Since the release of human genome sequences, one of the most important research issues is about indexing the genome sequences, and the suffix tree is most widely adopted for that purpose. The traditional suffix tree construction algorithms suffer from severe performance degradation due to the memory bottleneck problem. The recent disk-based algorithms also provide limited performance improvement due to random disk accesses. Moreover, they do not fully utilize the recent CPUs with multiple cores. In this paper, we propose a fast algorithm based on ‘divide-and-conquer’ strategy for indexing the human genome sequences. Our algorithm nearly eliminates random disk accesses by accessing the disk in the unit of contiguous chunks. In addition, our algorithm fully utilizes the multi-core CPUs by dividing the genome sequences into multiple partitions and then assigning each partition to a different core for parallel processing. Experimental results show that our algorithm outperforms the previous fastest DIGEST algorithm by up to 10.5 times.

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The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

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Manufacturing defects in the deep sub-micron VLSI process and aging resulted problems of devices during lifecycle are inevitable, and fault-tolerant routing algorithms are important to provide the required communication for NoCs in spite of failures. The proposed algorithm, referred to as scalable and reconfigurable fault-tolerant distributed routing (RFDR), partitions the system into nine regions using the concept of divide-and-conquer. It is a distributed algorithm, and each router guarantees fault-tolerance within one's own region and the system can be still sustained with multiple fault areas. The proposed RFDR has excellent scalability with hardware cost keeping constant independent of system size. Also it is completely reconfigurable when new nodes fail. Simulations under various synthetic traffic patterns show its better performance compared to Extended-XY routing algorithm. Moreover, there is almost no hardware overhead compared to Logic-Based Distributed Routing (LBDR), but the fault-tolerance capacity is enhanced in the proposed algorithm. Hardware cost is reduced 37% compared to Reconfigurable Distributed Scalable Predictable Interconnect Network (R-DSPIN) which only supports single fault region.

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Power consumption has become an increasing concern in high performance microprocessor design. Especially, Instruction Cache (I-Cache) contributes a large portion of the total power consumption in a microprocessor, since it is a complex unit and is accessed very frequently. Several studies on low-power design have been presented for the power-efficient cache design. However, these techniques usually suffer from the restrictions in the traditional Instruction Fetch Unit (IFU) architectures where the fetch address needs to be sent to I-Cache once it is available. Therefore, work to reduce the power consumption is limited after the address generation and before starting an access. In this paper, we present a new power-aware IFU architecture, named Analysis Before Starting an Access (ABSA), which aims at maximizing the power efficiency of the low-power designs by eliminating the restrictions on those low-power designs of the traditional IFU. To achieve this goal, ABSA reorganizes the IFU pipeline and carefully assigns tasks for each stages so that sufficient time and information can be provided for the low-power techniques to maximize the power efficiency before starting an access. The proposed design is fully scalable and its cost is low. Compared to a conventional IFU design, simulation results show that ABSA saves about 30.3% fetch power consumption, on average. I-Cache employed by ABSA reduces both static and dynamic power consumptions about 85.63% and 66.92%, respectively. Meanwhile the performance degradation is only about 0.97%.

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Conventional array processors randomly access input/coefficient data stored in memory many times during three-dimensional discrete cosine transform (3D-DCT) calculations. This causes a calculation bottleneck. In this paper, a 3D array processor dedicated to 3D-DCT is proposed. The array processor drastically reduces data swapping or replacement during the calculation and thus improves performance. The time complexity of the proposed N×N×N array processor is O(N) for an N³-size input data cube, and that of the 3D-DCT sequential calculation is O(N⁴). A specific I/O architecture, throughput-improved architectures, and more scalable architecture are also discussed in terms of practical implementation. Experimental results of implementation on FPGA (field-programmable gate array) suggest that our architecture provides good performance for real-time 3D-DCT calculations.

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An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.

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It is important to find an efficient design-for-testability methodology that satisfies both security and testability, although there exists an inherent contradiction between security and testability for digital circuits. In our previous work, we reported a secure and testable scan design approach by using extended shift registers that are functionally equivalent but not structurally equivalent to shift registers, and showed a security level by clarifying the cardinality of those classes of shift register equivalents (SR-equivalents). However, SR-equivalents are not always secure for scan-based side-channel attacks. In this paper, we consider a scan-based differential-behavior attack and propose several classes of SR-equivalent scan circuits using dummy flip-flops in order to protect the scan-based differential-behavior attack. To show the security level of those SR-equivalent scan circuits, we introduce a differential-behavior equivalent relation and clarify the number of SR-equivalent scan circuits, the number of differential-behavior equivalent classes and the cardinality of those equivalent classes.

