Hotspots occur frequently in 3D multi-core processors (3D-MCPs), and they may adversely impact both the reliability and lifetime of a system. We present a new thermally constrained task scheduler based on a thermal-pattern-aware voltage assignment (TPAVA) to reduce hotspots in and optimize the performance of 3D-MCPs. By analyzing temperature profiles of different voltage assignments, TPAVA pre-emptively assigns different initial operating-voltage levels to cores for reducing temperature increase in 3D-MCPs. The proposed task scheduler consists of an on-line allocation strategy and a new voltage-scaling strategy. In particular, the proposed on-line allocation strategy uses the temperature-variation rates of the cores and takes into two important thermal behaviors of 3D-MCPs that can effectively minimize occurrences of hotspots in both thermally homogeneous and heterogeneous 3D-MCPs. Furthermore, a new vertical-grouping voltage scaling (VGVS) strategy that considers thermal correlation in 3D-MCPs is used to handle thermal emergencies. Experimental results indicate that, when compared to a previous online thermally constrained task scheduler, the proposed task scheduler can reduce hotspot occurrences by approximately 66% (71%) and improve throughput by approximately 8% (2%) in thermally homogeneous (heterogeneous) 3D-MCPs. These results indicate that the proposed task scheduler is an effective technique for suppressing hotspot occurrences and optimizing throughput for 3D-MCPs subject to thermal constraints.

Improving robustness in electrostatic discharge (ESD) protection by inserting drain-side isolated silicon-controlled rectifiers (SCRs) in a high-voltage (HV) p-channel lateral-diffused MOSFET (pLDMOS) device was investigated in this paper. Additionally, the effects of anti-ESD reliability in the HV pLDMOS transistors provided by this technique were evaluated. From the experimental data, it was determined that the holding voltage (Vh) values of the pLDMOS with an embedded npn-arranged SCR and discrete thin-oxide (OD) layout on the cathode side increased as the parasitic SCR OD row number decreased. Moreover, the trigger voltage (Vt1) and the Vh values of the pLDMOS with a parasitic pnp-arranged SCR and discrete OD layout on the drain side fluctuated slightly as the SCR OD-row number decreased. Furthermore, the secondary breakdown current (It2) values (i.e., the equivalent ESD-reliability robustness) of all pLDMOS-SCR npn-arranged types increased (>408.4%) to a higher degree than those of the pure pLDMOS, except for npn-DIS_3 and npn-DIS_2, which had low areas of SCRs. All pLDMOS-SCR pnp-arranged types exhibited an increase of up to 2.2A-2.4A, except for the pnp_DIS_3 and pnp_DIS_2 samples; the pnp_DIS_91 increased by approximately 2000.9% (249.1%), exhibiting a higher increase than that of the reference pLDMOS (i.e., the corresponding pnp-stripe type). The ESD robustness of the pLDMOS-SCR pnp-arranged type and npn-arranged type with a discrete OD layout on the SCR cathode side was greater than that of the corresponding pLDMOS-SCR stripe type and a pure pLDMOS, particularly in the pLDMOS-SCR pnp-arranged type.

This work reports a novel power-rail electrostatic discharge (ESD) clamp circuit with parasitic bipolar-junction-transistor (BJT) and channel parallel shunt paths. The parallel shunt paths are formed by delivering a tiny ratio of drain voltage to the gate terminal of the clamp device in ESD events. Under such a mechanism, the proposed circuit achieves enhanced robustness over those of both gate-grounded NMOS (ggNMOS) and the referenced gate-coupled NMOS (gcNMOS). Besides, the proposed circuit also achieves improved fast power-up immunity over that of the referenced gcNMOS. All investigated designs are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and human-body-model (HBM) test results have both confirmed the performance enhancements of the proposed circuit. Finally, the validity of the achieved performance enhancements on other trigger circuits is essentially revealed in this work.

We present a new notion of public-key encryption, called multi-divisible on-line/off-line encryptions, in which partial ciphertexts can be computed and made publicly available for the recipients before the recipients' public key and/or the plaintexts are determined. We formalize its syntax and define several security notions with regard to the level of divisibility, the number of users, and the number of encryption (challenge) queries per user. Furthermore, we show implications and separations between these security notions and classify them into three categories. We also present concrete multi-divisible on-line/off-line encryption schemes. The schemes allow the computationally-restricted and/or bandwidth-restricted devices to transmit ciphertexts with low computational overhead and/or low-bandwidth network.

Many researchers studied computationally-secure (verifiable) secret sharing schemes which distribute multiple secrets with a bulletin board. However, the security definition is ambiguous in many of the past articles. In this paper, we first review existing schemes based on formal definitions of indistinguishability of secrets, verifiability of consistency, and cheater-detectability. And then, we propose a new secret sharing scheme which is the first scheme with indistinguishability of secrets, verifiability, and cheater-detectability, and allows to share secrets with arbitrary access structures. Further, our scheme is provably secure under well known computational assumptions.

