A novel activation method for a B dopant implanted in a Si substrate using a soft X-ray undulator was examined. As the photon energy of the irradiated soft X-ray approached the energy of the core level of Si 2p, the activation ratio increased. The effect of soft X-ray irradiation on B activation was remarkable at temperatures lower than 400°C. The activation energy of B activation by soft X-ray irradiation (0.06 eV) was lower than that of B activation by furnace annealing (0.18 eV). The activation of the B dopant by soft X-ray irradiation occurs at low temperature, although the activation ratio shows small values of 6.2×10-3 at 110°C. The activation by soft X-ray is caused not only by thermal effects, but also electron excitation and atomic movement.

Low-temperature bonding methods of optoelectronic chips, such as laser diodes (LD) and photodiode (PD) chips, have been the focus of much interest to develop highly functional and compact optoelectronic devices, such as microsensors and communication modules. In this paper, room-temperature bonding of the optoelectronic chips with Au thin film to coined Au stud bumps with smooth surfaces (Ra: 1.3nm) using argon and hydrogen gas mixture atmospheric-pressure plasma was demonstrated in ambient air. The die-shear strength was high enough to exceed the strength requirement of MIL-STD-883F, method 2019 (×2). The measured results of the light-current-voltage characteristics of the LD chips and the dark current-voltage characteristics of the PD chips indicated no degradation after bonding.

In the field of education such as elementary and middle schools, teachers want to take care of schoolchildren during physical trainings and after-school club activities. On the other hand, in the field of sports such as professional and national-level sports, physical or technical trainers want to manage the health, physical and physiological conditions of athletes during exercise trainings in the grounds. In this way, it is required to monitor vital signs for persons during exercises, however, there are several technical problems to be solved in its realization. In this paper, we present the importance and necessity of vital monitoring for persons during exercises, and to make it possible periodically, reliably and in real-time, we present the solutions which we have so far worked out and point out remaining technical challenges in terms of vital/physical sensing, wireless transmission and human interface.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

Verification of temporal logic properties plays a crucial role in proving the desired behaviors of continuous systems. In this paper, we propose an interval method that verifies the properties described by a bounded signal temporal logic. We relax the problem so that if the verification process cannot succeed at the prescribed precision, it outputs an inconclusive result. The problem is solved by an efficient and rigorous monitoring algorithm. This algorithm performs a forward simulation of a continuous-time dynamical system, detects a set of time intervals in which the atomic propositions hold, and validates the property by propagating the time intervals. In each step, the continuous state at a certain time is enclosed by an interval vector that is proven to contain a unique solution. We experimentally demonstrate the utility of the proposed method in formal analysis of nonlinear and complex continuous systems.

Analogy-based software effort estimation has gained a considerable amount of attention in current research and practice. Its excellent estimation accuracy relies on its solution adaptation stage, where an effort estimate is produced from similar past projects. This study proposes a solution adaptation technique named LSA-X that introduces an approach to exploit the potential of productivity factors, i.e., project variables with a high correlation with software productivity, in the solution adaptation stage. The LSA-X technique tailors the exploitation of the productivity factors with a procedure based on the Linear Size Adaptation (LSA) technique. The results, based on 19 datasets show that in circumstances where a dataset exhibits a high correlation coefficient between productivity and a related factor (r≥0.30), the proposed LSA-X technique statistically outperformed (95% confidence) the other 8 commonly used techniques compared in this study. In other circumstances, our results suggest using any linear adaptation technique based on software size to compensate for the limitations of the LSA-X technique.

Facial paralysis is a popular clinical condition occurring in 30 to 40 patients per 100,000 people per year. A quantitative tool to support medical diagnostics is necessary. This paper proposes a simple, visual and robust method that can objectively measure the degree of the facial paralysis by the use of spatiotemporal features. The main contribution of this paper is the proposal of an effective spatiotemporal feature extraction method based on a tracking of landmarks. Our method overcomes the drawbacks of the other techniques such as the influence of irrelevant regions, noise, illumination change and time-consuming process. In addition, the method is simple and visual. The simplification helps to reduce the time-consuming process. Also, the movements of landmarks, which relate to muscle movement ability, are visual. Therefore, the visualization helps reveal regions of serious facial paralysis. For recognition rate, experimental results show that our proposed method outperformed the other techniques tested on a dynamic facial expression image database.

We focus on the feature transform approach as one methodology for biometric template protection, where the template consists of the features extracted from the biometric trait. This study considers some properties of the unitary (including orthogonal) transform-based template protection in particular. It is known that the Euclidean distance between the templates protected by a unitary transform is the same as that between original (non-protected) ones as a property. In this study, moreover, it is shown that it provides the same results in l2-norm minimization problems as those of original templates. This means that there is no degradation of recognition performance in authentication systems using l2-norm minimization. Therefore, the protected templates can be reissued multiple times without original templates. In addition, a DFT-based template protection scheme is proposed as an unitary transform-based one. The proposed scheme enables to efficiently generate protected templates by the FFT, in addition to the useful properties. It is also applied to face recognition experiments to evaluate the effectiveness.

Bugs in industrial financial systems have not been extensively studied. To address this gap, we focused on the empirical study of bugs in three systems, PMS, β-Analyzer, and OrderPro. Results showed the 3 most common types of bugs in industrial financial systems to be internal interface (19.00%), algorithm/method (17.67%), and logic (15.00%).

