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.

The number of networked devices of sensors and actuators continues to increase. We are developing a data-sharing mechanism called uTupleSpace as middleware for storing and retrieving ubiquitous data that are input or output by such devices. uTupleSpace enables flexible retrieval of sensor data and flexible control of actuator devices, and it simplifies the development of various applications. Though uTupleSpace requires scalability against increasing amounts of ubiquitous data, traditional load-distribution methods using a distributed hash table (DHT) are unsuitable for our case because of the ununiformity of the data. Data are nonuniformly generated at some particular times, in some particular positions, and by some particular devices, and their hash values focus on some particular values. This feature makes it difficult for the traditional methods to sufficiently distribute the load by using the hash values. Therefore, we propose a new load-distribution method using a DHT called the dynamic-help method. The proposed method enables one or more peers to handle loads related to the same hash value redundantly. This makes it possible to handle the large load related to one hash value by distributing the load among peers. Moreover, the proposed method reduces the load caused by dynamic load-redistribution. Evaluation experiments showed that the proposed method achieved sufficient load-distribution even when the load was concentrated on one hash value with low overhead. We also confirmed that the proposed method enabled uTupleSpace to accommodate the increasing load with simple operational rules stably and with economic efficiency.

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.

Wireless access in the vehicular environment (WAVE) architecture of intelligent transportation system (ITS) has been standardized in the IEEE 802.11p specification and it is going to be widely deployed in many roadway environments in order to provide prompt emergency information and internet services. A typical WAVE network consists of a number of WAVE devices, in which one is the road-side-unit (RSU) and the others are on-board-units (OBUs), and supports one control channel (CCH) and one or more service channels (SCH) for OBU access. The CCH is used to transport the emergency messages and service information of SCHs and the SCHs could be used to carry internet traffic and non-critical safety traffic of OBUs. However, the IEEE 802.11p contention-based medium access control protocol would suffer degraded transmission efficiency if the number of OBUs contending on an SCH is large. Moreover, synchronizing all WAVE devices to periodically and equally access the CCH and an SCH will waste as much as 50% of the channel resources of the SCH [1]. As a solution, we propose an efficiency-improvement scheme, namely the agent-based coordination (ABC) scheme, which improves the SCH throughput by means of electing one OBU to be the agent to schedule the other OBUs so that they obtain the access opportunities on one SCH and access the other SCH served by RSU in a contention-free manner. Based on the ABC scheme, three different scheduling and/or relaying strategies are further proposed and compared. Numerical results and simulation results confirm that the proposed ABC scheme significantly promotes the standard transmission efficiency.

A photovoltaic (PV)-assisted CMOS rectifier was developed for efficient energy harvesting from ambient radio waves as one example of the synergistic energy harvesting concept. The rectifier operates truly synergistically. A pn junction diode acting as a PV cell converts light energy to DC bias voltage, which compensates the threshold voltage (Vth) of the MOSFETs and enhances the radio frequency (RF) to DC power conversion efficiency (PCE) of the rectifier even under extremely low input power conditions. The indoor illuminance level was sufficient to generate gate bias voltages to compensate Vths. Although the same PV cell structure for biasing nMOS and pMOS transistors was used, photo-generated bias voltages were found to become unbalanced due to the two-layered pn junction structures and parasitic bipolar transistor action. Under typical indoor lighting conditions, a fabricated PV-assisted rectifier achieved a PCE greater than 20% at an RF input power of -20dBm, a frequency of 920MHz, and an output load of 47kΩ. This PCE value is twice the value obtained by a conventional rectifier without PV assistance. In addition, it was experimentally revealed that if symmetric biasing voltages for nMOS and pMOS transistors were available, the PCE would increase even further.

This paper derives a maximum lock range of an injection locked ring oscillator in a direct injection method and presents an injection locked charge-pump phase-locked loop (CPPLL) with a replica of a ring oscillator. The proposed injection-locked PLL separates the injection-locked VCO from the continuous phase-tracking loop of the PLL such that can provide stable lock-state maintenance and tolerance to temperature and supply voltage variation. The measurement results show that the proposed injection-locked PLL can be tolerable to voltage variation of 11.2% in supply voltage of 1.2V. In-band noises of the injection-locked oscillator at offset frequencies of 10kHz and 100kHz are -108.2dBc/Hz and -114.6dBc/Hz, respectively.

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.

This paper gives a write voltage and read reference current generator considering temperature characteristics for multi-level Ge2Sb2Te5-based phase change memories. Since the optimum SET and RESET voltages linearly changes by the temperature, the voltage supply circuit must track this characteristic. In addition, the measurement results show that the read current depends on both read temperature and the write temperature and has exponential dependence on the read temperature. Thus, the binning technique is applied for each read and write temperature regions. The proposed variable TC generator can achieve below ±0.5 LSB precision from the measured differential non-linearity and integral non-linearity. As a result, the temperature characteristics of both the linear write voltage and the exponential read current can be tracked with the proposed variation tolerant linear temperature coefficient current generator.

Data exchange, in which two blocks of data are swapped between cores in distributed memory systems, necessitates additional memory buffer in a multiprocessor system-on-chip. In this paper, we propose a novel bidirectional inter-core communication mechanism called coherent direct memory access (CoDMA). The CoDMA ensures that the writing address is always less than the reading address in coherent read and write mode, so as to avoid read-after-write (RAW) errors. It features an efficient data exchanging scheme without using data buffer in the memory. A four-core single-instruction multiple-data processor is established for the experiments, based on a multi-bus network-on-chip. Experimental results show that the proposed method consumes no additional memory buffer and achieves 39% and 20% average performance improvement compared with traditional Methods 1 and 2, respectively. And a maximal of 43% reduction in memory usage is achieved, at the cost of only 0.22% more area overhead compared with the entire system.

