Automated or semi-automated annotation is a practical solution for large-scale corpus construction. However, the special characteristics of Thai language, such as lack of word-boundary and sentence-boundary markers, trigger several issues in automatic corpus annotation. This paper presents a multi-stage annotation framework, containing two stages of chunking and three stages of tagging. The two chunking stages are pattern matching-based named entity (NE) extraction and dictionary-based word segmentation while the three succeeding tagging stages are dictionary-, pattern- and statist09812490981249ical-based tagging. Applying heuristics of ambiguity priority, NE extraction is performed first on an original text using a set of patterns, in the order of pattern ambiguity. Next, the remaining text is segmented into words with a dictionary. The obtained chunks are then tagged with types of named entities or parts-of-speech (PoS) using dictionaries, patterns and statistics. Focusing on the reduction of human intervention in corpus construction, our experimental results show that the dictionary-based tagging process can assign unique tags to 64.92% of the words, with the remaining of 24.14% unknown words and 10.94% ambiguously tagged words. Later, the pattern-based tagging can reduce unknown words to only 13.34% while the statistical-based tagging can solve the ambiguously tagged words to only 3.01%.

This paper presents a voice conversion (VC) technique for noisy environments, where parallel exemplars are introduced to encode the source speech signal and synthesize the target speech signal. The parallel exemplars (dictionary) consist of the source exemplars and target exemplars, having the same texts uttered by the source and target speakers. The input source signal is decomposed into the source exemplars, noise exemplars and their weights (activities). Then, by using the weights of the source exemplars, the converted signal is constructed from the target exemplars. We carried out speaker conversion tasks using clean speech data and noise-added speech data. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method.

In this paper, we propose a use of the group sparsity in adaptive learning of second-order Volterra filters for the nonlinear acoustic echo cancellation problem. The group sparsity indicates sparsity across the groups, i.e., a vector is separated into some groups, and most of groups only contain approximately zero-valued entries. First, we provide a theoretical evidence that the second-order Volterra systems tend to have the group sparsity under natural assumptions. Next, we propose an algorithm by applying the adaptive proximal forward-backward splitting method to a carefully designed cost function to exploit the group sparsity effectively. The designed cost function is the sum of the weighted group l1 norm which promotes the group sparsity and a weighted sum of squared distances to data-fidelity sets used in adaptive filtering algorithms. Finally, Numerical examples show that the proposed method outperforms a sparsity-aware algorithm in both the system-mismatch and the echo return loss enhancement.

Serial RapidIO (SRIO) is a high-performance interconnection standard for embedded systems. Cyclic Redundancy Check (CRC) provides protection for packet transmissions and impacts the device performances. In this paper, two CRC calculation strategies, based on adjustable slicing parallelization and simplified calculators, are proposed. In the first scheme, the temporary CRC result of the previous cycle (CPre) is considered as a dependent input for the new cycle and is combined with a specific segment of packet data before slicing parallelization. In the second scheme, which can reach a higher maximum working frequency, CPre is considered as an independent input and is separated from the calculation of packet data for further parallelization. Performance comparisons based on ASIC and FPGA implementations are demonstrated to show their effectiveness. Compared with the reference designs, more than 34.8% and 13.9% of average power can be improved by the two proposed schemes at 156.25MHz in 130nm technology, respectively.

MIMO transmission technologies have become an essential component of cellular systems such as Long Term Evolution (LTE) and LTE-Advanced. Recently, evaluating the communication performance of mobile users in cellular MIMO systems has become an urgent requirement. In this paper, we propose dynamic MIMO channel modeling for the urban environment. Our proposal is based on Geometry-based Stochastic Channel Modeling (GSCM). The cluster parameters such as the local scatterer locations around the measurement course are estimated by applying the particle filtering to measured data. We carried out radio propagation measurements in an urban environment at 3.35GHz band, and generated the dynamic channel from the measured data. The experiments showed that both the spreads and auto-correlation of Time of Arrival (ToA), Angle of Arrival (AoA) and Angle of Departure (AoD) were reconstructed within the acceptable error range in our dynamic channel model.

For face recognition with a single training image per person, Collaborative Representation based Classification (CRC) has significantly less complexity than Extended Sparse Representation based Classification (ESRC). However, CRC gets lower recognition rates than ESRC. In order to combine the advantages of CRC and ESRC, we propose Extended Collaborative Representation based Classification (ECRC) for face recognition with a single training image per person. ECRC constructs an auxiliary intraclass variant dictionary to represent the possible variation between the testing and training images. Experimental results show that ECRC outperforms the compared methods in terms of both high recognition rates and low computation complexity.

