A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

A new digital controller for a single-phase boost power factor correction (PFC) converter operating at a discontinuous conduction mode (DCM), is presented to achieve high input power factor over wide input voltage and load range. A method of duty cycle modulation is proposed to reduce the line harmonic distortion and improve the power factor. The loop regulation scheme is adopted to further improve the system stability and the power factor simultaneously. Meanwhile, a novel digital pulse width modulator (DPWM) based on the delay lock loop technique, is realized to improve the regulation linearity of duty cycle and reduce the regulation deviation. The single-phase DCM boost PFC converter with the proposed digital controller based on the field programmable gate array (FPGA) has been implemented. Experimental results indicate that the proposed digital controller can achieve high power factor more than 0.99 over wide input voltage and load range, the output voltage deviation is less than 3V, and the peak conversion efficiency is 96.2% in the case of a full load.

Generalized quasi-cyclic (GQC) codes with arbitrary lengths over the ring $mathbb{F}_{q}+umathbb{F}_{q}$, where u2=0, q=pn, n a positive integer and p a prime number, are investigated. By the Chinese Remainder Theorem, structural properties and the decomposition of GQC codes are given. For 1-generator GQC codes, minimum generating sets and lower bounds on the minimum distance are given.

Linear dynamical systems are basic state space models literally dealing with underlying system dynamics on the basis of linear state space equations. When the model is employed for time-series data analysis, the system identification, which detects the dimension of hidden state variables, is one of the most important tasks. Recently, it has been found that the model has singularities in the parameter space, which implies that analysis for adverse effects of the singularities is necessary for precise identification. However, the singularities in the models have not been thoroughly studied. There is a previous work, which dealt with the simplest case; the hidden state and the observation variables are both one dimensional. The present paper extends the setting to general dimensions and more rigorously reveals the structure of singularities. The results provide the asymptotic forms of the generalization error and the marginal likelihood, which are often used as criteria for the system identification.

Introducing inter-satellite ranging and communication links in a Global Navigation Satellite System (GNSS) can improve its performance. In view of the highly dynamic characteristics and the rapid but reliable acquisition requirement of inter-satellite link (ISL) signal of navigation constellation, we utilize navigation data, which is the special resource of navigation satellites, to assist signal acquisition. In this paper, we introduce a method that uses the navigation data for signal acquisition from three aspects: search space, search algorithm, and detector structure. First, an iteration method to calculate the search space is presented. Then the most efficient algorithm is selected by comparing the computation complexity of different search algorithms. Finally, with the navigation data, we also propose a method to guarantee the detecting probability constant by adjusting the non-coherent times. An analysis shows that with the assistance of navigation data, we can reduce the computing cost of ISL signal acquisition significantly, as well effectively enhancing acquisition speed and stabling the detection probability.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

A high-speed and low-power 8-bit subranging analog-to-digital converter (ADC) based on 65-nm CMOS technology was fabricated. Rather than using digital foreground calibration, an analog-centric approach was adopted to reduce power dissipation. An offset cancelling charge-steering amplifier and capacitive-averaging technique effectively reduce the offset, noise, and power dissipation of the ADC. Moreover, the circuit used to compensate the kickback noise current from the comparator can also reduce the power dissipation. The reference-voltage generator for the fine ADC is composed of a fine ladder and a capacitor providing an AC signal path. This configuration reduces the power dissipation of the selection signal drivers for the analog multiplexer. A test chip fabricated using 65-nm digital CMOS technology achieved a high sampling rate of 1GHz, a low power dissipation of 17.5mW, and a figure of merit of 118fJ/conv.-step.

This paper discusses Mixed Signal LSI technology with embedded power transistors. Trends in Mixed Signal LSI technology are explained at first. Mixed signal LSI technology has proceeded with the help of fine fabrication technology and SOI technology. The BEOL transistor is a new development, which uses InGaZnO (IGZO) as its TFT channel material. The BEOL transistor is one future device which enables 3D IC and chip shrinking technology.

The objective of our research is to develop a support system for creating presentation speech, especially speech that explains relations between two slides (complementary speech). Complementary speech is required between slides whose relations are difficult to understand from their contents, such as texts, figures, and tables. If presenters could notice relations between created slides that are recognized by audiences, they would prepare appropriate complementary speech at the right places. To make presenters notice slides where complementary speech is needed, our system analyzes relations between slides based on their texts and visualizes them. Four slide relations are defined and the method for detecting these relations from the slide texts is proposed. Then, analyzed relations are arranged in two-dimensional spaces that represent sequential relation and inclusive relation of their topics. The experimental results showed that most detected slide relations were the same as what examinees understood, and visualization of slide relations was useful in creating complementary speech, especially for less-experienced presenter.

