The thin film transistor liquid crystal display (TFT-LCD) image has nonuniform brightness, which is a major difficulty in finding the Mura defect region. To facilitate Mura segmentation, globally widely varying background signal must be flattened and then Mura signal must be enhanced. In this paper, Mura signal enhancement and background-signal-flattening methods using wavelet coefficient processing are proposed. The wavelet approximation coefficients are used for background-signal flattening, while wavelet detail coefficients are employed to magnify the Mura signal on the basis of an adapted contrast sensitivity function (CSF). Then, for the enhanced image, trimodal thresholding segmentation technique and a false-region elimination method based on the human visual system (HVS) are employed for reliable Mura segmentation. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding Muras in a TFT-LCD image.

We propose a filtering method for optimal estimation of TFT-LCD's surface region except defect's region. To estimate the non-uniform intensity variation on TFT-LCD surface region, the 4-directional Gaussian filter based on image pyramid structure is proposed. The experimental result verified the proposed method's performance

In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the generalization error is minimized. However, since the generalization error is inaccessible in practice, the model parameters are usually determined so that an estimator of the generalization error is minimized. The regularized subspace information criterion (RSIC) is such a generalization error estimator for model selection. RSIC includes an additional regularization parameter and it should be determined appropriately for better model selection. A meta-criterion for determining the regularization parameter has also been proposed and shown to be useful in practice. In this paper, we show that there are several drawbacks in the existing meta-criterion and give an alternative meta-criterion that can solve the problems. Through simulations, we show that the use of the new meta-criterion further improves the model selection performance.

In this paper, we propose a new modulation named parallel combinatory/high compaction multi-carrier modulation (PC/HC-MCM) using the techniques of parallel combinatory orthogonal frequency division multiplexing (PC-OFDM) and high compaction multi-carrier modulation (HC-MCM). Two types of PC/HC-MCM systems, which are named as modulated PC/HC-MCM system and (unmodulated) PC/HC-MCM system, can be designed. The modulated PC/HC-MCM system achieves better bit-error rate (BER) performance than that of HC-MCM system with equal bandwidth efficiency (BWE). The PC/HC-MCM system can obtain the better peak-to-average power ratio (PAPR) characteristics by selecting appropriate constellation for each subcarrier. On the other hand, since PC/HC-MCM can divide the PC-OFDM symbol duration into multiple time-slots, the advantages of frequency hopping (FH) can be applied in the PC/HC-MCM system. Therefore, we also combine the PC/HC-MCM and frequency hopping multiple access (FHMA) to propose a novel multiple access (MA) system. It can simultaneously transmit multiple users' data within one symbol duration of PC-OFDM.

The antidictionary of a string is the set of all words of minimal length that never appear in this string. Antidictionaries are in particular useful for source coding. We present a fast and memory-efficient algorithm to construct an antidictionary using a suffix tree. It is proved that the complexity of this algorithm is linear in space and time, and its effectiveness is demonstrated by simulation results.

In rich scattering environments, multiple antenna systems designed to accomplish spatial multiplexing have enormous potential of lifting the capacity of corresponding multiple input multiple output channels. In this paper, we present a new low complexity algorithm for decision feedback equalization detector in the SM scheme. The basic idea is to reduce the joint optimization problem to separate optimization problems to achieve better performance-complexity tradeoffs. Concretely, we separately optimize the detection order and the detector filters so that the complexity of the entire signal detection task is reduced. The new order search rule approximates the optimal Bell Labs layered space time (BLAST) approach from a geometrical perspective, and the detector filters are derived using a Cholesky based QR decomposition. The new algorithm is able to switch from zero forcing to minimum mean square error without additional operations and the computational effort is a small fraction of that in the optimal BLAST algorithm. Despite its low complexity, the error performance of new detector closely approximates that of the standard BLAST.

This letter deals with multiuser detection under imprecise knowledge of the received signature codes of all active users for multicarrier code division multiple access (MC-CDMA) systems. The weight vector of the modified multiple constrained minimum variance (MMCMV) is found by projecting the multiple constrained minimum variance (MCMV) weight vector onto a vector subspace constructed from the eigenstructure of the correlation matrix. However, MMCMV still cannot handle the large code-mismatch. Shaping the noise subspace with all estimated active spreading codes, we present an effective approach to achieve more robust capabilities than the MMCMV. Computer simulations show the effectiveness of the proposed detector.

