Many studies have reported that the single-event transient (SET) width increases with temperature. However, the mechanism for this temperature dependency is not clear, especially for an N-hit SET. In this study, TCAD simulations are carried out to study the temperature dependence of N-hit SETs in detail. Several possible factors are examined, and the results show that the temperature dependence in bulk devices is due to the decrease in the carrier mobility with temperature in both the struck NMOS and the pull-up PMOS. In contrast, the temperature dependence in SOI devices is due to the decrease in the diffusion constant and carrier lifetime with temperature, which enhances the parasitic bipolar effect.

Turbo mode, which accelerates many applications without major change of existing systems, is widely used in commercial processors. Since time duration or powerfulness of turbo mode depends on peak temperature of a processor chip, reducing the peak temperature can reinforce turbo mode. This paper presents that adding small amount of hardware allows microprocessors to reduce the peak temperature drastically and then to reinforce turbo mode successfully. Our approach is to find out a few small units that become heat sources in a processor and to appropriately duplicate them for reduction of their power density. By duplicating the limited units and using the copies evenly, the processor can show significant performance improvement while achieving area-efficiency. The experimental result shows that the proposed method achieves up to 14.5% of performance improvement in exchange for 2.8% of area increase.

This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.

One of the most important requirements for a routing protocol in wireless body area networks (WBANs) is to lower the network's temperature increase. The temperature of a node is closely related to its activities. The proactive routing approach, which is used by existing routing protocols for WBANs, tends to produce a higher temperature increase due to more frequent activities, compared to the on-demand reactive routing approach. In this paper, therefore, we propose a reactive routing protocol for WBANs called priority-based temperature-aware routing (PTR). In addition to lowering the temperature increase, the protocol also recognizes vital nodes and prioritizes them so they are able to achieve higher throughput. Simulation results show that the PTR protocol achieves a 50% lower temperature increase compared to the conventional temperature-aware routing protocol and is able to improve throughput of vital nodes by 35% when the priority mode is enabled.

In this letter, we research the method of using face and gesture image sequences to deal with the video-based bimodal emotion recognition problem, in which both Harris plus cuboids spatio-temporal feature (HST) and sparse canonical correlation analysis (SCCA) fusion method are applied to this end. To efficaciously pick up the spatio-temporal features, we adopt the Harris 3D feature detector proposed by Laptev and Lindeberg to find the points from both face and gesture videos, and then apply the cuboids feature descriptor to extract the facial expression and gesture emotion features [1],[2]. To further extract the common emotion features from both facial expression feature set and gesture feature set, the SCCA method is applied and the extracted emotion features are used for the biomodal emotion classification, where the K-nearest neighbor classifier and the SVM classifier are respectively used for this purpose. We test this method on the biomodal face and body gesture (FABO) database and the experimental results demonstrate the better recognition accuracy compared with other methods.

In this paper, we investigate how to achieve call admission control (CAC) for guaranteeing call dropping probability QoS which is caused by handoff timeout in cognitive radio (CR) networks. When primary user (PU) appears, spectrum handoff should be initiated to maintain secondary user (SU)'s link. We propose a novel virtual queuing (VQ) scheme to schedule spectrum handoff requests sent by multiple SUs. Unlike the conventional first-come-first-served (FCFS) scheduling, resuming transmission in the original channel has higher priority than switching to another channel. It costs less because it avoids the cost of signaling frequent spectrum switches. We characterize the handoff delay on the effect of PU's behavior and the number of SUs in CR networks. And user capacity under certain QoS requirement is derived as a guideline for CAC. The analytical results show that call dropping performance can be greatly improved by CAC when a large amount of SUs arrives fast as well as the VQ scheme is verified to reduce handoff cost compared to existing methods.

In analysis of general-purpose problems which involves many factors from different viewpoints, an important challenge is to acquire different opinions and distributed modeling templates from multiple remote experts, and to aggregate these templates. In order to deal with this problem, we developed the Distributed Cooperative Modeling System (DCMS) by integrating our achievements [1]-[5]. The paper introduces how to analyze a complex problem using DCMS, with distributed templates from multiple experts, historical templates based on statistical data, and trend templates deduced from historical data, with the example of analyzing “diversification of Macao industries”

This letter presents a scheme to improve the running time of exemplar-based image inpainting, first proposed by Criminisi et al. In the exemplar-based image inpainting, a patch that contains unknown pixels is compared to all the patches in the known region in order to find the best match. This is very time-consuming and hinders the practicality of Criminisi's method to be used in real time. We show that a simple bounding algorithm can significantly reduce number of distance calculations, and thus the running time. Performance of the bounding algorithm is affected by the order of patches that are compared, as well as the order of pixels in a patch. We present pixel and patch ordering schemes that improve the performance of bounding algorithms. Experiments with well-known images used in inpainting literature show that the proposed reordering scheme can reduce running time of the bounding algorithm up to 50%.

