The goal of software testing should go beyond simply finding defects. Ultimately, testing should be focused on increasing customer satisfaction. Defects that are detected in areas of the software that the customers are especially interested in can cause more customer dissatisfaction. If these defects accumulate, they can cause the software to be shunned in the marketplace. Therefore, it is important to focus on reducing defects in areas that customers consider valuable. This article proposes a value-driven V-model (V2 model) that deals with customer values and reflects them in the test design for increasing customer satisfaction and raising test efficiency.

A parallel Aho-Corasick (AC) approach, named PAC-k, is proposed for string matching in deep packet inspection (DPI). The proposed approach adopts graphic processing units (GPUs) to perform the string matching in parallel for high throughput. In parallel string matching, the boundary detection problem happens when a pattern is matched across chunks. The PAC-k approach solves the boundary detection problem because the number of characters to be scanned by a thread can reach the longest pattern length. An input string is divided into multiple sub-chunks with k characters. By adopting the new starting position in each sub-chunk for the failure transition, the required number of threads is reduced by a factor of k. Therefore, the overhead of terminating and reassigning threads is also decreased. In order to avoid the unnecessary overlapped scanning with multiple threads, a checking procedure is proposed that decides whether a new starting position is in the sub-chunk. In the experiments with target patterns from Snort and realistic input strings from DEFCON, throughputs are enhanced greatly compared to those of previous AC-based string matching approaches.

A fully synthesizable all-digital phase-locked loop (AD-PLL) with a stochastic time-to-digital converter (STDC) is proposed in this paper. The whole AD-PLL circuit design is based on only standard cells from digital library, thus the layout of this AD-PLL can be automatically synthesized by a commercial place-and-route (P&R) tool with a foundry-provided standard-cell library. No manual layout and process modification is required in the whole AD-PLL design. In order to solve the delay mismatch issue in the delay-line-based time-to-digital converter (TDC), an STDC employing only standard D flip-flop (DFF) is presented to mitigate the sensitivity to layout mismatch resulted from automatic P&R. For the stochastic TDC, the key idea is to utilize the layout uncertainty due to automatic P&R which follows Gaussian distribution according to statistics theory. Moreover, the fully synthesized STDC can achieve a finer resolution compared to the conventional TDC. Implemented in a 28nm fully depleted silicon on insulator (FDSOI) technology, the fully synthesized PLL consumes only 480µW under 1.0V power supply while operating at 0.9GHz. It achieves a figure of merit (FoM) of -231.1dB with 4.0ps RMS jitter while occupying 0.0055mm2 chip area only.

Modern optimizing compilers tend to be conservative and often fail to vectorize programs that would have benefited from it. In this paper, we propose a way to predict the relevant command-line options of the compiler so that it chooses the most profitable vectorization strategy. Machine learning has proven to be a relevant approach for this matter: fed with features that describe the software to the compiler, a machine learning device is trained to predict an appropriate optimization strategy. The related work relies on the control and data flow graphs as software features. In this article, we consider tensor contraction programs, useful in various scientific simulations, especially chemistry. Depending on how they access the memory, different tensor contraction kernels may yield very different performance figures. However, they exhibit identical control and data flow graphs, making them completely out of reach of the related work. In this paper, we propose an original set of software features that capture the important properties of the tensor contraction kernels. Considering the Intel Merom processor architecture with the Intel Compiler, we model the problem as a classification problem and we solve it using a support vector machine. Our technique predicts the best suited vectorization options of the compiler with a cross-validation accuracy of 93.4%, leading to up to a 3-times speedup compared to the default behavior of the Intel Compiler. This article ends with an original qualitative discussion on the performance of software metrics by means of visualization. All our measurements are made available for the sake of reproducibility.

Some diffusive and recurrence properties of Lorentz Lattice Gas Cellular Automata (LLGCA) have been expensively studied in terms of the densities of some of the left/right static/flipping mirrors/rotators. In this paper, for any combination S of these well known scatters, we study the computational complexity of the following problem which we call PERIODICITY on the S-model: given a finite configuration that distributes only those scatters in S, whether a particle visits the starting position periodically or not. Previously, the flipping mirror model and the occupied flipping rotator model have been shown unbounded, i.e. the process is always diffusive [17]. On the other hand, PERIODICITY is shown PSPACE-complete in the unoccupied flipping rotator model [21]. In this paper, we show that PERIODICITY is PSPACE-compete in any S-model that is neither occupied, unbounded, nor static. Particularly, we prove that PERIODICITY in any unoccupied and bounded model containing flipping mirror is PSPACE-complete.