This paper proposes an on-line rigid object tracking framework via discriminative object appearance modeling and learning. Strong classifiers are combined with 2D scale-rotation invariant local features to treat tracking as a keypoint matching problem. For on-line boosting, we correspond a Gaussian mixture model (GMM) to each weak classifier and propose a GMM-based classifying mechanism. Meanwhile, self-organizing theory is applied to perform automatic clustering for sequential updating. Benefiting from the invariance of the SURF feature and the proposed on-line classifying technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experiments show that the proposed method achieves better performance than previously reported trackers.

Pre-Cantor bar, the one-dimensional fractal media, consists of two kinds of materials. Using the transmission-line theory we will explain the double-exponential behavior of the minimum of the transmittance as a function of the stage number n, and obtain formulae of two kinds of scaling behaviors of the transmittance. From numerical calculations for n=1 to 5 we will find that the maximum of field amplitudes of resonance which increases double-exponentially with n is well estimated by the theoretical upper bound. We will show that after sorting field amplitudes for resonance frequencies of the 5th stage their distribution is a staircase function of the index.

A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.

Pulse Pairs (PPs) generated by Distance Measure Equipment (DME) cause severe interference on L-band Digital Aeronautical Communication System type 1 (L-DACS1) which is based on Orthogonal Frequency Division Multiplexing (OFDM). In this paper, a novel and practical PP mitigation approach is proposed. Different from previous work, it adopts only time domain methods to mitigate interference, so it will not affect the subsequent signal processing in frequency domain. At the receiver side, the proposed approach can precisely reconstruct the deformed PPs (DPPs) which are often overlapped and have various parameters. Firstly, a filter bank and a correlation scheme are jointly used to detect non-overlapped DPPs, also a weighted average scheme is used to automatically measure the waveform of DPP. Secondly, based on the measured waveform, sparse estimation is used to estimate the precise positions of DPPs. Finally, the parameters of each DPP are estimated by a non-linear estimator. The key point of this step is, a piecewise linear model is used to approximate the non-linear carrier frequency of each DPP. Numerical simulations show that comparing with existing work, the proposed approach is more robust, closer to interference free environment and its Bit Error Rate is reduced by about 10dB.

Layout strategies including source edge to substrate space (SESS) and inserted substrate-pick stripes of gate-grounded NMOS(ggNMOS) are optimized in this work for on-chip electrostatic discharge (ESD) protection. In order to fully investigate influences of substrate resistors on triggering and conduction behaviors of ggNMOS, various devices are designed and fabricated in a 65-nm CMOS process. Direct current (DC), transmission-line-pulsing (TLP), human body model (HBM) and very-fast TLP (VF-TLP) tests are executed to fully characterize performance of fabricated ggNMOS. Test results reveal that an enlarged SESS parameter results in an earlier triggering behavior of ggNMOS, which presents a layout option for subtle adjustable triggering behaviors. Besides, inserted substrate-pick stripes are proved to have a bell-shape influence on the ESD robustness of ggNMOS and this bell-shape influence is valid in TLP, HBM and VF-TLP tests. Moreover, the most ESD-robust ggNMOS optimized under different inserted substrate-pick stripes always achieves a higher HBM level over the traditional ggNMOS at each concerned total device-width. Physical mechanisms of test results will be deeply discussed in this work.

This paper proposes a novel object tracking method via online boosting. The on-line boosting technique is combined with local features to treat tracking as a keypoint matching problem. First, We improve matching reliability by exploiting the statistical repeatability of local features. In addition, we propose 2D scale-rotation invariant quasi-keypoint matching to further improve matching efficiency. Benefiting from SURF feature's statistical repeatability and the complementary quasi-keypoint matching technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experimental results show that the proposed method achieves better performance compared with previously reported trackers.

The paper presents a recognition method of character-position-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

Nonnegative matrix factorization (NMF) is one of the most popular tools for speech enhancement. In this letter, we present an improved semi-supervised NMF (ISNMF)-based speech enhancement algorithm combining techniques of noise estimation and Incremental NMF (INMF). In this approach, fixed speech bases are obtained from training samples offline in advance while noise bases are trained on-the-fly whenever new noisy frame arrives. The INMF algorithm is adopted for noise bases learning because it can overcome the difficulties that conventional NMF confronts in online processing. The proposed algorithm is real-time capable in the sense that it processes the time frames of the noisy speech one by one and the computational complexity is feasible. Four different objective evaluation measures at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed method over traditional semi-supervised NMF (SNMF) and well-known robust principal component analysis (RPCA) algorithm.

This paper describes a significantly improved recognition system for on-line handwritten Japanese text free from line direction and character orientation constraints. The recognition system separates handwritten text of arbitrary character orientation and line direction into text line elements, estimates and normalizes character orientation and line direction, applies two-stage over-segmentation, constructs a segmentation-recognition candidate lattice and evaluates the likelihood of candidate segmentation-recognition paths by combining the scores of character recognition, geometric features and linguistic context. Enhancements over previous systems are made in line segmentation, over-segmentation and context integration model. The results of experiments on text from the HANDS-Kondate_t_bf-2001-11 database demonstrate significant improvements in the character recognition rate compared with the previous systems. Its recognition rate on text of arbitrary character orientation and line direction is now comparable with that possible on horizontal text with normal character orientation. Moreover, its recognition speed and memory requirement do not limit the platforms or applications that employ the recognition system.