Recognizing human action in complex scenes is a challenging problem in computer vision. Some action-unrelated concepts, such as camera position features, could significantly affect the appearance of local spatio-temporal features, and therefore the performance of low-level features based methods degrades. In this letter, we define the action-unrelated concept: the position of camera as high-level features. We observe that they can serve as a prior to local spatio-temporal features for human action recognition. We encode this prior by modeling interactions between spatio-temporal features and camera position features. We infer camera position features from local spatio-temporal features via these interactions. The parameters of this model are estimated by a new max-margin algorithm. We evaluate the proposed method on KTH, IXMAS and Youtube actions datasets. Experimental results show the effectiveness of the proposed method.

We propose a new face relighting method using an illuminance template generated from a single reference portrait. First, the reference is wrapped according to the shape of the target. Second, we employ a new spatially variant edge-preserving smoothing filter to remove the facial identity and texture details of the wrapped reference, and obtain the illumination template. Finally, we relight the target with the template in CIELAB color space. Experiments show the effectiveness of our method for both grayscale and color faces taken from different databases, and the comparisons with previous works demonstrate a better relighting effect produced by our method.

The most important advantage of HMM-based TTS is its highly intelligible. However, speech synthesized by HMM-based TTS is muffled and far from natural, especially under limited data conditions, which is mainly caused by its over-smoothness. Therefore, the motivation for this paper is to improve the naturalness of HMM-based TTS trained under limited data conditions while preserving its intelligibility. To achieve this motivation, a hybrid TTS between HMM-based TTS and the modified restricted Temporal Decomposition (MRTD), named HTD in this paper, was proposed. Here, TD is an interpolation model of decomposing a spectral or prosodic sequence of speech into sparse event targets and dynamic event functions, and MRTD is one simplified version of TD. With a determination of event functions close to the concept of co-articulation in speech, MRTD can synthesize smooth speech and the smoothness in synthesized speech can be adjusted by manipulating event targets of MRTD. Previous studies have also found that event functions of MRTD can represent linguistic information of speech, which is important to perceive speech intelligibility, while sparse event targets can convey the non-linguistics information, which is important to perceive the naturalness of speech. Therefore, prosodic trajectories and MRTD event functions of the spectral trajectory generated by HMM-based TTS were kept unchanged to preserve the high and stable intelligibility of HMM-based TTS. Whereas MRTD event targets of the spectral trajectory generated by HMM-based TTS were rendered with an original speech database to enhance the naturalness of synthesized speech. Experimental results with small Vietnamese datasets revealed that the proposed HTD was equivalent to HMM-based TTS in terms of intelligibility but was superior to it in terms of naturalness. Further discussions show that HTD had a small footprint. Therefore, the proposed HTD showed its strong efficiency under limited data conditions.

In this paper, penetration phenomenon of an early-time (E1) high altitude electromagnetic pulse (HEMP) into dispersive underground multilayer structures is analyzed using electromagnetic modeling of wave propagation in frequency dependent lossy media. The electromagnetic pulse is dealt with in the power spectrum ranging from 100kHz to the 100MHz band, considering the fact that the power spectrum of the E1 HEMP rapidly decreases 30dB below its maximum value beyond the 100MHz band. In addition, the propagation channel consisting of several dielectric materials is modeled with the dispersive relative permittivity of each medium. Based on source and channel models, the propagation phenomenon is analyzed in the frequency and time domains. The attenuation levels at a 100m underground point are observed to be about 15 and 20dB at 100kHz and 1MHz, respectively, and the peak level of the penetrating electric field is found 5.6kV/m. To ensure the causality of the result, we utilize the Hilbert transform.

