The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.

In this letter, a mobile relay station (MRS) for vehicles with beamforming antennas is considered to increase the reliability of transmission link, especially for the MRS at cell boundary. Joint methods for cell searching and DoA estimation are proposed to form a beam in the direction of target BS while nulling interferences from adjacent BSs, especially for IP-based cellular systems employing break-before-make handover or make-before-break handover. The proposed cell searching and DoA estimation methods are evaluated by computer simulation under the environment of IEEE 802.16e (WiBro).

Phased array antennas are attractive in terms of low cost and power consumption. This paper proposes a controlling scheme based on a bisection method for phased array antennas employing phase shifters with slow switching speed, which is typical for Micro Electro Mechanical Systems (MEMS) switches. Computer simulation results, assuming the IEEE 802.11a Wireless Local Area Network (WLAN) standard, show that the proposed scheme has good gain enhancement capability in multipath fading channels.

Binary search tree and framed ALOHA algorithms are commonly adopted to solve the anti-collision problem in RFID systems. In this letter, the read efficiency of these two anti-collision algorithms is compared through computer simulations. Simulation results indicate the framed ALOHA algorithm requires less total read time than the binary search tree algorithm. The initial frame length strongly affects the uplink throughput for the framed ALOHA algorithm.

Variable Block Size Motion Estimation (VBSME) costs a lot of computation during video coding. Search range reduction algorithm is widely used to reduce computational cost of motion estimation. Current VBSME designs are not suitable for this algorithm. This paper proposes a reconfigurable design of VBSME which can be efficiently used with search range reduction algorithm. While using proposed design, nm reference MBs form an MB array which can be processed in parallel. n and m can be configured according to the new search range shape calculated by algorithm. In this way, the parallelism of proposed design is very flexible and can be adapted to any search range shape. The hardware resource is also fully used while performing VBSME. There are two primary reconfigurable modules in this design: PEGA (PE Group Array) and SAD comparator. By using TSMC 0.18 µm standard cell library, the implementation results show that the hardware cost of design which uses 16 PEGs (PE Groups) is about 179 K Gates, the clock frequency is 167 MHz.

Similarity search in time-series databases finds such data sequences whose changing patterns are similar to that of a query sequence. For efficient processing, it normally employs a multi-dimensional index. In order to alleviate the well-known dimensionality curse, the previous methods for similarity search apply the Discrete Fourier Transform (DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes. Other than this ad-hoc approach, there have been no research efforts on devising a systematic guideline for choosing the best organizing attributes. This paper first points out the problems occurring in the previous methods, and proposes a novel solution to construct optimal multi-dimensional indexes. The proposed method analyzes the characteristics of a target time-series database, and identifies the organizing attributes having the best discrimination power. It also determines the optimal number of organizing attributes for efficient similarity search by using a cost model. Through a series of experiments, we show that the proposed method outperforms the previous ones significantly.

Motion estimation (ME) is a computation-intensive module in video coding system. In MPEG-2 to H.264 transcoding, motion vector (MV) from MPEG-2 reused as search center in H.264 encoder is a simple but effective technique to simplify ME processing. However, directly applying MPEG-2 MV as search center will bring difficulties on application of data reuse method in hardware design, because the irregular overlapping of search windows between successive macro block (MB). In this paper, we propose a search window reuse scheme for transcoding, especially for HDTV application. By utilizing the similarity between neighboring MV, overlapping area of search windows can be regularized. Experiment results show that our method achieves average 93.1% search window reuse-rate in HDTV720p sequence with almost no video quality degradation. Compared to transcoding method without any data reuse scheme, bandwidth of the proposed method can be reduced to 40.6% of that.

An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.

We propose a noise suppression method based on multi-model compositions and multi-pass search. In real environments, input speech for speech recognition includes many kinds of noise signals. To obtain good recognized candidates, suppressing many kinds of noise signals at once and finding target speech is important. Before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Noise suppression is frame-synchronously performed using the multiple models selected by recognized label sequences with time alignments. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method obtained higher performance than the conventional method.

The HITS algorithm proposed by Kleinberg is one of the representative methods of scoring Web pages by using hyperlinks. In the days when the algorithm was proposed, most of the pages given high score by the algorithm were really related to a given topic, and hence the algorithm could be used to find related pages. However, the algorithm and the variants including Bharat's improved HITS, abbreviated to BHITS, proposed by Bharat and Henzinger cannot be used to find related pages any more on today's Web, due to an increase of spam links. In this paper, we first propose three methods to find "linkfarms," that is, sets of spam links forming a densely connected subgraph of a Web graph. We then present an algorithm, called a trust-score algorithm, to give high scores to pages which are not spam pages with a high probability. Combining the three methods and the trust-score algorithm with BHITS, we obtain several variants of the HITS algorithm. We ascertain by experiments that one of them, named TaN+BHITS using the trust-score algorithm and the method of finding linkfarms by employing name servers, is most suitable for finding related pages on today's Web. Our algorithms take time and memory no more than those required by the original HITS algorithm, and can be executed on a PC with a small amount of main memory.