This paper presents a novel data compression method for testing integrated circuits within the selective dictionary coding framework. Due to the inverse value of dictionary indices made use of for the compatibility analysis with the heuristic algorithm utilized to solve the maximum clique problem, the method can obtain a higher compression ratio than existing ones.

In this research, we have investigated the deposition condition of pentacene film on nitrogen doped (N-doped) LaB6 donor layer for larger grain growth at the channel region for bottom-contact type pentacene-based organic field-effect transistors (OFETs) to improve the device characteristics. Source and drain bottom-contacts of Al were patterned and 2nm-thick N-doped LaB6 donor layer was deposited on the SiO2/Si(100) back-gate structure. The dendritic grain growth of pentacene larger than 10µm without lamellar grain growth was demonstrated when the deposition temperature and rate were 100°C and 0.5nm/min, respectively. Furthermore, it was found that the dendritic grain growth was realized at the boundary region of bottom-contact as well as channel region.

Mixed-signal integrated circuit design and simulation highly rely on behavioral models of circuit blocks. Such models are used for the validation of design specification, optimization of system topology, and behavioral synthesis using a description language, etc. However, automatic behavioral model generation is still in its early stages; in most scenarios designers are responsible for creating behavioral models manually, which is time-consuming and error prone. In this paper an automatic behavioral model generation method for switched-capacitor (SC) integrator is proposed. This technique is based on symbolic circuit modeling with approximation, by which parametric behavioral integrator model can be generated. Such parametric models can be used in circuit design subject to severe process variational. It is demonstrated that the automatically generated integrator models can accurately capture process variation effects on arbitrarily selected circuit elements; furthermore, they can be applied to behavioral simulation of SC Sigma-Delta modulators (SDMs) with acceptable accuracy and speedup. The generated models are compared to a recently proposed manually generated behavioral integrator model in several simulation settings.

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

East Japan Railway Company has created new businesses such as life-style business and information technology business on the basis of railway business for sustainable growth. These businesses generate and provide synergy to one another effectively because each business is autonomous decentralized system based on diversified infrastructure. The infrastructure includes not just structure but management, technology, operation and maintenance: we call this “MTOMI Model.” The MTOMI Model is the key concept of JR East's businesses and can generate JR East's ecosystem.

We propose a novel multiple kernel learning (MKL) method using a collaborative representation constraint, called CR-MKL, for fusing the emotion information from multi-level features. To this end, the similarity and distinctiveness of multi-level features are learned in the kernels-induced space using the weighting distance measure. Our method achieves better performance than existing methods by using the voiced-level and unvoiced-level features.

We developed an electrically driven near-infrared broadband light source based on self-assembled InAs quantum dots (QDs). By combining emissions from four InAs QD ensembles with controlled emission center wavelengths, electro-luminescence (EL) with a Gaussian-like spectral shape and approximately 85-nm bandwidth was obtained. The peak wavelength of the EL was blue-shifted from approximately 1230 to 1200 nm with increased injection current density (J). This was due to the state-filling effect: sequential filling of the discrete QD electron/hole states by supplied carriers from lower (ground state; GS) to higher (excited state; ES) energy states. The EL intensities of the ES and GS emissions exhibited different J dependence, also because of the state-filling effect. The point-spread function (PSF) deduced from the Fourier-transformed EL spectrum exhibited a peak without apparent side lobes. The half width at half maximum of the PSF was 6.5 µm, which corresponds to the estimated axial resolution of the optical coherence tomography (OCT) image obtained with this light source. These results demonstrate the effectiveness of the QD-based device for realizing noise-reduced high-resolution OCT.

In this paper we present a novel treatment of cellular automata (CA) from an algebraic point of view. CA on monoids associated with Σ-algebras are introduced. Then an extension of Hedlund's theorem which connects CA associated with Σ-algebras and continuous functions between prodiscrete topological spaces on the set of configurations are discussed.

Analysis of security governed by dynamics of power systems, which we refer to as dynamic security analysis, is a primary but challenging task because of its hybrid nature, that is, nonlinear continuous-time dynamics integrated with discrete switchings. In this paper, we formulate this analysis problem as checking the reachability of a mathematical model representing dynamic performances of a target power system. We then propose a computational approach to the analysis based on the so-called RRT (Rapidly-exploring Random Tree) algorithm. This algorithm searches for a feasible trajectory connecting an initial state possibly at a lower security level and a target set with a desirable higher security level. One advantage of the proposed approach is that it derives a concrete control strategy to guarantee the desirable security level if the feasible trajectory is found. The performance and effectiveness of the proposed approach are demonstrated by applying it to two running examples on power system studies: single machine-infinite system and two-area system for frequency control problem.

