic units are extracted from the documents and queries. Those units extracted from the documents are used to index the documents. Those extracted from a query are weighted according to their degrees of importance. The most important units are chosen as query terms. An infor- mation retrieval system takes the selected query terms, matches the terms with the indexes of documents, calculates a retrieval status value for each matched document using a term-weighting method, and presents the user with potentially relevant docu- ments from a collection of documents. The performance of the information retrieval system in identifying relevant documents greatly depends on the text processing method used. We investigate the effectiveness of different text extraction methods and different term weighting methods in the context of Chinese information retrieval. It is a well- known problem that there is no separator between Chinese words, so Chinese words cannot be used easily to index or search texts as is possible in English. Therefore, some people use characters or n-grams as searchable tokens instead of words. We discuss two text extraction methods. One extraction method uses words, compound words, and phrases in the document and query texts as indexing terms to represent the texts. We refer to this method as the word-based approach. For this approach, text segmentation, which divides both document and query texts into linguistic units, is regarded not only as a necessary precursor but also as a bottleneck of this kind of system (Wu and Tseng 1993). The other extraction method is based on single Chinese characters, in which texts are indexed by the characters appearing in the texts (Chen 1992). By using sin- gle character approaches, a search could be conducted for any multi-character word or phrase identified at search time, no matter whether this word or phrase is in the dic- tionary. Both word-based and character-based methods have been used in information
329318
The garbage collection advantage: improving program locality
As improvements in processor speed continue to outpace improvements in cache and memory speed, poor locality increasingly degrades performance. Because copying garbage collectors move objects, they have an opportunity to improve locality. However, no static copying order is guaranteed to match program traversal orders. This paper introduces <i>online object reordering</i> (OOR) which includes a new dynamic, online class analysis for Java that detects program traversal patterns and exploits them in a copying collector. OOR uses run-time method sampling that drives just-in-time (JIT) compilation. For each <i>hot</i> (frequently executed) method, OOR analysis identifies the hot field accesses. At garbage collection time, the OOR collector then copies referents of hot fields together with their parent. Enhancements include static analysis to exclude accesses in cold basic blocks, heuristics that decay heat to respond to phase changes, and a separate space for hot objects. The overhead of OOR is on average negligible and always less than 2% on Java benchmarks in Jikes RVM with MMTk. We compare program performance of OOR to static class-oblivious copying orders (e.g., breadth and depth first). Performance variation due to static orders is often low, but can be up to 25%. In contrast, OOR matches or improves upon the best static order since its history-based copying tunes memory layout to program traversal.