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This paper proposes a proactive management system for the events that occur across multiple personal user devices, including desktop PCs, laptops, and smart phones. We implemented the Personal Event Management Service using Dynamic Bayesian Networks (PEMS-DBN) system that proactively executes appropriate tasks across multiple devices without explicit user requests by recognizing the user's device reuse intention, based on the observed actions of the user for specific devices. The client module of PEMS-DBN installed on each device monitors the user actions and recognizes user intention by using dynamic Bayesian networks. The server provides data sharing and maintenance for the clients. A series of experiments were performed to evaluate user satisfaction and system accuracy, and also the amounts of resource consumption during intention recognition and proactive execution are measured to ensure the system efficiency. The experimental results showed that the PEMS-DBN system can proactively provide appropriate, personalized services with a high degree of satisfaction to the user in an effective and efficient manner.

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This paper provides conceptual and experimental analysis of a new approach in the study of kanji, our “Learner's Visualization (LV) Approach”. In a previous study we found that the LV Approach assists beginning learners in significantly updating their personal kanji deconstruction visualization. Additionally, in another study our findings provided evidence that beginning learners also receive a significant impact in the ability to acquire vocabulary. In this study, our research problem examines how beginning and intermediate students use visualization to cognitively deconstruct (divide) kanji in different ways, and how this affects their learning progress. We analyze the cognitive differences in how kanji learners explore and deconstruct novel kanji while using the LV Approach and how these differences affect their learning process while using the LV Approach. During the learning experience, our LILES System (Learner's Introspective Latent Envisionment System), based on the LV Approach, guides learners to choose from a set of possible “kanji deconstruction layouts” (layouts showing different ways in which a given kanji can be divided). The system then assists learners in updating their “kanji deconstruction level” (the average number of parts they visualize within kanji according to their current abilities). Statistical analysis based on achieved performance was conducted. The analysis of our results proves that there are cognitive differences: beginners deconstruct kanji into more parts (“blocks”) than intermediate learners do, and while both improve their kanji deconstruction scores, there is a more significant change in “kanji deconstruction level” in beginners. However, it was also found that intermediate learners benefit more in “kanji retention score” compared with beginners. Suggestions for further research are provided.

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In this paper, we propose a text corpus design method for a Korean stereo super-wideband speech database. Since a small-sized text corpus for speech coding is generally required for speech coding, the corpus should be designed to comply with the pronunciation behavior of natural conversation in order to ensure efficient speech quality tests. To this end, the proposed design method utilizes a similarity measure between the phoneme distribution occurring from natural conversation and that from the designed text corpus. In order to achieve this goal, we first collect and refine text data from textbooks and websites. Next, a corpus is designed from the refined text data based on the similarity measure to compare phoneme distributions. We then construct a Korean stereo super-wideband speech (K-SW) database using the designed text corpus, where the recording environment is set to meet the conditions defined by ITU-T. Finally, the subjective quality of the K-SW database is evaluated using an ITU-T super-wideband codec in order to demonstrate that the K-SW database is useful for developing and evaluating super-wideband codecs.

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The detection of object categories with large variations in appearance is a fundamental problem in computer vision. The appearance of object categories can change due to intra-class variations, background clutter, and changes in viewpoint and illumination. For object categories with large appearance changes, some kind of sub-categorization based approach is necessary. This paper proposes a sub-category optimization approach that automatically divides an object category into an appropriate number of sub-categories based on appearance variations. Instead of using predefined intra-category sub-categorization based on domain knowledge or validation datasets, we divide the sample space by unsupervised clustering using discriminative image features. We then use a cluster performance analysis (CPA) algorithm to verify the performance of the unsupervised approach. The CPA algorithm uses two performance metrics to determine the optimal number of sub-categories per object category. Furthermore, we employ the optimal sub-category representation as the basis and a supervised multi-category detection system with χ² merging kernel function to efficiently detect and localize object categories within an image. Extensive experimental results are shown using a standard and the authors' own databases. The comparison results reveal that our approach outperforms the state-of-the-art methods.