Sparse representation has been studied within the field of signal processing as a means of providing a compact form of signal representation. This paper introduces a sparse representation based framework named Sparse Probabilistic Linear Discriminant Analysis in speaker recognition. In this latent variable model, probabilistic linear discriminant analysis is modified to obtain an algorithm for learning overcomplete sparse representations by replacing the Gaussian prior on the factors with Laplace prior that encourages sparseness. For a given speaker signal, the dictionary obtained from this model has good representational power while supporting optimal discrimination of the classes. An expectation-maximization algorithm is derived to train the model with a variational approximation to a range of heavy-tailed distributions whose limit is the Laplace. The variational approximation is also used to compute the likelihood ratio score of all trials of speakers. This approach performed well on the core-extended conditions of the NIST 2010 Speaker Recognition Evaluation, and is competitive compared to the Gaussian Probabilistic Linear Discriminant Analysis, in terms of normalized Decision Cost Function and Equal Error Rate.

This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method without specifying the number of sources.

Electromagnetic power transmission through two cyl-inder-penetrated circular apertures in parallel conducting planes is studied. The Weber transform and superposition principle are used to represent the scattered field. A set of simultaneous equations for the modal coefficients are constituted based on the mode-matching and boundary conditions. The whole integration path is slightly deformed into a new one below the positive real axis not to pass through the pole singularities encountered on the original path so that it is easily calculated by direct numerical quadrature. Computation shows the behaviors of power transmission in terms of aperture geometry and wavelength. The presented scheme is very amenable to numerical evaluations and useful for various electromagnetic scattering and antenna radiation analysis involved with singularity problems.

In this paper, online sparse Volterra system identification is proposed. For that purpose, the conventional adaptive projection-based algorithm with weighted l1 balls (APWL1) is revisited for nonlinear system identification, whereby the linear-in-parameters nature of Volterra systems is utilized. Compared with sparsity-aware recursive least squares (RLS) based algorithms, requiring higher computational complexity and showing faster convergence and lower steady-state error due to their long memory in time-invariant cases, the proposed approach yields better tracking capability in time-varying cases due to short-term data dependence in updating the weight. Also, when N is the number of sparse Volterra kernels and q is the number of input vectors involved to update the weight, the proposed algorithm requires O(qN) multiplication complexity and O(Nlog 2N) sorting-operation complexity. Furthermore, sparsity-aware least mean-squares and affine projection based algorithms are also tested.

Near range microwave imaging systems have broad application prospects in the field of concealed weapon detection, biomedical imaging, nondestructive testing, etc. In this paper, the technique of optimized sparse antenna array is applied to near range microwave imaging, which can greatly reduce the complexity of imaging systems. In detail, the paper establishes three-dimensional sparse array imaging geometry and corresponding echo model, where the imaging geometry is formed by arranging optimized sparse antenna array in elevation, scanning in azimuth and transmitting broadband signals in range direction; and by analyzing the characteristics of near range imaging, that is, the maximum interval of transmitting and receiving elements is limited by the range from imaging system to targets, we propose the idea of piecewise sparse line array; secondly, by analyzing the convolution principle, we develop a method of arranging piecewise sparse array which can generate the same distribution of equivalent phase centers as filled antenna array; then, the paper deduces corresponding imaging algorithm; finally, the imaging geometry and corresponding algorithm proposed in this paper are investigated and verified via numerical simulations and near range imaging experiments.

In the image classification applications, the test sample with multiple man-handcrafted descriptions can be sparsely represented by a few training subjects. Our paper is motivated by the success of multi-task joint sparse representation (MTJSR), and considers that the different modalities of features not only have the constraint of joint sparsity across different tasks, but also have the constraint of local manifold structure across different features. We introduce the constraint of local manifold structure into the MTJSR framework, and propose the Locality-constrained multi-task joint sparse representation method (LC-MTJSR). During the optimization of the formulated objective, the stochastic gradient descent method is used to guarantee fast convergence rate, which is essential for large-scale image categorization. Experiments on several challenging object classification datasets show that our proposed algorithm is better than the MTJSR, and is competitive with the state-of-the-art multiple kernel learning methods.

This paper proposes the synchronization control method for two different unified chaotic systems with unknown mismatched parameters using sum of squares method. Previously, feedback-linearizing and stabilization terms were used in the controller for the synchronization problem. However, they used just a constant matrix as a stabilization control gain, whose performance is shown to be valid only for a linear model. Thus, we propose the novel control method for the synchronization of the two different unified chaotic systems with unknown mismatched parameters via sum of squares method. We design the stabilization control input which is of the polynomial form by sum of squares method and also the adaptive law for the estimation of the unknown mismatched parameter between the master and slave systems. Since we can use the polynomial control input which is dependent on the system states as the stabilization controller, the proposed method can have better performance than the previous methods. Numerical simulations for both uni-directional and bi-directional chaotic systems show the validity and advantage of the proposed method.

