This paper proposes a visualization system for supporting the task of monitoring bug update information. Recent growth of the Web has brought us various kinds of text stream data, such as bulletin board systems (BBS), blogs, and social networking services (SNS). Bug update information managed by bug tracking systems (BTS) is also a kind of text stream data. As such text stream data continuously generates new data, it is difficult for users to watch it all the time. Therefore, the task of monitoring text stream data inevitably involves breaks of monitoring, which would cause users to lose the context of monitoring. In order to support such a monitoring task involving breaks, the proposed system employs several visualization techniques. The dynamic relationship between bugs is visualized with animation, and a function of highlighting updated bugs as well as that of replaying a part of the last monitoring time is also proposed in order to help a user grasping the context of monitoring. The result of experiment with test participants shows that highlighting and replay functions can reduce frequency of checking data and monitoring time.

This paper describes an ultrasonic system that estimates the cell quantity of an artificial culture bone, which is effective for appropriate treat with a composite of this material and Bone Marrow Stromal Cells. For this system, we examine two approaches for analyzing the ultrasound waves transmitted through the cultured bone, including stem cells to estimate cell quantity: multiple regression and fuzzy inference. We employ two characteristics from the obtained wave for applying each method. These features are the amplitude and the frequency; the amplitude is measured from the obtained wave, and the frequency is calculated by the cross-spectrum method. The results confirmed that the fuzzy inference method yields the accurate estimates of cell quantity in artificial culture bone. Using this ultrasonic estimation system, the orthopaedic surgeons can choose the composites that contain favorable number of cells before the implantation.

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.

In this paper, we present a high frame rate (HFR) vision system that can automatically control its exposure time by executing brightness histogram-based image processing in real time at a high frame rate. Our aim is to obtain high-quality HFR images for robust image processing of high-speed phenomena even under dynamically changing illumination, such as lamps flickering at 100 Hz, corresponding to an AC power supply at 50 / 60 Hz. Our vision system can simultaneously calculate a 256-bin brightness histogram for an 8-bit gray image of 512×512 pixels at 2000 fps by implementing a brightness histogram calculation circuit module as parallel hardware logic on an FPGA-based high-speed vision platform. Based on the HFR brightness histogram calculation, our method realizes automatic exposure (AE) control of 512×512 images at 2000 fps using our proposed AE algorithm. The proposed AE algorithm can maximize the number of pixels in the effective range of the brightness histogram, thus excluding much darker and brighter pixels, to improve the dynamic range of the captured image without over- and under-exposure. The effectiveness of our HFR system with AE control is evaluated according to experimental results for several scenes with illumination flickering at 100 Hz, which is too fast for the human eye to see.

This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian distributions. This method involves the bisection of cones whose bases are part of the hyperspheres, in order to locate probabilistically important regions of failure; the determination of these regions accelerates the convergence speed of importance sampling. Clustering of the failure samples determines the required number of Gaussian distributions. Successful static random access memory (SRAM) yield estimations of 6- to 24-dimensional problems are presented. The number of Monte Carlo trials has been reduced by 2-5 orders of magnitude as compared to conventional Monte Carlo simulation methods.

This paper presents a resilient cache memory for dynamic variation tolerance in a 40-nm CMOS. The cache can perform sustained operations under a large-amplitude voltage droop. To realize sustained operation, the resilient cache exploits 7T/14T bit-enhancing SRAM and on-chip voltage/temperature monitoring circuit. 7T/14T bit-enhancing SRAM can reconfigure itself dynamically to a reliable bit-enhancing mode. The on-chip voltage/temperature monitoring circuit can sense a precise supply voltage level of a power rail of the cache. The proposed cache can dynamically change its operation mode using the voltage/temperature monitoring result and can operate reliably under a large-amplitude voltage droop. Experimental result shows that it does not fail with 25% and 30% droop of Vdd and it provides 91 times better failure rate with a 35% droop of Vdd compared with the conventional design.

An LSI Chip-Package-Board integrated power noise simulation model and its validity is discussed in this paper. A unified power delivery network (PDN) of LSI chip, package, and printed circuit board (PCB) is connected with on-chip power supply current models with capacitor charging expression. The proposed modeling flow is demonstrated for the 32-bit microprocessor in a 1.0V 90nm CMOS technology. The PDN of the system that includes a chip, bonding wires and a printed circuit board is modeled in an equivalent circuit. The on-chip power supply noise monitoring technique and the magnetic probe method is applied for validating simulation results. Simulations and measurements explore power supply noise generation with the dependency on operating frequencies in the wide range from 10MHz to 300MHz, under the operation mode of dynamic frequency scaling, and in the long time operation with various operation codes. It is confirmed that the proposed power supply noise simulation model is helpful for the noise estimation throughout the design phase of the LSI system.