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This paper introduces a tensorial representation of multiple cameras with arbitrary curvilinear motions. It enables us to define a multilinear relationship among image points derived from non-rigid object motions viewed from multiple cameras with arbitrary curvilinear motions. We show the new multilinear relationship is useful for generating images and reconstructing 3D non-rigid object motions viewed from cameras with arbitrary curvilinear motions. The method is tested in real image sequences.

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For the 3D face recognition numerous methods have been proposed, but little attention has been given to the local-based representation for the texture map of the 3D models. In this paper, we propose a novel 3D face recognition approach based on locally extracted Geodesic Pseudo Zernike Moment Array (GPZMA) of the texture map when only one exemplar per person is available. In the proposed method, the function of the PZM is controlled by the geodesic deformations to tackle the problem of face recognition under the expression and pose variations. The feasibility and effectiveness investigation for the proposed method is conducted through a wide range of experiments using publicly available BU-3DFE and Bosphorus databases including samples with different expression and pose variations. The performance of the proposed method is compared with the performance of three state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the benchmarks in single-model databases.

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In this paper, we propose a novel low-cost Message Passing Interface (MPI) unit between processor nodes, which supports message passing in multiprocessor systems using distributed memory architecture. Our MPI unit operates in the standard mode - using the buffered mode for small amounts of data transaction and the synchronous mode for large amounts of data transaction. This results in increased performance by reducing the control message transmission time for the small amount of data. We verified the performance with a simulator designed based on SystemC. Additionally, we designed the MPI unit using VerilogHDL, and we synthesized it with a synopsys design compiler. The proposed standard mode MPI unit shows a high performance even though the size of the MPI unit occupies less than 1% of the whole chip. Thus, with respect to low-cost design and scalability, this MPI hardware unit is useful to increase overall performance of the embedded Multiprocessor System on a Chip (MPSoC).

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An unsupervised algorithm is proposed for learning overcomplete topographic representations of nature image. Our method is based on Independent Component Analysis (ICA) model due to its superiority on feature extraction, and overcomes the weakness of traditional method in fast overcomplete learning. Besides, the learnt topographic representation, resembling receptive fields of complex cells, can be used as descriptors to extract invariant features. Recognition experiments on Caltech-101 dataset confirm that these complex cell descriptors are not only efficient in feature extraction but achieve comparable performances to traditional descriptors.

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A stereo video coding scheme which is compatible with monoview-processor is presented in this paper. At the same time, this paper proposes an adaptive prediction structure which can make different prediction modes to be applied to different groups of picture (GOPs) according to temporal correlations and interview correlations to improve the coding efficiency. Moreover, the most advanced video coding standard H.264 is used conveniently for maximize the coding efficiency in this paper. Finally, the effectiveness of the proposed scheme is verified by extensive experimental results.

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Nearest neighbor search (NNS) among large-scale and high-dimensional vectors plays an important role in recent large-scale multimedia search applications. This paper proposes an optimized multiple codebook construction method for an approximate NNS scheme based on product quantization, where sets of residual sub-vectors are clustered according to their distribution and the codebooks for product quantization are constructed from these clusters. Our approach enables us to adaptively select the number of codebooks to be used by trading between the search accuracy and the amount of memory available.

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This paper studies the design of Cascade Granular Neural Networks (CGNN) for human-centric systems. In contrast to typical rule-based systems encountered in fuzzy modeling, the proposed method consists of two-phase development for CGNN. First, we construct a Granular Neural Network (GNN) which could be treated as a preliminary design. Next, all modeling discrepancies are compensated by a second GNN with a collection of rules that become attached to the regions of the input space where the error is localized. These granular networks are constructed by building a collection of user-centric information granules through Context-based Fuzzy c-Means (CFCM) clustering. Finally, the experimental results on two examples reveal that the proposed approach shows good performance in comparison with the previous works.

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Since retina blood vessels (RBV) are a major factor in ophthalmological diagnosis, it is essential to detect RBV from a fundus image. In this letter, we proposed the detection method of RBV using a morphological analysis and support vector machine classification. The proposed RBV detection method consists of three strategies: pre-processing, features extraction and classification. In pre-processing, noises were reduced and RBV were enhanced by anisotropic diffusion filtering and illumination equalization. Features were extracted by using the image intensity and morphology of RBV. And a support vector machine (SVM) classification algorithm was used to detect RBV. The proposed RBV detection method was simulated and validated by using the DRIVE database. The averages of accuracy and TPR are 0.94 and 0.78, respectively. Moreover, by comparison, we confirmed that the proposed RBV detection method detected RBV better than the recent RBV detections methods.

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