This report describes an all-digital phase-locked loop (ADPLL) using a temperature compensated settling time reduction technique. The novelty of this work is autonomous oscillation control word estimation without a look-up table or memory circuits. The proposed ADPLL employs a multi-phase digitally controlled oscillator (DCO). In the proposed estimation method, the optimum oscillator tuning word (OTW) is estimated from the DCO frequency characteristic in the setup phase of ADPLL. The proposed ADPLL, which occupies 0.27×0.36mm2, is fabricated by a 65 nm CMOS process. The temperature compensation PLL controller (TCPC) is implemented using an FPGA. Although the proposed method has 20% area overhead, measurement results show that the 47% settling time is reduced. The average settling time at 25°C is 3µs. The average reduction energy is at least 42% from 0°C to 100°C.

The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.

Transparent organic light-emitting diodes (TOLEDs) were investigated with top electrode of indium-tin-oxide (ITO) by ion-plating method. High deposition rate of 4.4 nm/s was realized without plasma damage of under organic layer. In the TOLEDs with inverted structure, high transmittance of over 75% at 550 nm and bright emission of 1,850 and 1,410 cd/m2, from bottom and top side at 163 mA/cm2, respectively, were obtained.

An 8-Mbit 0.18-µm CMOS 1T1C ferroelectric RAM (FeRAM) in a planar ferroelectric technology was developed. Even though the cell area of 2.48 µm2 is almost equal to that of a 4-Mbit stacked-capacitor FeRAM (STACK FeRAM) 2.32 µm2[1], the chip size of the developed 8-Mbit FeRAM, including extra 2-Mbit parities for the error correction code (ECC), is just 52.37 mm2, which is about 30% smaller than twice of the 4-Mbit STACK FeRAM device, 37.68mm2×2[1]. This excellent characteristic can be attributed to the large cell matrix architectures of the sectional cyclic word line (WL) that was used to increase the column numbers, and to the 1T1C bit-line GND level sensing (BGS)[2][3] circuit design intended to sense bit lines (BL) that have bit cells 1K long and a large capacitance. An access time of 52 ns and a cycle time of 77 ns in RT at a VDD of 1.8 V were achieved.

Template tracking has been extensively studied in Computer Vision with a wide range of applications. A general framework is to construct a parametric model to predict movement and to track the target. The difference in intensity between the pixels belonging to the current region and the pixels of the selected target allows a straightforward prediction of the region position in the current image. Traditional methods track the object based on the assumption that the relationship between the intensity difference and the region position is linear or non-linear. They will result in bad tracking performance when just one model is adopted. This paper proposes a method, called as Mixture Hyperplanes Approximation, which is based on finite mixture of generalized linear regression models to perform robust tracking. Moreover, a fast learning strategy is discussed, which improves the robustness against noise. Experiments demonstrate the performance and stability of Mixture Hyperplanes Approximation.

Collective activity recognition plays an important role in high-level video analysis. Most current feature representations look at contextual information extracted from the behaviour of nearby people. Every person needs to be detected and his pose should be estimated. After extracting the feature, hierarchical graphical models are always employed to model the spatio-temporal patterns of individuals and their interactions, and so can not avoid complex preprocessing and inference operations. To overcome these drawbacks, we present a new feature representation method, called attribute-based spatio-temporal (AST) descriptor. First, two types of information, spatio-temporal (ST) features and attribute features, are exploited. Attribute-based features are manually specified. An attribute classifier is trained to model the relationship between the ST features and attribute-based features, according to which the attribute features are refreshed. Then, the ST features, attribute features and the relationship between the attributes are combined to form the AST descriptor. An objective classifier can be specified on the AST descriptor and the weight parameters of the classifier are used for recognition. Experiments on standard collective activity benchmark sets show the effectiveness of the proposed descriptor.

This paper proposes a voice activity detection (VAD) algorithm based on an energy related feature of the frequency modulation of harmonics. A multi-resolution spectro-temporal analysis framework, which was developed to extract texture features of the audio signal from its Fourier spectrogram, is used to extract frequency modulation features of the speech signal. The proposed algorithm labels the voice active segments of the speech signal by comparing the energy related feature of the frequency modulation of harmonics with a threshold. Then, the proposed VAD is implemented on one of Texas Instruments (TI) digital signal processor (DSP) platforms for real-time operation. Simulations conducted on the DSP platform demonstrate the proposed VAD performs significantly better than three standard VADs, ITU-T G.729B, ETSI AMR1 and AMR2, in non-stationary noise in terms of the receiver operating characteristic (ROC) curves and the recognition rates from a practical distributed speech recognition (DSR) system.

Traditional sparse representation-based methods for human action recognition usually pool over the entire video to form the final feature representation, neglecting any spatio-temporal information of features. To employ spatio-temporal information, we present a novel histogram representation obtained by statistics on temporal changes of sparse coding coefficients frame by frame in the spatial pyramids constructed from videos. The histograms are further fed into a support vector machine with a spatial pyramid matching kernel for final action classification. We validate our method on two benchmarks, KTH and UCF Sports, and experiment results show the effectiveness of our method in human action recognition.

In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

This paper proposes an algorithm for exemplar-based image inpainting, which produces the same result as that of Criminisi's original scheme but at the cost of much smaller computation cost. The idea is to compute mean and standard deviation of every patch in the image, and use the values to decide whether to carry out pixel by pixel comparison or not when searching for the best matching patch. Due to the missing pixels in the target patch, the same pixels in the candidate patch should be omitted when computing the distance between patches. Thus, we first compute the range of mean and standard deviation of a candidate patch with missing pixels, using the average and standard deviation of the entire patch. Then we use the range to determine if the pixel comparison should be conducted. Measurements with well-known images in the inpainting literature show that the algorithm can save significant amount of computation cost, without risking degradation of image quality.