In this paper, we propose an optimum access method for a phase change memory (PCM) with NAND strings. A PCM with a block erase interface is proposed. The method, which has a SET block erase operation and fast RESET programming, is proposed since the SET operation causes a slow access time for conventional PCM;. From the results of measurement, the SET-ERASE operation is successfully completed while the RESET-ERASE operation is incomplete owing to serial connection. As a result, the block erase interface with the SET-ERASE and RESET program method realizes a 7.7 times faster write speed compared than a conventional RAM interface owing to the long SET time. We also give pass-transistor design guidelines for PCM with NAND strings. In addition, the write-capability and write-disturb problems are investigated. The ERASE operation for the proposed device structure can be realized with the same current as that for the SET operation of a single cell. For the pass transistor, about 4.4 times larger on-current is needed to carry out the RESET operation and to avoid the write-disturb problem than the minimum RESET current of a single cell. In this paper, the SET programming method is also verified for a conventional RAM interface. The experimental results show that the write-capability and write-disturb problems are negligible.

Cognitive radio (CR) systems aim for more efficient spectrum utilization by having so called secondary users (SUs) transmit on a frequency band reserved for licensed primary users (PUs). The secondary transmissions are allowed provided that no harmful interference will be caused to the PUs. SU terminals with multiple antennas can employ transmit power control with transmit precoding in order to control the interference levels. In most of the existing works, perfect channel state information (CSI) is assumed to be available for the SUs. However, in practical systems where perfect CSI is not available, the SUs are not able to guarantee that the interference constraints are sufficiently satisfied. In this paper, we investigate the problem of spectrum sharing for multiantenna CR systems using estimated CSI. Due to the random nature of the estimation error, we set a probabilistic interference constraint and, in order to satisfy it, provide a density function for the interference power. In addition, we present a power control framework for the SU to meet the probabilistic interference constraint.

Squared-loss mutual information (SMI) is a robust measure of the statistical dependence between random variables. The sample-based SMI approximator called least-squares mutual information (LSMI) was demonstrated to be useful in performing various machine learning tasks such as dimension reduction, clustering, and causal inference. The original LSMI approximates the pointwise mutual information by using the kernel model, which is a linear combination of kernel basis functions located on paired data samples. Although LSMI was proved to achieve the optimal approximation accuracy asymptotically, its approximation capability is limited when the sample size is small due to an insufficient number of kernel basis functions. Increasing the number of kernel basis functions can mitigate this weakness, but a naive implementation of this idea significantly increases the computation costs. In this article, we show that the computational complexity of LSMI with the multiplicative kernel model, which locates kernel basis functions on unpaired data samples and thus the number of kernel basis functions is the sample size squared, is the same as that for the plain kernel model. We experimentally demonstrate that LSMI with the multiplicative kernel model is more accurate than that with plain kernel models in small sample cases, with only mild increase in computation time.

Power supply noise waveforms within cryptographic VLSI circuits in a 65nm CMOS technology are captured by using an on-chip voltage waveform monitor (OCM). The waveforms exhibit the correlation of dynamic voltage drops to internal logical activities during Advance Encryption Standard (AES) processing, and causes side-channel information leakage regarding to secret key bytes. Correlation Power Analysis (CPA) is the method of an attack extracting such information leakage from the waveforms. The frequency components of power supply noise contributing the leakage are shown to be localized in an extremely low frequency region. The level of information leakage is strongly associated with the size of increment of dynamic voltage drops against the Hamming distance in the AES processing. The time window of significant importance where the leakage most likely happens is clearly designated within a single clock cycle in the final stage of AES processing. The on-chip power supply noise measurements unveil the facts about side-channel information leakage behind the traditional CPA with on-board sensing of power supply current through a resistor of 1 ohm.

In this paper, we apply a mutation operation based on a multivariate Cauchy distribution to fast evolutionary programming and analyze its effect in terms of various function optimizations. The conventional fast evolutionary programming in-cooperates the univariate Cauchy mutation in order to overcome the slow convergence rate of the canonical Gaussian mutation. For a mutation of n variables, while the conventional method utilizes n independent random variables from a univariate Cauchy distribution, the proposed method adopts n mutually dependent random variables that satisfy a multivariate Cauchy distribution. The multivariate Cauchy distribution naturally has higher probabilities of generating random variables in inter-variable regions than the univariate Cauchy distribution due to the mutual dependence among variables. This implies that the multivariate Cauchy random variable enhances the search capability especially for a large number of correlated variables, and, as a result, is more appropriate for optimization schemes characterized by interdependence among variables. In this sense, the proposed mutation possesses the advantage of both the univariate Cauchy and Gaussian mutations. The proposed mutation is tested against various types of real-valued function optimizations. We empirically find that the proposed mutation outperformed the conventional Cauchy and Gaussian mutations in the optimization of functions having correlations among variables, whereas the conventional mutations showed better performance in functions of uncorrelated variables.