News articles usually represent a biased viewpoint on contentious issues, potentially causing social problems. To mitigate this media bias, we propose a novel framework for predicting orientation of a news article by analyzing social user behaviors in Twitter. Highly active users tend to have consistent behavior patterns in social network by retweeting behavior among users with the same viewpoints for contentious issues. The bias ratio of highly active users is measured to predict orientation of users. Then political orientation of a news article is predicted based on the bias ratio of users, mutual retweeting and opinion analysis of tweet documents. The analysis of user behavior shows that users with the value of 1 in bias ratio are 88.82%. It indicates that most of users have distinctive orientation. Our prediction method based on orientation of users achieved 88.6% performance in accuracy. Experimental results show significant improvements over the SVM classification. These results show that proposed detection method is effective in social network.

In this Letter, a new face recognition approach based on curvelets and local ternary patterns (LTP) is proposed. First, we observe that the curvelet transform is a new anisotropic multi-resolution transform and can efficiently represent edge discontinuities in face images, and that the LTP operator is one of the best texture descriptors in terms of characterizing face image details. This motivated us to decompose the image using the curvelet transform, and extract the features in different frequency bands. As revealed by curvelet transform properties, the highest frequency band information represents the noisy information, so we directly drop it from feature selection. The lowest frequency band mainly contains coarse image information, and thus we deal with it more precisely to extract features as the face's details using LTP. The remaining frequency bands mainly represent edge information, and we normalize them for achieving explicit structure information. Then, all the extracted features are put together as the elementary feature set. With these features, we can reduce the features' dimension using PCA, and then use the sparse sensing technique for face recognition. Experiments on the Yale database, the extended Yale B database, and the CMU PIE database show the effectiveness of the proposed methods.

To achieve high quality output speech synthesis systems, data-driven grapheme-to-phoneme (G2P) conversion is usually used to generate the phonetic transcription of out-of-vocabulary (OOV) words. To improve the performance of G2P conversion, this paper deals with the problem of conflicting phonemes, where an input grapheme can, in the same context, produce many possible output phonemes at the same time. To this end, we propose a two-stage neural network-based approach that converts the input text to phoneme sequences in the first stage and then predicts each output phoneme in the second stage using the phonemic information obtained. The first-stage neural network is fundamentally implemented as a many-to-many mapping model for automatic conversion of word to phoneme sequences, while the second stage uses a combination of the obtained phoneme sequences to predict the output phoneme corresponding to each input grapheme in a given word. We evaluate the performance of this approach using the American English words-based pronunciation dictionary known as the auto-aligned CMUDict corpus[1]. In terms of phoneme and word accuracy of the OOV words, on comparison with several proposed baseline approaches, the evaluation results show that our proposed approach improves on the previous one-stage neural network-based approach for G2P conversion. The results of comparison with another existing approach indicate that it provides higher phoneme accuracy but lower word accuracy on a general dataset, and slightly higher phoneme and word accuracy on a selection of words consisting of more than one phoneme conflicts.

Demand for wireless communication is increasing significantly, but the frequency resources available for wireless communication are quite limited. Currently, various countries are prompting the use of TV white spaces (TVWS). IEEE 802.11 Working Group (WG) has started a Task Group (TG), namely IEEE 802.11af, to develop an international standard for Wireless local Area Networks (WLANs) in TVWS. In order to increase maximum throughput, a channel aggregation mechanism is introduced in the draft standard. In Japan, ISDB-T based area-one-segment broadcasting system (Area-1seg) which is a digital TV broadcast service in limited areas has been permitted to offer actual TVWS services since April 2012. The operation of the IEEE 802.11af system shall not jeopardize the Area-1seg system due to the common operating frequency band. If the Area-1seg partially overlaps with the IEEE 802.11af in some frequency, the IEEE 802.11af cannot use the channel aggregation mechanism due to a lack of channels. As a result, the throughput of the IEEE 802.11af deteriorates. In this paper, the physical layer of IEEE 802.11af D4.0 is introduced briefly, and a partial subcarrier system for IEEE 802.11af is proposed to efficiently use the TVWS spectrum. The IEEE 802.11af co-exist with the Area-1seg by using null subcarriers. Computer simulation shows up to around 70% throughput gain is achieved with the proposed mechanism.

