In our previous work, we introduced new concepts of secure scan design; shift register equivalent circuits (SR-equivalents, for short) and strongly secure circuits, and also introduced generalized shift registers (GSRs, for short) to apply them to secure scan design. In this paper, we combine both concepts of SR-equivalents and strongly secure circuits and apply them to GSRs, and consider the synthesis problem of strongly secure SR-equivalents using GSRs. We also consider the enumeration problem of GSRs that are strongly secure and SR-equivalent, i.e., the cardinality of the class of strongly secure SR-equivalent GSRs to clarify the security level of the secure scan architecture.

A non-deterministic finite automaton (NFA)-based parallel string matching scheme is proposed. To parallelize the operations of NFAs, a graphic processing unit (GPU) is adopted. Considering the resource occupancy of threads and size of the shared memory, the optimized resource allocation is performed in the proposed string matching scheme. Therefore, the performance is enhanced significantly in all evaluations.

From past experience of the large-scale cutoff of existing networks as a result of the East Japan Great Earthquake and tsunamis, and from previous research on stabilizing ad hoc networks that lack control mechanisms, we have strengthened the resilience of NerveNet. NerveNet was originally designed and developed as an access network for providing context-aware services with the use of sensors and actuators. Thus, at present, it has the capability to enable resilient information sharing and communications in a region even if access to the Internet is impossible in emergency situations. NerveNet is composed of single or multiple base stations interconnected by a variety of Ethernet-based wired or wireless transmission systems. A network is formed using line, star, tree, or mesh topology. Network and data management works in each base station in a distributed manner, resulting in the resilience of this system. In collaboration with the town of Shirahama in Wakayama prefecture in Japan, we have been conducting a pilot test with the NerveNet testbed. The test includes nine base stations interconnected by 5.6-GHz Wi-Fi and Fixed Wireless Access (FWA), providing tourists and residents with Internet access. In the future, we expect that not only NerveNet but also other novel technologies will contribute to solving social problems and enriching people's lives.

In at-speed scan testing, capture power is a serious problem because the high power dissipation that can occur when the response for a test vector is captured by flip-flops results in excessive voltage drops, known as IR-drops, which may cause significant capture-induced yield loss. In low capture power test generation, the test vectors that violate capture power constraints in an initial test set are defined as capture-unsafe test vectors, while faults that are detected solely by capture-unsafe test vectors are defined as unsafe faults. It is necessary to regenerate the test vectors used to detect unsafe faults in order to prevent unnecessary yield losses. In this paper, we propose a new low capture power test generation method based on fault simulation that uses capture-safe test vectors in an initial test set. Experimental results show that the use of this method reduces the number of unsafe faults by 94% while requiring just 18% more additional test vectors on average, and while requiring less test generation time compared with the conventional low capture power test generation method.

This paper proposes a read-only memory driving circuit for RFID tags based on a-IGZO thin-film transistors. The circuit consists of a Johnson counter and monotype complementary gates. By utilizing complementary signals to drive a decoder based on monotype complementary gates, the propagation delay can be decreased and the redundant current can be reduced. The Johnson counter reduces the number of registers. The new circuit can effectively avoid glitch generation, and reduce circuit power consumption and delay.

The United Nations (UN) reports that the global population reached 7 billion in 2011, and today, it stands at about 7.3 billion. This dramatic increase has been driven largely by the extension of people's lifetime. The urban population has been also increasing, which causes a lot of issues for cities, such as congestion and increased demand for resources, including energy, water, sanitation, education, and healthcare services. A smart city has been expected a lot to solve those issues. The concept of a smart city is not new. Due to the progress of information and communication technology (ICT), including the Internet of Things (IoT) and big data (BD), the concept of a smart city has been being realized in various aspects. This paper introduces the concept and definition of a smart city. Then it explains the ambient intelligence that supports a smart city. Moreover, it introduces several key components of a smart city.

This paper considers the recovery problem of distributed compressed sensing (DCS), where J (J≥2) signals all have sparse common component and sparse innovation components. The decoder attempts to jointly recover each component based on {Mj} random noisy measurements (j=1,…,J) with the prior information on the support probabilities, i.e., the probabilities that the entries in each component are nonzero. We give both the sufficient and necessary conditions on the total number of measurements $sum olimits_{j = 1}^J M_j$ that is needed to recover the support set of each component perfectly. The results show that when the number of signal J increases, the required average number of measurements $sum olimits_{j = 1}^J M_j/J$ decreases. Furthermore, we propose an extension of one existing algorithm for DCS to exploit the prior information, and simulations verify its improved performance.