Distributed Denial-of-Service attack (DDoS) is one of the most outstanding menaces on the Internet. A DDoS attack generally attempts to overwhelm the victim in order to deny their services to legitimate users. A number of approaches have been proposed for defending against DDoS attacks accurately in real time. However, existing schemes have limits in terms of detection accuracy and delay if the IDRS (Intrusion Detection and Response System) deployed only at a specific location detects and responds against attacks. As in this case, it is not able to catch the characteristic of the attack which is distributed in large-scale. Moreover, the existing detection schemes have vulnerabilities to intellectual DDoS attacks which are able to avoid its detection threshold or delay its detection time. This paper suggests the effective DDoS defense system which uses the collaborative scheme among distributed IDRSs located in the vicinity of the attack source or victim network. In proposed scheme, both victim and source-end IDRS work synergistically to identify the attack and avoid false alarm rate up to great extent. Additionally, we propose the duplicate detection window scheme to detect various attacks dynamics which increase the detection threshold gradually in early stage. The proposed scheme can effectively detect and respond against these diverse DDoS attack dynamics.

We propose in this paper a SOM-like algorithm that accepts online, as inputs, starts and ends of viewing of a multimedia content by many users; a one-dimensional map is then self-organized, providing an approximation of density distribution showing how many users see a part of a multimedia content. In this way "viewing behavior of crowds" information is accumulated as experience accumulates, summarized into one SOM-like network as knowledge is extracted, and is presented to new users as the knowledge is transmitted. Accumulation of multimedia contents on the Internet increases the need for time-efficient viewing of the contents and the possibility of compiling information on many users' viewing experiences. In the circumstances, a system has been proposed that presents, in the Internet environment, a kind of summary of viewing records of many viewers of a multimedia content. The summary is expected to show that some part is seen by many users but some part is rarely seen. The function is similar to websites utilizing "wisdom of crowds" and is facilitated by our proposed algorithm.

In a community which consists of a large number of people interacting with each other, social dilemma is an important problem. This problem occurs when the payoff of each person conflicts with the total payoff of the community which he/she belongs to. Evolutionary game theory has been used as a powerful mathematical framework to analyze such a social problem. Recently, the authors have proposed replicator dynamics with reallocation of payoffs. In this model, the government is willing to lead the population to a desirable goal state by using collections and reallocations of payoffs. In this paper, we investigate this model, and show conditions for the goal state to be a locally or a globally asymptotically stable equilibrium point, respectively. We also propose a government's strategy depends on population states which can stabilize the goal state globally.

This study introduces the fuzzy Lyapunov function to the fuzzy PID control systems, modified fuzzy systems, with an optimized robust tracking performance. We propose a compound search strategy called conditional linear matrix inequality (CLMI) approach which was composed of the proposed improved random optimal algorithm (IROA) concatenated with the simplex method to solve the linear matrix inequality (LMI) problem. If solutions of a specific system exist, the scheme finds more than one solutions at a time, and these fixed potential solutions and variable PID gains are ready for tracking performance optimization. The effectiveness of the proposed control scheme is demonstrated by the numerical example of a cart-pole system.

We have investigated real-time object tracking using a wide view imaging system. For the system, we have designed and fabricated new smart image sensor with four functions effective in wide view imaging, such as a random access function. In this system, eight smart sensors and an octagonal mirror are used and each image obtained by the sensors is equivalent to a partial image of the wide view. In addition, by using an FPGA for processing, the circuits in this system can be scaled down and a panoramic image can be obtained in real time. For object tracking using this system, the object-detection method based on background subtraction is used. When moving objects are detected in the panoramic image, the objects are constantly displayed on the monitor at higher resolution in real time. In this paper, we describe the random access image sensor and show some results obtained using this sensor. In addition, we describe the wide view imaging system using eight sensors. Furthermore, we explain the method of object tracking in this system and show the results of real-time multipl-object tracking.

In this paper, a computationally efficient stack-based iterative detection algorithm is proposed for V-BLAST systems. To minimize the receiver's efforts as much as possible, the proposed scheme employs iterative tree search for complexity reduction and storage saving. After an M-ary tree structure by QR decomposition of channel matrix is constructed, the full tree depth is divided into the first depth and the remaining ones. At tree depth of one, the proposed algorithm finds M candidate symbols. Based on these symbols, it iteratively searches the remaining symbols at second-to-last depth, until finding an optimal symbol sequence. Simulation results demonstrate that the proposed algorithm yields the performance close to that of sphere detection (SD) with significant saving in complexity and storage.

In this letter, we present a modified printed folded λ/2 dipole antenna design for Digital Video Broadcasting (DVB) applications in UHF band (470-862 MHz). The arms of dipole are meandered to yield an asymmetrical structure. Wideband operation is obtained by increasing dipole-area. The impedance matching of the dipole structure is obtained by inserting some slots on the dipole-arms. This antenna combines omni-directional radiation pattern and wide bandwidth in an easy-to-fabricate structure. The experimental results of the constructed prototype are presented.