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.

Probabilistic frequent itemset mining, which discovers frequent itemsets from uncertain data, has attracted much attention due to inherent uncertainty in the real world. Many algorithms have been proposed to tackle this problem, but their performance is not satisfactory because handling uncertainty incurs high processing cost. To accelerate such computation, we utilize GPUs (Graphics Processing Units). Our previous work accelerated an existing algorithm with a single GPU. In this paper, we extend the work to employ multiple GPUs. Proposed methods minimize the amount of data that need to be communicated among GPUs, and achieve load balancing as well. Based on the methods, we also present algorithms on a GPU cluster. Experiments show that the single-node methods realize near-linear speedups, and the methods on a GPU cluster of eight nodes achieve up to a 7.1 times speedup.

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 standard fighting videogames, users usually prefer playing against other users rather than against machines because opponents controlled by machines are in a rut and users can memorize their behaviors after repetitive plays. On the other hand, human players adapt to each other's behaviors, which makes fighting videogames interesting. Thus, in this paper, we propose an artificial agent for a fighting videogame that can adapt to its users, allowing users to enjoy the game even when playing alone. In particular, this work focuses on combination attacks, or combos, that give great damage to the opponent. The agent treats combos independently, i.e., it is composed of a subagent for predicting combos the user executes, that for choosing combos the agent executes, and that for controlling the whole agent. Human users evaluated the agent compared to static opponents, and the agent received minimal negative ratings.

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.

This paper presents a facial expression recognition algorithm based on segmentation of a face image into four facial regions (eyes-eyebrows, forehead, mouth and nose). In order to unify the different results obtained from facial region combinations, a modal value approach that employs the most frequent decision of the classifiers is proposed. The robustness of the algorithm is also evaluated under partial occlusion, using four different types of occlusion (half left/right, eyes and mouth occlusion). The proposed method employs sub-block eigenphases algorithm that uses the phase spectrum and principal component analysis (PCA) for feature vector estimation which is fed to a support vector machine (SVM) for classification. Experimental results show that using modal value approach improves the average recognition rate achieving more than 90% and the performance can be kept high even in the case of partial occlusion by excluding occluded parts in the feature extraction process.

This letter presents a no-reference blocking artifact measure based on analysis of color discontinuities in YUV color space. Color shift and color disappearance are first analyzed in JPEG images. For color-shifting and color-disappearing areas, the blocking artifact scores are obtained by computing the gradient differences across the block boundaries in U component and Y component, respectively. An overall quality score is then produced as the average of the local ones. Extensive simulations and comparisons demonstrate the efficiency of the proposed method.

We propose a parameterized multisurface fitting method for multi-frame super-resolution (SR) processing. A parameter assumed for the unknown high-resolution (HR) pixel is used for multisurface fitting. Each surface fitted at each low-resolution (LR) pixel is an expression of the parameter. Final SR result is obtained by fusing the sampling values from these surfaces in the maximum a posteriori fashion. Experimental results demonstrate the superiority of the proposed method.

Travel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods.

A body bias generator (BBG) for fine-grained body biasing (FGBB) is proposed. The FGBB is effective to reduce variability and power consumption in a system-on-chip (SoC). Since FGBB needs a number of BBGs, the BBG is preferred to be implemented in cell-based design procedure. In the cell-based design, it is inefficient to provide an extra supply voltage for BBGs. We invented a BBG with switched capacitor configuration and it enables BBG to operate with wide range of the supply voltage from 0.6V to 1.2V. We fabricated the BBG in a 65nm CMOS process to control 0.1mm2 of core circuit with the area overhead of 1.4% for the BBG.

This paper investigates interference coordination for 3-dimension (3D) antenna array systems in multicell multiple-input multiple-output (MIMO) and orthogonal frequency division multiple-access (OFDMA) wireless networks. Cell-center user and cell-edge user specific downtilts are accordingly partitioned through dynamic vertical beamforming in the 3D MIMO-OFDM communication systems. Taking these user specific downtilts into consideration, the objective of our proposed interference coordination scheme is to maximize both the cell-edge users' and cell-center users' throughput, subject to per base-station (BS) power, cell-center user and cell-edge user specific downtilt constraints. Here, two coordination techniques, consisting of the fractional frequency reuse (FFR) scheme and partial joint process (JP) coordinated multiple point (COMP) transmission mode, are introduced in this paper. To solve the interference coordination problem, two resource block (RB) partitioning schemes are proposed for the above-mentioned coordination techniques accordingly. Based on such RB partitioning, JP CoMP-based dual decomposition method (JC-DDM) and FFR-based dual decomposition method (FDDM) are proposed, where RB assignment, power allocation (RAPA) and downtilts adjustment are jointly optimized. To simplify the computation complexity, a suboptimal algorithm (SOA) is presented to decouple the optimization problem into three subproblems by using FFR scheme. Simulation results show that all of our proposed algorithms outperform the interference coordination scheme with fixed downtilts. JC-DDM and FDDM find the local optimal throughput with different transmission techniques, while SOA iteratively optimize the downtilts and RAPA which shows close-to-optimal performance with much lower computation complexity.