Nonlinear precoding improves the downlink bit error rate (BER) performance of multi-user multiple-input multiple-output (MU-MIMO). Broadband single-carrier (SC) block transmission can improve the capability that nonlinear precoding reduces BER, as it provides frequency diversity gain. This paper considers Tomlinson-Harashima precoding (THP) as a nonlinear precoding scheme for SC-MU-MIMO downlink. In the SC-MU-MIMO downlink with frequency-domain THP proposed by Degen and Rrühl (called SC-FDTHP), the inter-symbol interference (ISI) is suppressed by transmit frequency-domain equalization (FDE) after suppressing the inter-user interference (IUI) by frequency-domain THP. Transmit FDE increases the signal variance, hence transmission performance improvement is limited. In this paper, we propose a new SC-MU-MIMO downlink with time-domain THP which can pre-remove both ISI and IUI (called SC-TDTHP) if perfect channel state information (CSI) is available. Modulo operation in THP suppresses the signal variance increase caused by ISI and IUI pre-removal, and hence the transmission quality improves. For further performance improvement, vector perturbation is introduced to SC-TDTHP (called SC-TDTHP w/VP). Computer simulation shows that SC-TDTHP achieves better BER performance than SC-FDTHP and that SC-TDTHP w/VP offers further improvement in BER performance over SC-MU-MIMO with VP (called SC-VP). Computational complexity is also compared and it is showed that SC-TDTHP and SC-TDTHP w/VP incur higher computational complexity than SC-FDTHP but lower than SC-VP.

Recently, wearable wireless devices or terminals have become hot a topic not only in research but also in business. Implantable wireless devices can temporarily be utilized to monitor a patient's condition in an emergency situation or to identify people in highly secured places. Unlike conventional wireless devices, wearable or implantable devices are used on or in the human body. In this sense, body-centric wireless communications (BCWCs) have become a very active area of research. Radio-frequency or microwave medical devices used for cancer treatment systems and surgical operation have completely different functions, but they are used on or in the human body. In terms of research techniques, such medical devices have a lot of similarities to BCWCs. The antennas to be used in the vicinity of the human body should be safe, small and robust. Also, their interaction with the human body should be well considered. This review paper describes some of the wearable antennas as well as implantable antennas that have been studied in our laboratory.

Many recent studies have focused on leveraging rich information types to increase useful information for improving fault localization effectiveness. However, they rarely investigate the impact of information richness on fault localization to give guidance on how to enrich information for improving localization effectiveness. This paper presents the first systematic study to fill this void. Our study chooses four representative information types and investigates the relationship between their richness and the localization effectiveness. The results show that information richness related to frequency execution count involves a high risk of degrading the localization effectiveness, and backward slice is effective in improving localization effectiveness.

The RCS of a radar target is an important factor related with the radar performance such as detection, tracking and classification. When dealing with the design of 26/79GHz automotive surveillance radar system, it is essential to know individual RCS of typical vehicles and pedestrian. However, there are few papers related to the RCS measurement at 26 and 79GHz. In this letter, the RCS measurements of typical vehicles and pedestrian were performed in a large-scale anechoic chamber room and the characteristics are discussed.

Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.

The problem of identifying moving objects in a video recording produced by a range sensor camera is due to the limited information available for classifying different objects. On the other hand, the infrared signal from a range sensor camera is more robust for extreme luminance intensity when the monitored area has light conditions that are too bright or too dark. This paper proposes a method of detection and tracking moving objects in image sequences captured by stationary range sensor cameras. Here, the depth information is utilized to correctly identify each of detected objects. Firstly, camera calibration and background subtraction are performed to separate the background from the moving objects. Next, a 2D projection mapping is performed to obtain the location and contour of the objects in the 2D plane. Based on this information, graph matching is performed based on features extracted from the 2D data, namely object position, size and the behavior of the objects. By observing the changes in the number of objects and the objects' position relative to each other, similarity matching is performed to track the objects in the temporal domain. Experimental results show that by using similarity matching, object identification can be correctly achieved even during occlusion.