This paper describes a new method for lossy audio signal compression via compressive sensing (CS). In this method, a structured shrinkage operator is employed to decompose the audio signal into three layers, with two sparse layers, tonal and transient, and additive noise, and then, both the tonal and transient layers are compressed using CS. Since the shrinkage operator is able to take into account the structure information of the coefficients in the transform domain, it is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two layers, thus improving the performance of CS. Experimental results demonstrated that the new method provided a better compression performance than conventional methods did.

A joint continuous phase frequency shift keying (CPFSK) modulation and physical-layer network coding (PNC), i.e., CPFSK-PNC, is proposed for two-way relay channels (TWRCs). This letter discusses the signal detection of the CPFSK-PNC scheme with emphasis on the maximum-likelihood sequence detection (MLSD) algorithm for the relay receiver. The end-to-end error performance of the proposed CPFSK-PNC scheme is evaluated through simulations.

In this letter, we propose a new secure and efficient certificateless aggregate signature scheme which has the advantages of both certificateless public key cryptosystem and aggregate signature. Based on the computational Diffie-Hellman problem, our scheme can be proven existentially unforgeable against adaptive chosen-message attacks. Most importantly, our scheme requires short group elements for aggregate signature and constant pairing computations for aggregate verification, which leads to high efficiency due to no relations with the number of signers.

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.

Online Social Networks (OSNs) have recently been playing an important role in communication. From the audience aspect, they enable audiences to get unlimited information via the information feeding mechanism (IFM), which is an important part of the OSNs. The audience relies on the quantity and quality of the information served by it. We found that existing IFMs can result in two problems: information overload and cultural ignorance. In this paper, we propose a new type of IFM that solves these problems. The advantage of our proposed IFM is that it can filter irrelevant information with consideration of audiences' culture by using the Naïve Bayes (NB) algorithm together with features and factors. It then dynamically serves interesting and important information based on the current situation and preference of the audience. This mechanism helps the audience to reduce the time spent in finding interesting information. It can be applied to other cultures, societies and businesses. In the near future, the audience will be provided with excellent, and less annoying, communication. Through our studies, we have found that our proposed IFM is most appropriate for Thai and some groups of Japanese audiences under the consideration of audiences' culture.

With the ever-growing prevalence of web 2.0, users can access information and resources easily and ubiquitously. It becomes increasingly important to understand the characteristics of user's complex behavior for efficient network management and security monitoring. In this paper, we develop a novel method to visualize and measure user's web-communication-behavior character in large-scale networks. First, we employ the active and passive monitoring methods to collect more than 20,000 IP addresses providing web services, which are divided into 12 types according to the content they provide, e.g. News, music, movie and etc, and then the IP address library is established with elements as (servicetype, IPaddress). User's behaviors are complex as they stay in multiple service types during any specific time period, we propose the behavior spectrum to model this kind of behavior characteristics in an easily understandable way. Secondly, two kinds of user's behavior characters are analyzed: the character at particular time instants and the dynamic changing characters among continuous time points. We then employ Renyi cross entropy to classify the users into different groups with the expectation that users in the same groups have similar behavior profiles. Finally, we demonstrated the application of behavior spectrum in profiling network traffic patterns and finding illegal users. The efficiency and correctness of the proposed methods are verified by the experimental results using the actual traffic traces collected from the Northwest Regional Center of China Education and Research Network (CERNET).

In sentiment classification, conventional supervised approaches heavily rely on a large amount of linguistic resources, which are costly to obtain for under-resourced languages. To overcome this scarce resource problem, there exist several methods that exploit graph-based semi-supervised learning (SSL). However, fundamental issues such as controlling label propagation, choosing the initial seeds, selecting edges have barely been studied. Our evaluation on three real datasets demonstrates that manipulating the label propagating behavior and choosing labeled seeds appropriately play a critical role in adopting graph-based SSL approaches for this task.

This paper proposes a radiation-hardened phase-locked loop (RH-PLL) with a switchable dual modular redundancy (DMR) structure. After radiation strikes, unhardened PLLs suffer clock perturbations. Conventional RH-PLLs have been proposed to reduce recovery time after perturbation. However, this recovery still requires tens of clock cycles. Our proposal involves ‘detecting’ and ‘switching’, rather than ‘recovering’ from clock perturbation. Detection speed is crucial for robust perturbation-immunity. We identify types of clock perturbation and then propose a set of detectors to detect each type. With this method, the detectors guarantee high-speed detection that leads to perturbation-immune switching from a radiated clock to an undistorted clock. The proposed RH-PLL was fabricated and then verified with a radiation test on real silicon.