Current research trends in computer vision have tended towards achieving the goal of recognizing human action, due to the potential utility of such recognition in various applications. Among many potential approaches, an approach involving Gaussian Mixture Model (GMM) supervectors with a Support Vector Machine (SVM) and a nonlinear GMM KL kernel has been proven to yield improved performance for recognizing human activities. In this study, based on tensor analysis, we develop and exploit an extended class of action features that we refer to as gradient-flow tensor divergence. The proposed method has shown a best recognition rate of 96.3% for a KTH dataset, and reduced processing time.

To achieve more accurate measurements of the mobile station (MS) location, it is possible to integrate many kinds of measurements. In this paper we proposed several hybrid methods that utilized time of arrival (TOA) at seven base stations (BSs) and the angle of arrival (AOA) information at the serving BS to give location estimation of the MS in non-line-of-sight (NLOS) environments. Rather than applying the nonlinear circular lines of position (LOP), the proposed methods are easier by using linear LOP to determine the MS. In addition, the proposed methods can mitigate the NLOS effect, simply by applying the weighted sum of the intersections between different linear LOP and the AOA line, without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods can always yield superior performance in comparison with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) and the previous proposed methods employing circular LOP.

Hidden Credential Retrieval (HCR) protocols are designed for access credentials management where users who remember short passwords can retrieve his/her various credentials (access keys and tokens) with the help of a remote storage server over insecure networks (e.g., the Internet). In this paper, we revisit two HCR protocols, both of which are based on blind signature schemes: one (we call it B-HCR) was proposed in ASIACCS 2009 and the other (we call it MRS-HCR) was in WISA 2010. In particular, we show that the B-HCR protocol is insecure against an outside attacker who impersonates server S. Specifically, the attacker can find out the user's password pw with off-line dictionary attacks by eavesdropping the communications between the user and a third-party online service provider. Also, we show that the MRS-HCR protocol does not work correctly itself. In other words, user U can not retrieve the plaintext Msg (i.e., credentials) even if he/she has a knowledge of the password.

The development of multichannel audio systems has increased the need for multichannel contents. However, the supply of multichannel audio contents is not sufficient for advanced multichannel systems. Therefore, home entertainment manufacturers need upmixing systems, including systems that utilize monaural time-frequency domain information. Therefore, a monaural ambience extraction algorithm based on nonnegative matrix factorization (NMF) has been developed recently. Ambience signals refer to sound components that do not have obvious spatial images, e.g., wind, rain, and diffuse sound. The developed algorithm provides good upmixing performance; however, the algorithm is a batch process and therefore, it cannot be used by home audio manufacturers. In this paper, we propose an on-line monaural ambience extraction algorithm. The proposed algorithm analyzes the dominant components with an on-line NMF algorithm, and extracts the remaining sound as ambience components. Experiments were performed with artificial mixed signals and real music signals, and the performance of the proposed algorithm was compared with the performance of the conventional batch algorithm as a reference. The experimental results show that the proposed algorithm extracts the ambience components as well as the batch algorithm, despite the on-line constraints.

In this paper, we present an efficient time-of-arrival (TOA)-based localization method for wireless sensor networks. The goal of a localization system is to accurately estimate the geographic location of a wireless device. In real wireless sensor networks, accurately estimating mobile device location is difficult because of the presence of various errors. Therefore, localization methods have been studied in recent years. In indoor environments, the accuracy of wireless localization systems is affected by non-line-of-sight (NLOS) errors. The presence of NLOS errors degrades the performance of wireless localization systems. In order to effectively estimate the location of the mobile device, NLOS errors should be recognized and mitigated in indoor environments. In the TOA-based ranging method, the distance between the two wireless devices can be computed by multiplying a signal's propagation delay time by the speed of light. TOA-based localization measures the distance between the mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors of the measured distance between the i-th BS and the MS is different due to dissimilar obstacles in the direct signal path between the two nodes. In order to accurately estimate the location in a TOA-based localization system, an optimized localization algorithm that selects three measured distances with fewer NLOS errors is necessary. We present an efficient TOA-based localization scheme that combines three selected BSs in wireless sensor networks. This localization scheme yields improved localization performance in wireless sensor networks. In this paper, performance tests are performed, and the simulation results are verified through comparisons between various localization methods and the proposed method. As a result, proposed localization scheme using BS selection achieves remarkably better localization performance than the conventional methods. This is verified by experiments in real environments, and demonstrates a performance analysis in NLOS environments. By using BS selection, we will show an efficient and effective TOA-based localization scheme in wireless sensor networks.

More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.