In this paper, we propose a novel design method of two channel critically sampled compactly supported biorthogonal graph wavelet filter banks with half-band kernels. First of all, we use the polynomial half-band kernels to construct a class of biorthogonal graph wavelet filter banks, which exactly satisfy the PR (perfect reconstruction) condition. We then present a design method of the polynomial half-band kernels with the specified degree of flatness. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which ensures that the half-band kernels have the specified zeros at λ=2. Therefore the constraints of flatness are satisfied at both of λ=0 and λ=2, and then the resulting graph wavelet filters have the flat spectral responses in passband and stopband. Furthermore, we apply the Remez exchange algorithm to minimize the spectral error of lowpass (highpass) filter in the band of interest by using the remaining degree of freedom. Finally, several examples are designed to demonstrate the effectiveness of the proposed design method.

We propose a novel method, built upon the hierarchical Dirichlet process hidden semi-Markov model, to reveal the content structures of unstructured domain-specific texts. The content structures of texts consisting of sequential local contexts are useful for tasks, such as text retrieval, classification, and text mining. The prominent feature of our model is the use of the recursive uniform partitioning, a stochastic process taking a view different from existing HSMMs in modeling state duration. We show that the recursive uniform partitioning plays an important role in avoiding the rapid switching between hidden states. Remarkably, our method greatly outperforms others in terms of ranking performance in our text retrieval experiments, and provides more accurate features for SVM to achieve higher F1 scores in our text classification experiments. These experiment results suggest that our method can yield improved representations of domain-specific texts. Furthermore, we present a method of automatically discovering the local contexts that serve to account for why a text is classified as a positive instance, in the supervised learning settings.

This paper presents a novel speaking aid system to help laryngectomees produce more naturally sounding electrolaryngeal (EL) speech. An electrolarynx is an external device to generate excitation signals, instead of vibration of the vocal folds. Although the conventional EL speech is quite intelligible, its naturalness suffers from the unnatural fundamental frequency (F0) patterns of the mechanically generated excitation signals. To improve the naturalness of EL speech, we have proposed EL speech enhancement methods using statistical F0 pattern prediction. In these methods, the original EL speech recorded by a microphone is presented from a loudspeaker after performing the speech enhancement. These methods are effective for some situation, such as telecommunication, but it is not suitable for face-to-face conversation because not only the enhanced EL speech but also the original EL speech is presented to listeners. In this paper, to develop an EL speech enhancement also effective for face-to-face conversation, we propose a method for directly controlling F0 patterns of the excitation signals to be generated from the electrolarynx using the statistical F0 prediction. To get an "actual feel” of the proposed system, we also implement a prototype system. By using the prototype system, we find latency issues caused by a real-time processing. To address these latency issues, we furthermore propose segmental continuous F0 pattern modeling and forthcoming F0 pattern modeling. With evaluations through simulation, we demonstrate that our proposed system is capable of effectively addressing the issues of latency and those of electrolarynx in term of the naturalness.

Since the sensor nodes are subject to faults due to the highly-constrained resources and hostile deployment environments, fault management in wireless sensor networks (WSNs) is essential to guarantee the proper operation of networks, especially routing. In contrast to existing fault management methods which mainly aim to be tolerant to faults without considering the fault type, we propose a novel efficient fault-aware routing method where faults are classified and dealt with accordingly. More specifically, we first identify each fault and then try to set up the new routing path according to the fault type. Our proposed method can be easily integrated with any kind of existing routing method. We show that our proposed method outperforms AODV, REAR, and GPSR, which are the representative works of single-path routing, multipath routing and location based routing, in terms of energy efficiency and data delivery ratio.

Spectral compressive sensing is a novel approach that enables extraction of spectral information from a spectral-sparse signal, exclusively from its compressed measurements. Thus, the approach has received considerable attention from various fields. However, standard compressive sensing algorithms always require a sparse signal to be on the grid, whose spacing is the standard resolution limit. Thus, these algorithms severely degenerate while handling spectral compressive sensing, owing to the off-the-grid issue. Some off-the-grid algorithms were recently proposed to solve this problem, but they are either inaccurate or computationally expensive. In this paper, we propose a novel algorithm named parameterized ℓ1-minimization (PL1), which can efficiently solves the off-the-grid spectral estimation problem with relatively low computational complexity.

For subgraph enumeration problems, very efficient algorithms have been proposed whose time complexities are far smaller than the number of subgraphs. Although the number of subgraphs can exponentially increase with the input graph size, these algorithms exploit compressed representations to output and maintain enumerated subgraphs compactly so as to reduce the time and space complexities. However, they are designed for enumerating only some specific types of subgraphs, e.g., paths or trees. In this paper, we propose an algorithm framework, called the frontier-based search, which generalizes these specific algorithms without losing their efficiency. Our frontier-based search will be used to resolve various practical problems that include constrained subgraph enumeration.

