We developed an 8.4-inch electrostatic-tactile touch display using a segmented-electrode array (30×20) as both tactile pixels and touch sensors. Each pixel can be excited independently so that the electrostatic-tactile touch display allows presenting real localized tactile textures in any shape. A driving scheme in which the tactile strength is independent of the grounding state of the human body by employing two-phased actuation was also proposed and demonstrated. Furthermore, tactile crosstalk was investigated to find it was due to the voltage fluctuation in the human body and it was diminished by applying the aforementioned driving scheme.

In this letter, we consider the incorrigible sets of binary linear codes. First, we show that the incorrigible set enumerator of a binary linear code is tantamount to the Tutte polynomial of the vector matroid induced by the parity-check matrix of the code. A direct consequence is that determining the incorrigible set enumerator of binary linear codes is #P-hard. Then for a cycle code, we express its incorrigible set enumerator via the Tutte polynomial of the graph describing the code. Furthermore, we provide the explicit formula of incorrigible set enumerators of cycle codes constructed from complete graphs.

This paper describes a method to control prosodic features using phonetic and prosodic symbols as input of attention-based sequence-to-sequence (seq2seq) acoustic modeling (AM) for neural text-to-speech (TTS). The method involves inserting a sequence of prosodic symbols between phonetic symbols that are then used to reproduce prosodic acoustic features, i.e. accents, pauses, accent breaks, and sentence endings, in several seq2seq AM methods. The proposed phonetic and prosodic labels have simple descriptions and a low production cost. By contrast, the labels of conventional statistical parametric speech synthesis methods are complicated, and the cost of time alignments such as aligning the boundaries of phonemes is high. The proposed method does not need the boundary positions of phonemes. We propose an automatic conversion method for conventional labels and show how to automatically reproduce pitch accents and phonemes. The results of objective and subjective evaluations show the effectiveness of our method.

3D video contents depend on the shooting condition, which is camera positioning. Depth range control in the post-processing stage is not easy, but essential as the video from arbitrary camera positions must be generated. If light field information can be obtained, video from any viewpoint can be generated exactly and post-processing is possible. However, a light field has a huge amount of data, and capturing a light field is not easy. To compress data quantity, we proposed the visually equivalent light field (VELF), which uses the characteristics of human vision. Though a number of cameras are needed, VELF can be captured by a camera array. Since camera interpolation is made using linear blending, calculation is so simple that we can construct a ray distribution field of VELF by optical interpolation in the VELF3D display. It produces high image quality due to its high pixel usage efficiency. In this paper, we summarize the relationship between the characteristics of human vision, VELF and VELF3D display. We then propose a method to control the depth range for the observed image on the VELF3D display and discuss the effectiveness and limitations of displaying the processed image on the VELF3D display. Our method can be applied to other 3D displays. Since the calculation is just weighted averaging, it is suitable for real-time applications.

The Benchmarking Working Group of IETF has defined a benchmarking methodology for IPv6 transition technologies including stateless NAT64 (also called SIIT) in RFC 8219. The aim of our effort is to design and implement a test program for SIIT gateways, which complies with RFC 8219, and thus to create the world's first standard free software SIIT benchmarking tool. In this paper, we overview the requirements for the tester on the basis of RFC 8219, and make scope decisions: throughput, frame loss rate, latency and packet delay variation (PDV) tests are implemented. We fully disclose our design considerations and the most important implementation decisions. Our tester, siitperf, is written in C++ and it uses the Intel Data Plane Development Kit (DPDK). We also document its functional tests and its initial performance estimation. Our tester is distributed as free software under GPLv3 license for the benefit of the research, benchmarking and networking communities.

The performance of packet processing applications is dependent on the memory access speed of network systems. Table lookup requires fast memory access and is one of the most common processes in various packet processing applications, which can be a dominant performance bottleneck. Therefore, in Network Function Virtualization (NFV)-aware environments, on-chip fast cache memories of a CPU of general-purpose hardware become critical to achieve high performance packet processing speeds of over tens of Gbps. Also, multiple types of applications and complex applications are executed in the same system simultaneously in carrier network systems, which require adequate cache memory capacities as well. In this paper, we propose a packet processing architecture that utilizes interleaved 3 Dimensional (3D)-stacked Dynamic Random Access Memory (DRAM) devices as off-chip Last Level Cache (LLC) in addition to several levels of dedicated cache memories of each CPU core. Entries of a lookup table are distributed in every bank and vault to utilize both bank interleaving and vault-level memory parallelism. Frequently accessed entries in 3D-stacked DRAM are also cached in on-chip dedicated cache memories of each CPU core. The evaluation results show that the proposed architecture reduces the memory access latency by 57%, and increases the throughput by 100% while reducing the blocking probability but about 10% compared to the architecture with shared on-chip LLC. These results indicate that 3D-stacked DRAM can be practical as off-chip LLC in parallel packet processing systems.