Broadcast and synchronization techniques are used for cache coherence control in conventional larger scale snoop-based SMP systems. The penalty for synchronization is directly proportional to system size. Meanwhile, advances in LSI technology now enable placing a memory controller on a CPU die. The latency to access directly linked memory is drastically reduced by an on-die controller. Developing an enterprise server system with these CPUs allows us an opportunity to achieve higher performance. Though the penalty of synchronization is counted whenever a cache miss occurs, it is necessary to improve the coherence method to receive the full benefit of this effect. In this paper, we demonstrate a coherence directory organization that fits into DSM enterprise server systems. Originally, a directory-based method was adopted in high performance computing systems because of its huge scalability in comparison with snoop-based method. Though directory capacity miss and long directory access latency are the major problems of this method, the relaxed scalability requirement of enterprise servers is advantageous to us to solve these problems along with an advanced LSI technology. Our proposed directory solves both problems by implementing a full bit vector level map of the coherence directory on an LSI chip. Our experimental results validate that a system controlled by our proposed directory can surpass a snoop-based system in performance even without applying data localization optimization to an online transaction processing (OLTP) workload.

This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering 'why' question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least three main problems involved in boundaries identification: the implicit boundary delimiter, the nonadjacent cause-consequence, and the effect surrounded by causes. This research proposes using verb-pair rules learnt by comparing the Naïve Bayes classifier (NB) and Support Vector Machine (SVM) to identify causality EDUs in Thai agricultural and health news domains. The boundary identification problems are solved by utilizing verb-pair rules, Centering Theory and cue phrase set. The reason for emphasizing on using verbs to extract causality is that they explicitly make, in a certain way, the consequent events of cause-effect, e.g. 'Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. The outcome of the proposed methodology shown that the verb-pair rules extracted from NB outperform those extracted from SVM when the corpus contains high occurence of each verb, while the results from SVM is better than NB when the corpus contains less occurence of each verb. The verb-pair rules extracted from NB for causality extraction has the highest precision (0.88) with the recall of 0.75 from the plant disease corpus whereas from SVM has the highest precision (0.89) with the recall of 0.76 from bird flu news. For boundary determination, our methodology can handle very well with approximate 96% accuracy. In addition, the extracted causality results from this research can be generalized as laws in the Inductive-Statistical theory of Hempel's explanation theory, which will be useful for QA and reasoning.

The security notion of indifferentiability was proposed by Maurer, Renner, and Holenstein in 2004. In 2005, Coron, Dodis, Malinaud, and Puniya discussed the indifferentiability of hash functions. They have shown that the Merkle-Damgård construction is not secure in the sense of indifferentiability. In this paper, we analyze the security of single-block-length and rate-1 compression functions in the sense of indifferentiability. We formally show that all single-block-length and rate-1 compression functions, which include the Davies-Meyer compression function, are insecure. Furthermore, we show how to construct a secure single-block-length and rate-1 compression function in the sense of indifferentiability. This does not contradict our result above.

Named entities play an important role in many Natural Language Processing applications. Currently, most named entity recognition systems rely on a small set of general named entity (NE) types. Though some efforts have been proposed to expand the hierarchy of NE types, there are still a fixed number of NE types. In real applications, such as question answering or semantic search systems, users may be interested in more diverse specific NE types. This paper proposes a method to extract categories of person named entities from text documents. Based on Dual Iterative Pattern Relation Extraction method, we develop a more suitable model for solving our problem, and explore the generation of different pattern types. A method for validating whether a category is valid or not is proposed to improve the performance, and experiments on Wall Street Journal corpus give promising results.

The support vector machines (SVMs) are one of the most effective classification techniques in several knowledge discovery and data mining applications. However, a SVM requires the user to set the form of its kernel function and parameters in the function, both of which directly affect to the performance of the classifier. This paper proposes a novel method, named a kernel-tree, the function of which is composed of multiple kernels in the form of a tree structure. The optimal kernel tree structure and its parameters is determined by genetic programming (GP). To perform a fine setting of kernel parameters, the gradient descent method is used. To evaluate the proposed method, benchmark datasets from UCI and dataset of text classification are applied. The result indicates that the method can find a better optimal solution than the grid search and the gradient search.

The Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. In the immune response, according to Burnet's clonal selection principle, the antigen imposes a selective pressure on the antibody population by allowing only those cells which specifically recognize the antigen to be selected for proliferation and differentiation. However ongoing investigations indicate that receptor editing, which refers to the process whereby antigen receptor engagement leads to a secondary somatic gene rearrangement event and alteration of the receptor specificity, is occasionally found in affinity maturation process. In this paper, we extend the traditional CSA approach by incorporating the receptor editing method, named RECSA, and applying it to the Traveling Salesman Problem. Thus, both somatic hypermutation (HM) of clonal selection theory and receptor editing (RE) are utilized to improve antibody affinity. Simulation results and comparisons with other general algorithms show that the RECSA algorithm can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of generations.