The Euler number of a binary image is an important topological property for pattern recognition, image analysis, and computer vision. A famous method for computing the Euler number of a binary image is by counting certain patterns of bit-quads in the image, which has been improved by scanning three rows once to process two bit-quads simultaneously. This paper studies the bit-quad-based Euler number computing problem. We show that for a bit-quad-based Euler number computing algorithm, with the increase of the number of bit-quads being processed simultaneously, on the one hand, the average number of pixels to be checked for processing a bit-quad will decrease in theory, and on the other hand, the length of the codes for implementing the algorithm will increase, which will make the algorithm less efficient in practice. Experimental results on various types of images demonstrated that scanning five rows once and processing four bit-quads simultaneously is the optimal tradeoff, and that the optimal bit-quad-based Euler number computing algorithm is more efficient than other Euler number computing algorithms.

Train-borne video captured from the camera installed in the front or back of the train has been used for railway environment surveillance, including missing communication units and bolts on the track, broken fences, unpredictable objects falling into the rail area or hanging on wires on the top of rails. Moreover, the track condition can be perceived visually from the video by observing and analyzing the train-swaying arising from the track irregularity. However, it's a time-consuming and labor-intensive work to examine the whole large scale video up to dozens of hours frequently. In this paper, we propose a simple and effective method to detect the train-swaying quickly and automatically. We first generate the long rail track panorama (RTP) by stitching the stripes cut from the video frames, and then extract track profile to perform the unevenness detection algorithm on the RTP. The experimental results show that RTP, the compact video representation, can fast examine the visual train-swaying information for track condition perceiving, on which we detect the irregular spots with 92.86% recall and 82.98% precision in only 2 minutes computation from the video close to 1 hour.

While conventional studies on real-time systems have mostly considered the real-time constraint of real-time systems only, recent research initiatives are trying to incorporate a security constraint into real-time scheduling due to the recognition that the violation of either of two constrains can cause catastrophic losses for humans, the system, and even environment. The focus of most studies, however, is the single-criticality systems, while the security of mixed-criticality systems has received scant attention, even though security is also a critical issue for the design of mixed-criticality systems. In this paper, we address the problem of the information leakage that arises from the shared resources that are used by tasks with different security-levels of mixed-criticality systems. We define a new concept of the security constraint employing a pre-flushing mechanism to cleanse the state of shared resources whenever there is a possibility of the information leakage regarding it. Then, we propose a new non-preemptive real-time scheduling algorithm and a schedulability analysis, which incorporate the security constraint for mixed-criticality systems. Our evaluation demonstrated that a large number of real-time tasks can be scheduled without a significant performance loss under a new security constraint.

Telecommunication networks have evolved from telephony networks to the Internet, and they sustainably support the development of a secured, safe, and comfortable society. The so-called “switching technology” including the evolved “network system technology” is one of the main infrastructure technologies used for realizing information communication services. On the occasion of completion of 100 years since the establishment of the IEICE, we summarize the history of network system technologies and present their future direction for the next generation. We mainly focus on a series of technologies that evolved through the discussions of the IEICE technical committees on switching engineering, launched 50 years ago, switching systems engineering, and network systems in action.

In current video encoding systems, the acquisition process is independent from the video encoding process. In order to compensate for the independence, pre-filters prior to the encoder are used. However, conventional pre-filters are designed under constraints on the temporal resolution, so they are not optimized enough in terms of coding efficiency. By relaxing the restriction on the temporal resolution of current video encoding systems, there is a good possibility to generate a video signal suitable for the video encoding process. This paper proposes a video generation method with an adaptive temporal filter that utilizes a temporally over-sampled signal. The filter is designed based on dynamic-programming. Experimental results show that the proposed method can reduce encoding rate on average by 3.01 [%] compared to the constant mean filter.

Recent research on the assessment of low probability of interception (LPI) radar waveforms is mainly based on limiting spectral properties of Wigner matrices. As the dimension of actual operating data is constrained by the sampling frequency, it is very urgent and necessary to research the finite theory of Wigner matrices. This paper derives a closed-form expression of the spectral cumulative distribution function (CDF) for Wigner matrices of finite sizes. The expression does not involve any derivatives and integrals, and therefore can be easily computed. Then we apply it to quantifying the LPI performance of radar waveforms, and the Kullback-Leibler divergence (KLD) is also used in the process of quantification. Simulation results show that the proposed LPI metric which considers the finite sample size and signal-to-noise ratio is more effective and practical.

In this paper, we propose a method for removing block noise which appears in JPEG (Joint Photographic Experts Group) encoded images. We iteratively perform the 3D wiener filtering and correction of the coefficients. In the wiener filtering, we perform the block matching for each patch in order to get the patches which have high similarities to the reference patch. After wiener filtering, the collected patches are returned to the places where they were and aggregated. We compare the performance of the proposed method to some conventional methods, and show that the proposed method has an excellent performance.