Inspired by an idea due to Levenshtein, we apply the low correlation zone constraint in the analysis of the weighted mean square aperiodic correlation. Then we derive a lower bound on the measure for quasi-complementary sequence sets with low correlation zone (LCZ-QCSS). We discuss the conditions of tightness for the proposed bound. It turns out that the proposed bound is tighter than Liu-Guan-Ng-Chen bound for LCZ-QCSS. We also derive a lower bound for QCSS, which improves the Liu-Guan-Mow bound in general.

Identifying the infection sources in a network, including the sponsor of a network rumor, the servers that inject computer virus into a computer network, or the zero-patient in an infectious disease network, plays a critical role in limiting the damage caused by the infection. A two-source estimator is firstly constructed on basis of partitions of infection regions in this paper. Meanwhile, the two-source estimation problem is transformed into calculating the expectation of permitted permutations count which can be simplified to a single-source estimation problem under determined infection region. A heuristic algorithm is also proposed to promote the estimator to general graphs in a Breadth-First-Search (BFS) fashion. Experimental results are provided to verify the performance of our method and illustrate variations of error detection in different networks.

In this study we propose a method to perform personal identification (PI) based on Electroencephalogram (EEG) signals, where the used network is named residual and multiscale spatio-temporal convolution neural network (RAMST-CNN). Combined with some popular techniques in deep learning, including residual learning (RL), multi-scale grouping convolution (MGC), global average pooling (GAP) and batch normalization (BN), RAMST-CNN has powerful spatio-temporal feature extraction ability as it achieves task-independence that avoids the complexity of selecting and extracting features manually. Experiments were carried out on multiple datasets, the results of which were compared with methods from other studies. The results show that the proposed method has a higher recognition accuracy even though the network it is based on is lightweight.

The partial-period autocorrelation of sequences is an important performance measure of communication systems employing them, but it is notoriously difficult to be analyzed. In this paper, we propose an algorithm to design unimodular sequences with low partial-period autocorrelations via directly minimizing the partial-period integrated sidelobe level (PISL). The proposed algorithm is inspired by the monotonic minimizer for integrated sidelobe level (MISL) algorithm. Then an acceleration scheme is considered to further accelerate the algorithms. Numerical experiments show that the proposed algorithm can effectively generate sequences with lower partial-period peak sidelobe level (PPSL) compared with the well-known Zadoff-Chu sequences.

We consider all-to-all broadcast and unicast among nodes in a multi-channel single-hop ad hoc network, with no time synchronization. Motivated by the hard delay requirement for ultra-reliable and low-latency communication (URLLC) in 5G wireless networks, we aim at designing medium access control (MAC) schemes to guarantee successful node-to-node transmission within a bounded delay. To provide a hard guarantee on the transmission delay, deterministic sequence schemes are preferred to probabilistic schemes such as carrier sense multiple access (CSMA). Therefore, we mainly consider sequence schemes, with the goal to design schedule sequence set to guarantee successful broadcast/unicast within a common sequence period. This period should be as short as possible since it determines an upper bound on the transmission delay. In previous works, we have considered sequence design under time division duplex (TDD). In this paper, we focus on another common duplex mode, frequency division duplex (FDD). For the FDD case, we present a lower bound on period of feasible sequence sets, and propose a sequence construction method by which the sequence period can achieve the same order as the lower bound, for both broadcast and unicast models. We also compare the sequence length for FDD with that for TDD.

In this paper, we build a microscopic simulator of traffic flow in a three-modal transport society for pedestrians/slow vehicles/vehicles (P/SV/V) to evaluate a post P/V society. The simulator assumes that the SV includes bicycles and micro electric vehicles, whose speed is strictly and mechanically limited up to 30 km/h. In addition, this simulator adopts an SV overtaking model. Modal shifts caused by modal diversity requires new valuation indexes. The simulator has a significant feature of a traveler-based traffic demand simulation not a vehicle-based traffic demand simulation as well as new evaluation indexes. New assessment taking this situation into account is conducted and the results explain new aspects of traffic flow in a three-mode transport society.