In this paper, we propose periodic and aperiodic security limits for compromising emanations in the VHF and UHF bands. First, we perform the electromagnetic emanation security (EMSEC)-channel measurements in the 200-1000MHz frequency bands. Second, we analyse the pathloss characteristics of the indoor EMSEC-channel based on these measurements. Through this EMSEC-channel analysis, we affirm that the total radio attenuation, which is one of the key parameters for determining the security limits for compromising emanations, follows the Rician distribution. With these results, we propose that periodic and aperiodic emission security limits can be classified into two levels depending on the total radio attenuation and the extent of required confidentiality. The proposed security limits are compared with other security limits and existing civil and military EMC standards.

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.

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.

In this paper, we design a new color space YUskin Vskin from YUV color space, based on the principle of skin color with respect to the change of color temperature. Compared with previous work, this color space proved to be the optimal color space for hand segmentation with linear thresholds. We also propose a novel fingertip detection method based on the concomitance between finger and fingernail. The two techniques together improve the performance of hand contour and fingertip extraction in hand gesture recognition.

We have optimized and evaluated organic thin-film solar cell devices with a structure of graded junction. The graded junction consisting of donor and accepter materials was fabricated by varying the deposition rates of both materials with a continuous grading, using two evaporation sources of cupper phthalocyanine and fullerene as p- and n-type materials, respectively. By evaluating device characteristics, optimized device structure ITO/CuPc (10 nm)/graded layer (35 nm)/C60 (15 nm)/BCP (10 nm)/Ag (100 nm) with an efficiency of 1.36% was obtained. In the structure, short-circuit current density was the largest and existence of larger voltage dependence in current density was observed. In addition, we have measured temperature dependences of current density versus voltage characteristics in the graded organic solar cell under illumination. The carrier extraction was enhanced by changing voltage possibly due to the internal electric field of the graded junction.

This paper proposes a novel high-temperature superconducting dual-band bandpass filter (HTS-DBPF), that employs a broadside coupling structure, in which quarter-wavelength resonators are formed on opposite sides of each substrate. This structure provides a dual-band operation of the BPF and flexibility, in the sense of having a wide range in selecting two center passband frequencies of the HTS-DBPF. This paper employs the ratio of the lower and higher center passband frequencies, α, as a criterion for evaluating the flexibility. The obtained α ranges are from 1 to 4.7, which are the widest for DBPFs for mobile communications applications, to the best knowledge of the authors. This paper presents a 2.4-/2.9-GHz band HTS-DBPF, as an experimental example, using a YBCO film deposited on an MgO substrate. The measured frequency responses of the HTS-DBPF agree with the electromagnetic simulated results. Measurement and simulation results confirm that the proposed filter architecture is effective in configuring a DBPF that can set each center passband frequency widely.

In this paper, we propose a pedestrian detection algorithm based on both appearance and motion features to achieve high detection accuracy when applied to complex scenes. Here, a pedestrian's appearance is described by a histogram of oriented spatial gradients, and his/her motion is represented by another histogram of temporal gradients computed from successive frames. Since pedestrians typically exhibit not only their human shapes but also unique human movements generated by their arms and legs, the proposed algorithm is particularly powerful in discriminating a pedestrian from a cluttered situation, where some background regions may appear to have human shapes, but their motion differs from human movement. Unlike the algorithm based on a co-occurrence feature descriptor where significant generalization errors may arise owing to the lack of extensive training samples to cover feature variations, the proposed algorithm describes the shape and motion as unique features. These features enable us to train a pedestrian detector in the form of a spatio-temporal histogram of oriented gradients using the AdaBoost algorithm with a relatively small training dataset, while still achieving excellent detection performance. We have confirmed the effectiveness of the proposed algorithm through experiments on several public datasets.

In this paper, a novel feature named bidirectional local template patterns (B-LTP) is proposed for use in pedestrian detection in still images. B-LTP is a combination and modification of two features, histogram of templates (HOT) and center-symmetric local binary patterns (CS-LBP). For each pixel, B-LTP defines four templates, each of which contains the pixel itself and two neighboring center-symmetric pixels. For each template, it then calculates information from the relationships among these three pixels and from the two directional transitions across these pixels. Moreover, because the feature length of B-LTP is small, it consumes less memory and computational power. Experimental results on an INRIA dataset show that the speed and detection rate of our proposed B-LTP feature outperform those of other features such as histogram of orientated gradient (HOG), HOT, and covariance matrix (COV).