We have developed a capacitance sensor of frequency modulation for integrated touchpanels using amorphous In-Sn-Zn-O (α-ITZO) thin-film transistors (TFTs). This capacitance sensor consists of a ring oscillator, whose one stage is replaced by a reset transistor, sensing transistor, and sensing electrode. The sensing electrode is prepared as one terminal to form a sensing capacitor when the other terminal is added by a finger. The ring oscillator consists of pseudo CMOS inverters. We confirm that the oscillation frequency changes when the other terminal is added. This result suggests that this capacitance sensor can be applied to integrated touchpanels on flatpanel displays.

We investigated a control of the crystalline orientation of soluble organic semiconductor single crystals using liquid crystal solvents aligned by the electric field to improve the performance of organic thin-film transistors. We clarified that the semiconductor single crystal grows to the direction parallel to the liquid crystal alignment oriented by the lateral electric field.

To develop a flexible liquid crystal display (LCD) with a wide viewing angle range and high contrast ratio, we have proposed a flexible blue-phase LC device sustained by polymer walls inside the LC cell. We clarified that the polymer walls can maintain a constant cell gap and suppress the generation of alignment defects of the blue-phase LC in a bending state.

In many multimedia applications, image encryption has to be conducted prior to image compression. This paper proposes a JPEG-friendly perceptual encryption method, which enables to be conducted prior to JPEG and Motion JPEG compressions. The proposed encryption scheme can provides approximately the same compression performance as that of JPEG compression without any encryption, where both gray scale images and color ones are considered. It is also shown that the proposed scheme consists of four block-based encryption steps, and provide a reasonably high level of security. Most of conventional perceptual encryption schemes have not been designed for international compression standards, but this paper focuses on applying the JPEG and Motion JPEG standards, as one of the most widely used image compression standards. In addition, this paper considers an efficient key management scheme, which enables an encryption with multiple keys to be easy to manage its keys.

This paper proposes a network clock system that detects degradation in the frequency accuracy of network clocks distributed across a network and finds the sources of the degradation. This system uses two factors to identify degradation in frequency accuracy and an algorithm that finds degradation sources by integrating and analyzing the evaluation results gathered from the entire network. Many frequency stability measurement systems have been proposed, and most are based on time synchronization protocols. These systems also realize avoidance of frequency degradation and identification of the sources of the degradation. Unfortunately, the use of time synchronization protocols is impractical if the service provider, such as NTT, has already installed a frequency synchronization system; the provider must replace massive amounts of equipment with new devices that support the time synchronization protocols. Considering the expenditure of installment, this is an excessive burden on service providers. Therefore, a new system that can detect of frequency degradation in network clocks and identify the degradation causes without requiring new equipment is strongly demanded. The proposals made here are implemented by the installation of new circuit cards in current equipment and installing a server that runs the algorithm. This proposed system is currently being installed in NTT's network.

For the last few decades, learning with multiple kernels, represented by the ensemble kernel regressor and the multiple kernel regressor, has attracted much attention in the field of kernel-based machine learning. Although their efficacy was investigated numerically in many works, their theoretical ground is not investigated sufficiently, since we do not have a theoretical framework to evaluate them. In this paper, we introduce a unified framework for evaluating kernel regressors with multiple kernels. On the basis of the framework, we analyze the generalization errors of the ensemble kernel regressor and the multiple kernel regressor, and give a sufficient condition for the ensemble kernel regressor to outperform the multiple kernel regressor in terms of the generalization error in noise-free case. We also show that each kernel regressor can be better than the other without the sufficient condition by giving examples, which supports the importance of the sufficient condition.

We have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate between left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the single-trial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant difference between the two methods. Our BNM might reflect each subject's strategy for task execution.

Multi-tenant datacenter networking, with which multiple customer networks (tenants) are virtualized and consolidated in a single shared physical infrastructure, has recently become a promising approach to reduce device cost, thanks to advances of virtualization technologies for various networking devices (e.g., switches, firewalls, load balancers). Since network devices are configured with low-level commands (no context of tenants), network engineers need to manually manage the context of tenants in different stores such as spreadsheet and/or configuration management database (CMDB). The use of CMDB is also effective in increasing the ‘visibility’ of tenant configurations (e.g., information sharing among various teams); However, different from the ideal use, only limited portion of network configuration are stored in CMDB in order to reduce the amount of ‘double configuration management’ between device settings (running information) and CMDB (stored information). In this present work, we aim to bridge the gap between CDMB and device status. Our basic approach is to automatically analyze per-device configuration settings to recover per-tenant network-wide configuration (running information) based on a graph-traversal technique applied over abstracted graph representation of device settings (to handle various types of vendor-specific devices); The recovered running information of per-tenant network configurations is automatically uploaded to CMDB. An implementation of this methodology is applied to a datacenter environment that management of about 100 tenants involves approximately 5,000 CMDB records, and our practical experiences are that this methodology enables to double the amount of CMDB records. We also discuss possible use cases enabled with this methodology.