In this paper, we discuss the collision search for hash functions, mainly in terms of their advanced message modification. The advanced message modification is a collision search tool based on Wang et al.'s attacks. Two advanced message modifications have previously been proposed: cancel modification for MD4 and MD5, and propagation modification for SHA-0. In this paper, we propose a new concept of advanced message modification, submarine modification. As a concrete example combining the ideas underlying these modifications, we apply submarine modification to the collision search for SHA-0. As a result, we show that this can reduce the collision search attack complexity from 239 to 236 SHA-0 compression operations.

We propose a query language based on extended regular expressions. This language extends texts with text-generating macros. These macros make it possible to define languages in a compressed, elegant way. This paper also extends queries with linear implications and additive (classical) conjunctions. To be precise, it allows goals of the form D —ο G and G1&G2 where D is a text or a macro and G is a query. The first goal is solved by adding D to the current text and then solving G. This goal is flexible in controlling the current text dynamically. The second goal is solved by solving both G1 and G2 from the current text. This goal is particularly useful for internet search.

In this paper, we describe a method of applying Collaborative Filtering with a Machine Learning technique to predict users' preferences for clothes on online shopping malls when user history is insufficient. In particular, we experiment with methods of predicting missing values, such as mean value, SVD, and support vector regression, to find the best method and to develop and utilize a unique feature vector model.

This paper proposes physical channel structures and a cell search method for OFDM based radio access in the Evolved UTRA (UMTS Terrestrial Radio Access) downlink, which supports multiple scalable transmission bandwidths from 1.25 to 20 MHz. In the proposed physical channel structures, the central sub-carrier of the OFDM signal is located on the frequency satisfying the 200-kHz raster condition regardless of the transmission bandwidth of the cell site. Moreover, the synchronization channel (SCH) and broadcast channel (BCH), which are necessary for cell search, are transmitted in the central part of the entire transmission spectrum with a fixed bandwidth. In the proposed cell search method, a user equipment (UE) acquires the target cell in the cell search process in the initial or connected mode employing the SCH and possibly the reference signal, which are transmitted in the central part of the given transmission bandwidth. After detecting the target cell, the UE decodes the common control information through the BCH, which is transmitted at the same frequency as the SCH, and identifies the transmission bandwidth of the cell to be connected. Computer simulations show the fast cell search performance made possible by using the proposed SCH structure and the cell search method.

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

This study initiates a systematic evaluation of web search engine performance using queries written in Thai. Statistical testing indicates that there are some significant differences in the performance of search engines. In addition to compare the search performance, an analysis of the returned results is carried out. The analysis of the returned results shows that the majority of returned results are unique to a particular search engine and each system provides quite different results. This encourages the use of metasearch techniques to combine the search results in order to improve the performance and reliability in finding relevant documents. We examine several metasearch models based on the Borda count and Condorcet voting schemes. We also propose the use of Evolutionary Programming (EP) to optimize weight vectors used by the voting algorithms. The results show that the use of metasearch approaches produces superior performance compared to any single search engine on Thai queries.

A novel high-speed and area-efficient Reed-Solomon decoder is proposed, which employs pipelining architecture of minimized modified Euclid (ME) algorithm. The logic synthesis and simulation results of its VLSI implementation show that it not only can operate at a higher clock frequency, but also consumes fewer hardware resources.

As the amount of recorded TV content is increasing rapidly, people need active and interactive browsing methods. In this paper, we use both text information from closed captions and visual information from video frames to generate links to enable users to easily explore not only the original video content but also augmented information from the Web. This solution especially shows its superiority when the video content cannot be fully represented by closed captions. A prototype system was implemented and some experiments were carried out to prove its effectiveness and efficiency.

This paper proposes a new computational optimization method modified from the dynamic encoding algorithm for searches (DEAS). Despite the successful optimization performance of DEAS for both benchmark functions and parameter identification, the problem of exponential computation time becomes serious as problem dimension increases. The proposed optimization method named univariate DEAS (uDEAS) is especially implemented to reduce the computation time using a univariate local search scheme. To verify the algorithmic feasibility for global optimization, several test functions are optimized as benchmark. Despite the simpler structure and shorter code length, function optimization performance show that uDEAS is capable of fast and reliable global search for even high dimensional problems.

A novel Adaptive Resource-based Probabilistic Search algorithm (ARPS) for P2P networks is proposed in this paper. ARPS introduces probabilistic forwarding for query messages according to the popularity of the resource being searched. A mechanism is introduced to estimate the popularity and adjust the forwarding probability accordingly such that a tradeoff between search performance and cost can be made. Using computer simulations, we compare the performance of ARPS with several other search algorithms. It is shown that ARPS performs well under various P2P scenarios. ARPS guarantees a success rate above a certain level under all circumstances, and enjoys high and popularity-invariant search success rate. Furthermore, ARPS adapts well to the variation of popularity, resulting in high efficiency and flexibility.