Chip multiprocessors (CMPs) improve performance by simultaneously executing multiple threads using integrated multiple cores. However, since these cores commonly share one cache, inter-thread cache conflicts often limit the performance improvement by multi-threading. This paper focuses on two causes of inter-thread cache conflicts. In shared caches of CMPs, cached data fetched by one thread are frequently evicted by another thread. Such an eviction, called inter-thread kickout (ITKO), is one of the major causes of inter-thread cache conflicts. The other cause is capacity shortage that occurs when one cache is shared by threads demanding large cache capacities. If the total capacity demanded by the threads exceeds the actual cache capacity, the threads compete to use the limited cache capacity, resulting in capacity shortage. To address inter-thread cache conflicts, we must take into account both ITKOs and capacity shortage. Therefore, this paper proposes a capacity-aware thread scheduling method combined with cache partitioning. In the proposed method, inter-thread cache conflicts due to ITKOs and capacity shortage are decreased by cache partitioning and thread scheduling, respectively. The proposed scheduling method estimates the capacity demand of each thread with an estimation method used in the cache partitioning mechanism. Based on the estimation used for cache partitioning, the thread scheduler decides thread combinations sharing one cache so as to avoid capacity shortage. Evaluation results suggest that the proposed method can improve overall performance by up to 8.1%, and the performance of individual threads by up to 12%. The results also show that both cache partitioning and thread scheduling are indispensable to avoid both ITKOs and capacity shortage simultaneously. Accordingly, the proposed method can significantly reduce the inter-thread cache conflicts and hence improve performance.

We address the problem of processing graph pattern matching queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process such queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filter-and-verify scheme working on the MapReduce framework. Moreover, we devise an efficient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of scalability and efficiency.

Orthogonal arrays have important applications in statistics and computer science, as well as in coding theory. In this letter, a new construction method of symmetric orthogonal arrays of strength t is proposed, which is a concatenation of two orthogonal partitions according to a latin square. As far as we know, this is a new construction of symmetric orthogonal arrays of strength t, where t is a given integer. Based on the different latin squares, we also study the enumeration problem of orthogonal partitions, and a lower bound on the count of orthogonal partitions is derived.

We propose an asynchronous datapath for the low-density parity-check decoder to decrease power consumption. Glitches and redundant computations are decreased by the asynchronous design. Taking advantage of the statistical characteristics of the input data, we develop novel key arithmetic elements in the datapath to reduce redundant computations. Two other types of datapaths, including normal synchronous design and clock-gating design, are implemented for comparisons with the proposed design. The three designs use similar architectures and realize the same function by using the 0.18µm process of the Semiconductor Manufacturing International Corporation. Post-layout result shows that the proposed asynchronous design exhibits the lowest power consumption. The proposed asynchronous design saves 48.7% and 21.9% more power than the normal synchronous and clock-gating designs, respectively. The performance of the proposed datapath is slightly worse than the clock-gating design but is better than the synchronous design. The proposed design is approximately 7% larger than the other two designs.

We present an acoustic model adaptation method where the transformation matrix for a new speaker is given by the product of bases and a weight matrix. The bases are built from the parallel factor analysis 2 (PARAFAC2) of training speakers' transformation matrices. We perform continuous speech recognition experiments using the WSJ0 corpus.

Many applications of wireless sensor networks (WSNs) require secure communication. The tree-based key management scheme, which is a symmetric key scheme, provides backward and forward secrecy. The sensor nodes in the communication group share a secret key for encrypting messages. When the sensor nodes are added to or evicted from the group, the group key has to be updated by sending rekeying messages. In this paper, we propose a method of key tree structure (KTS) generation by considering the addition and eviction ratio of sensor nodes to reduce the number of rekeying messages, which is influenced by the structure of the tree. For this, we define an extension of an existing tree structure such as a binary or ternary tree and generate KTS using an A* algorithm. To reduce the energy consumed by the message transmission, we also exploit genetic algorithm (GA) to build a secure communication group by considering the KTS. In the paper, we show the effectiveness of the proposed method compared with the existing structure via the simulation in terms of memory usage, the number of rekeying messages and energy consumption.

Arc discharge at breaking electrical contact is considered as a main source of not only degradation of the electrical property but also an undesired electromagnetic (EM) noise. In order to clarify the effect of holder temperature on the bridge and arc-duration, opening-waveforms at slowly separating silver-tin dioxide contact with different holder temperature are measured and discussed experimentally in this paper. Firstly, as opening-waveforms, the contact voltage, the contact current and the movement of moving contact related to the gap length are measured simultaneously. Secondly, the relationship between temperature of the holder and duration of the arc was quantified experimentally. It was revealed that as the initial temperature of the holder becomes higher, arc-duration becomes slightly longer. More importantly, the holder temperature dependencies of percentage of each-phase (metallic and gaseous-phases) are different with different closed-current.