The hop-limited adaptive routing (HLAR) mechanism and its enhancement (EHLAR), both tailored for the packet-switched non-geostationary (NGEO) satellite networks, are proposed and evaluated. The proposed routing mechanisms exploit both the predictable topology and inherent multi-path property of the NGEO satellite networks to adaptively distribute the traffic via all feasible neighboring satellites. Specifically, both mechanisms assume that a satellite can send the packets to their destinations via any feasible neighboring satellites, thus the link via the neighboring satellite to the destination satellite is assigned a probability that is proportional to the effective transmission to the destination satellites of the link. The satellite adjusts the link probability based on the packet sending information observed locally for the HLAR mechanism or exchanged between neighboring satellites for the EHLAR mechanism. Besides, the path of the packets are bounded by the maximum hop number, thus avoiding the unnecessary over-detoured packets in the satellite networks. The simulation results corroborate the improved performance of the proposed mechanisms compared with the existing in the literature.

The pilot contamination is a serious problem which hinders the capacity increasing in the massive MIMO system. Similar to Fractional Frequency Reuse (FFR) in the OFDMA system, Fractional Pilot Reuse (FPR) is proposed for the massive MIMO system. The FPR can be further classified as the strict FPR and soft FPR. Meanwhile, the detailed FPR schemes with pilot assignment and the mathematical models are provided. With FPR, the capacity and the transmission quality can be improved with metrics such as the higher Signal to Interference and Noise Ratio (SINR) of the pilots, the higher coverage probability, and the higher system capacity.

An effective time-step control method is proposed for the damped pseudo-transient analysis (DPTA). This method is based on the idea of the switched evolution/relaxation method which can automatically adapt the step size for different circuit states. Considering the number of iterations needed for the convergence of the Newton-Raphson method, the new method adapts the suitable time-step size with the status of previous steps. By numerical examples, it is proved that this method can improve the simulation efficiency and convergence for the DPTA method to solve nonlinear DC circuits.

In recent years, many variants of key point based image descriptors have been designed for the image matching, and they have achieved remarkable performances. However, to some images, local features appear to be inapplicable. Since theses images usually have many local changes around key points compared with a normal image, we define this special image category as the image with local changes (IL). An IL pair (ILP) refers to an image pair which contains a normal image and its IL. ILP usually loses local visual similarities between two images while still holding global visual similarity. When an IL is given as a query image, the purpose of this work is to match the corresponding ILP in a large scale image set. As a solution, we use a compressed HOG feature descriptor to extract global visual similarity. For the nearest neighbor search problem, we propose random projection indexed KD-tree forests (rKDFs) to match ILP efficiently instead of exhaustive linear search. rKDFs is built with large scale low-dimensional KD-trees. Each KD-tree is built in a random projection indexed subspace and contributes to the final result equally through a voting mechanism. We evaluated our method by a benchmark which contains 35,000 candidate images and 5,000 query images. The results show that our method is efficient for solving local-changes invariant image matching problems.

Multi-layer transport networks that utilize sub-lambda paths over a wavelength path have been shown to be effective in accommodating traffic with various levels of granularity. For different service requirements, a virtualized network was proposed where the infrastructure is virtually sliced to accommodate different levels of reliability. On the other hand, network reconfiguration is a promising candidate for quasi-dynamic and multi-granular traffic. Reconfiguration, however, incurs some risks such as service disruption and fluctuations in delay. There has not yet been any study on accommodating and reconfiguring paths according to different service classes in multi-layer transport networks. In this paper, we propose differentiated reconfiguration to address the trade-off relationship between accommodation efficiency and disruption risk in virtualized multi-layer transport networks that considers service classes defined as a combination of including or excluding a secondary path and allowing or not allowing reconfiguration. To implement the proposed network, we propose a multi-layer redundant path accommodation design and reconfiguration algorithm. A reliability evaluation algorithm is also introduced. Numerical evaluations show that when all classes are divided equally, equipment cost can be reduced approximately by 6%. The proposed reconfigurable networks are shown to be a cost effective solution that maintains reliability.