Blockchain is a distributed ledger technology for recording transactions. When two or more miners create different versions of the blocks at almost the same time, blockchain forks occur. We model the mining process with forks by a discrete event system and design a supervisor controlling these forks.

Although transmittance changes like a quadratic function due to the DC offset voltage in FFS mode LCD, its bottom position and flicker minimum DC offset voltage varies depending on the gray level due to the flexoelectric effect. We demonstrated how the influence of the flexoelectric effect changes depending on the electrode width or black matrix position.

Impulsive noise (IN) is the most dominant factor degrading the performance of communication systems over powerlines. In order to improve performance of high-speed power line communication (PLC), this work focuses on mitigating burst IN effects based on compressive sensing (CS), and an adaptive burst IN mitigation method, namely combination of adaptive interleaver and permutation of null carriers is designed. First, the long burst IN is dispersed by an interleaver at the receiver and the characteristic of noise is estimated by the method of moment estimation, finally, the generated sparse noise is reconstructed by changing the number of null carriers(NNC) adaptively according to noise environment. In our simulations, the results show that the proposed IN mitigation technique is simple and effective for mitigating burst IN in PLC system, it shows the advantages to reduce the burst IN and to improve the overall system throughput. In addition, the performance of the proposed technique outpeformences other known nonlinear noise mitigation methods and CS methods.

The recent development of neural architecture search (NAS) has enabled us to automatically discover architectures of neural networks with high performance within a few days. Convolutional neural networks extract fruitful features by repeatedly applying standard operations (convolutions and poolings). However, these operations also extract useless or even disturbing features. Attention mechanisms enable neural networks to discard information of no interest, having achieved the state-of-the-art performance. While a variety of attentions for CNNs have been proposed, current NAS methods have paid a little attention to them. In this study, we propose a novel NAS method that searches attentions as well as operations. We examined several patterns to arrange attentions and operations, and found that attentions work better when they have their own search space and follow operations. We demonstrate the superior performance of our method in experiments on CIFAR-10, CIFAR-100, and ImageNet datasets. The found architecture achieved lower classification error rates and required fewer parameters compared to those found by current NAS methods.

Recently, adaptive streaming over information centric network (ICN) has attracted attention. In adaptive streaming over ICN, the bitrate adaptation of the client often overestimates a bitrate for available bandwidth due to congestion because the client implicitly estimates congestion status from the content download procedures of ICN. As a result, streaming overestimated bitrate results in QoE degradation of clients such as cause of a stall time and frequent variation of the bitrate. In this paper, we propose a congestion-aware adaptive streaming over ICN combined with the explicit congestion notification (CAAS with ECN) to avoid QoE degradation. CAAS with ECN encourages explicit feedback of congestion detected in the router on the communication path, and introduces the upper band of the selectable bitrate (bitrate-cap) based on explicit feedback from the router to the bitrate adaptation of the clients. We evaluate the effectiveness of CAAS with ECN for client's QoE degradation due to congestion and behavior on the QoS metrics based on throughput. The simulation experiments show that the bitrate adjustment for all the clients improves QoE degradation and QoE fairness due to effective congestion avoidance.

Shape matching with local descriptors is an underlying scheme in shape analysis. We can visually confirm the matching results and also assess them for shape classification. Generally, shape matching is implemented by determining the correspondence between shapes that are represented by their respective sets of sampled points. Some matching methods have already been proposed; the main difference between them lies in their choice of matching cost function. This function measures the dissimilarity between the local distribution of sampled points around a focusing point of one shape and the local distribution of sampled points around a referring point of another shape. A local descriptor is used to describe the distribution of sampled points around the point of the shape. In this paper, we propose an extended scheme for shape matching that can compensate for errors in existing local descriptors. It is convenient for local descriptors to adopt our scheme because it does not require the local descriptors to be modified. The main idea of our scheme is to consider the correspondence of neighboring sampled points to a focusing point when determining the correspondence of the focusing point. This is useful because it increases the chance of finding a suitable correspondence. However, considering the correspondence of neighboring points causes a problem regarding computational feasibility, because there is a substantial increase in the number of possible correspondences that need to be considered in shape matching. We solve this problem using a branch-and-bound algorithm, for efficient approximation. Using several shape datasets, we demonstrate that our scheme yields a more suitable matching than the conventional scheme that does not consider the correspondence of neighboring sampled points, even though our scheme requires only a small increase in execution time.

In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.