Canonical correlation analysis (CCA) is applied to extract features for microphone classification. We utilized the coherence between near-silence regions. Experimental results show the promise of canonical correlation features for microphone classification.

An efficient beamforming algorithm for large-scale phased arrays with lossy digital phase shifters is presented. This problem, which arises in microwave power transmission from solar power satellites, is to maximize the array gain in a desired direction with the gain loss of the phase shifters taken into account. In this paper the problem is first formulated as a discrete optimization problem, which is then decomposed into element-wise subproblems by the real rotation theorem. Based on this approach, a polynomial-time algorithm to solve the problem numerically is constructed and its effectiveness is verified by numerical simulations.

The availability of more and more Web services provides great varieties for users to design service processes. However, there are situations that services or service processes cannot meet users' requirements in functional QoS dimensions (e.g., translation quality in a machine translation service). In those cases, composing Web services and human tasks is expected to be a possible alternative solution. However, analysis of such practical efforts were rarely reported in previous researches, most of which focus on the technology of embedding human tasks in software environments. Therefore, this study aims at analyzing the effects of composing Web services and human activities using a case study in the domain of language service with large scale experiments. From the experiments and analysis, we find out that (1) service implementation variety can be greatly increased by composing Web services and human activities for satisfying users' QoS requirements; (2) functional QoS of a Web service can be significantly improved by inducing human activities with limited cost and execution time provided certain quality of human activities; and (3) multiple QoS attributes of a composite service are affected in different ways with different quality of human activities.

We propose a method for downsizing line pictures to generate pixel line arts. In our method, topological properties such as connectivity of lines and segments are preserved by allowing slight distortion in the form of objects in input images. When input line pictures are painted with colors, the number of colors is preserved by our method.

Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Multimode patterns in image data matrices can vary from nominal to significant due to images with different expressions, pose, illumination, or occlusion variations. We show that manifold learning based image clustering models are unable to achieve well separated images at local level for image datasets with significant multimode data patterns. Because gray level image features used in these clustering models are not able to capture the local neighborhood structure effectively for multimode image datasets. In this study, we use nearest neighborhood quality (NNQ) measure based criterion to improve local neighborhood structure in terms of correct nearest neighbors of images locally. We found Gist as the optimal image descriptor among HOG, Gist, SUN, SURF, and TED image descriptors based on an overall maximum NNQ measure on 10 benchmark image datasets. We observed significant performance improvement for recently reported clustering models such as Spectral Embedded Clustering (SEC) and Nonnegative Spectral Clustering with Discriminative Regularization (NSDR) using proposed approach. Experimentally, significant overall performance improvement of 10.5% (clustering accuracy) and 9.2% (normalized mutual information) on 13 benchmark image datasets is observed for SEC and NSDR clustering models. Further, overall computational cost of SEC model is reduced to 19% and clustering performance for challenging outdoor natural image databases is significantly improved by using proposed NNQ measure based optimal image representations.

The hierarchical Dirichlet process (HDP) can provide a nonparametric prior for a mixture model with grouped data, where mixture components are shared across groups. However, the computational cost is generally very high in terms of both time and space complexity. Therefore, developing a method for fast inference of HDP remains a challenge. In this paper, we assume a symmetric multiprocessing (SMP) cluster, which has been widely used in recent years. To speed up the inference on an SMP cluster, we explore hybrid two-level parallelization of the Chinese restaurant franchise sampling scheme for HDP, especially focusing on the application to topic modeling. The methods we developed, Hybrid-AD-HDP and Hybrid-Diff-AD-HDP, make better use of SMP clusters, resulting in faster HDP inference. While the conventional parallel algorithms with a full message-passing interface does not benefit from using SMP clusters due to higher communication costs, the proposed hybrid parallel algorithms have lower communication costs and make better use of the computational resources.

In this paper, we propose voice conversion (VC) based on articulatory features (AF) to vocal-tract parameters (VTP) mapping. An artificial neural network (ANN) is applied to map AF to VTP and to convert a speaker's voice to a target-speaker's voice. The proposed system is not only text-independent VC, in which it does not need parallel utterances between source and target-speakers, but can also be used for an arbitrary source-speaker. This means that our approach does not require source-speaker data to build the VC model. We are also focusing on a small number of target-speaker training data. For comparison, a baseline system based on Gaussian mixture model (GMM) approach is conducted. The experimental results for a small number of training data show that the converted voice of our approach is intelligible and has speaker individuality of the target-speaker.