Isosurface extraction is one of the most popular techniques for visualizing scalar volume data. However, volume data contains infinitely many isosurfaces. Furthermore, a single isosurface might contain many connected components, or contours, with each representing a different object surface. Hence, it is often a tedious and time-consuming manual process to find and extract contours that are interesting to users. This paper describes a novel method for automatically extracting salient contours from volume data. For this purpose, we propose a contour gradient tree (CGT) that contains the information of salient contours and their saliency magnitude. We organize the CGT in a hierarchical way to generate a sequence of contours in saliency order. Our method was applied to various medical datasets. Experimental results show that our method can automatically extract salient contours that represent regions of interest in the data.

Most of the current voice conversion methods are conducted based on parallel speech, which is not easily obtained in practice. In this letter, a novel iterative speaker model alignment (ISMA) method is proposed to address this problem. First, the source and target speaker models are each trained from the background model by adopting maximum a posteriori (MAP) algorithm. Then, a novel ISMA method is presented for alignment and transformation of spectral features. Finally, the proposed ISMA approach is further combined with a Gaussian mixture model (GMM) to improve the conversion performance. A series of objective and subjective experiments are carried out on CMU ARCTIC dataset, and the results demonstrate that the proposed method significantly outperforms the state-of-the-art approach.

Routing is a challenging issue in mobile social networks (MSNs) because of time-varying links and intermittent connectivity. In order to enable nodes to make right decisions while forwarding messages, exploiting social relationship has become an important method for designing efficient routing protocols in MSNs. In this paper, we first use the temporal evolution graph model to accurately capture the dynamic topology of the MSN. Based on the model, we introduce the social relationship metric for detecting the quality of human social relationship from contact history records. Utilizing this metric, we propose social relationship based betweenness centrality metric to identify influential nodes to ensure messages forwarded by the nodes with stronger social relationship and higher likelihood of contacting other nodes. Then, we present SRBet, a novel social-based forwarding algorithm, which utilizes the aforementioned metric to enhance routing performance. Simulations have been conducted on two real world data sets and results demonstrate that the proposed forwarding algorithm achieves better performances than the existing algorithms.

By investigating the properties that the offsets should satisfy, this letter presents a brief proof of general QAM Golay complementary sequences (GCSs) in Cases I-III constructions. Our aim is to provide a brief, clear, and intelligible derivation so that it is easy for the reader to understand the known Cases I-III constructions of general QAM GCSs.

Neural networks are widely used in various fields due to their superior learning abilities. This paper proposes a hardware winner-take-all neural network (WTANN) that employs a new winner-take-all (WTA) circuit with phase-modulated pulse signals and digital phase-locked loops (DPLLs). The system uses DPLL as a computing element, so all input values are expressed by phases of rectangular signals. The proposed WTA circuit employs a simple winner search circuit. The proposed WTANN architecture is described by very high speed integrated circuit (VHSIC) hardware description language (VHDL), and its feasibility was tested and verified through simulations and experiments. Conventional WTA takes a global winner search approach, in which vector distances are collected from all neurons and compared. In contrast, the WTA in the proposed system is carried out locally by a distributed winner search circuit among neurons. Therefore, no global communication channels with a wide bandwidth between the winner search module and each neuron are required. Furthermore, the proposed WTANN can easily extend the system scale, merely by increasing the number of neurons. The circuit size and speed were then evaluated by applying the VHDL description to a logic synthesis tool and experiments using a field programmable gate array (FPGA). Vector classifications with WTANN using two kinds of data sets, Iris and Wine, were carried out in VHDL simulations. The results revealed that the proposed WTANN achieved valid learning.

A highly efficient sky-blue organic light-emitting diode (OLED) based on a thermally-activated delayed fluorescence (TADF) molecule, 1,2-bis(carbazol-9-yl)-4,5-dicyanobenzene (2CzPN), was studied. The sky-blue OLED exhibited a maximum external electroluminescence quantum efficiency (ηEQE) of over 24.0%. In addition, a white OLED using 2CzPN combined with green and orange TADF emitters showed a high ηEQE of 17.3% with a maximum power efficiency of 52.3 lm/W and Commission Internationale de l'Eclairage coordinates of (0.32, 0.43).

This paper considers how to evaluate the resiliency for virtualized system with software rejuvenation. The software rejuvenation is a proactive technique to prevent the failure caused by aging phenomenon such as resource exhaustion. In particular, according to Gohsh et al. (2010), we compute a quantitative criterion to evaluate resiliency of system by using continuous-time Markov chains (CTMC). In addition, in order to convert general state-based models to CTMCs, we employ PH (phase-type) expansion technique. In numerical examples, we investigate the resiliency of virtualized system with software rejuvenation under two different rejuvenation policies.