The spectral efficiency of photonic networks can be enhanced by the use of higher modulation orders and narrower channel bandwidth. Unfortunately, these solutions are precluded by the margins required to offset uncertainties in system performance. Furthermore, as recently highlighted, the disaggregation of optical transport systems increases the required margin. We propose here highly spectrally efficient networks, whose margins are minimized by transmission-quality-aware adaptive modulation-order/channel-bandwidth assignment enabled by optical performance monitoring (OPM). Their effectiveness is confirmed by experiments on 400-Gbps dual-polarization quadrature phase shift keying (DP-QPSK) and 16-ary quadrature amplitude modulation (DP-16QAM) signals with the application of recently developed Q-factor-based OPM. Four-subcarrier 32-Gbaud DP-QPSK signals within 150/162.5/175GHz and two-subcarrier 32-Gbaud DP-16QAM signals within 75/87.5/100GHz are experimentally analyzed. Numerical network simulations in conjunction with the experimental results demonstrate that the proposed scheme can drastically improve network spectral efficiency.

We analyze the cost of networks consisting of optical cross-connect nodes with different architectures for realizing the next generation large bandwidth networks. The node architectures include wavelength granular and fiber granular optical routing cross-connects. The network cost, capital expenditure (CapEx), involves link cost and node cost, both of which are evaluated for different scale networks under various traffic volumes. Numerical experiments demonstrate that the subsystem modular architecture with wavelength granular routing yields the highest cost effectiveness over a wide range of parameter values.

Ferroelectric gate field-effect transistors (MFSFETs) were investigated utilizing nondoped HfO2 deposited by RF magnetron sputtering utilizing Hf target. After the post-metallization annealing (PMA) process with Pt top gate at 500°C/30s, ferroelectric characteristic of 10nm thick nondoped HfO2 was obtained. The fabricated MFSFETs showed the memory window of 1.7V when the voltage sweep range was from -3 to 3V.

In this paper, the effects of the TiN encapsulating layer and the dopant segregation process on the interface properties and the Schottky barrier height reduction of PdEr-silicide/n-Si(100) were investigated. The results show that controlling the initial location of the boron dopants by adding the TiN encapsulating layer lowered the Schottky barrier height (SBH) for hole to 0.20 eV. Furthermore, the density of interface states (Dit) on the order of 1011eV-1cm-2 was obtained indicating that the dopant segregation process with TiN encapsulating layer effectively annihilated the interface states.

We have investigated the in-situ N2-plasma nitridation for high-k HfN gate insulator formed by electron cyclotron resonance (ECR) plasma sputtering to improve the electrical characteristics. It was found that the increase of nitridation gas pressure for the deposited HfN1.1 gate insulator, such as 98 mPa, decreased both the hysteresis width in C-V characteristics and leakage current. Furthermore, the 2-step nitiridation process with the nitridation gas pressure of 26 mPa followed by the nitridation at 98 mPa realized the decrease of equivalent oxide thickness (EOT) to 0.9 nm with decreasing the hysteresis width and leakage current. The fabricated metal-insulator-semiconductor field-effect transistor (MISFET) with 2-step nitridation showed a steep subthreshold swing of 87 mV/dec.

This paper presents the feasibility of a capacitive coupler utilizing an electric double layer for wireless power transfer under seawater. Since seawater is an electrolyte solution, an electric double layer (EDL) is formed on the electrode surface of the coupler in direct current. If the EDL can be utilized in radio frequency, it is possible that high power transfer efficiency can be achieved under seawater because a high Q-factor can be obtained. To clarify this, the following steps need taking; First, measure the frequency characteristics of the complex permittivity in seawater and elucidate the behaviors of the EDL from the results. Second, clarify that EDL leads to an improvement in the Q-factor of seawater. It will be shown in this paper that capacitive coupling by EDL occurs using two kinds of the coupler models. Third, design a coupler with high efficiency as measured by the Q-factor and relative permittivity of EDL. Last, demonstrate that the designed coupler under seawater can achieve over 85% efficiency at a transfer distance of 5 mm and feasibility of the coupler with EDL.

High frame rate and ultra-low delay are the most essential requirements for building excellent human-machine-interaction systems. As a state-of-the-art local keypoint detection and feature extraction algorithm, A-KAZE shows high accuracy and robustness. Nonlinear scale space is one of the most important modules in A-KAZE, but it not only has at least one frame delay and but also is not hardware friendly. This paper proposes a hardware oriented nonlinear scale space for high frame rate and ultra-low delay A-KAZE matching system. In the proposed matching system, one part of nonlinear scale space is temporally forward and calculated in the previous frame (proposal #1), so that the processing delay is reduced to be less than 1 ms. To improve the matching accuracy affected by proposal #1, pre-adjustment of nonlinear scale (proposal #2) is proposed. Previous two frames are used to do motion estimation to predict the motion vector between previous frame and current frame. For further improvement of matching accuracy, pixel-level pre-adjustment (proposal #3) is proposed. The pre-adjustment changes from block-level to pixel-level, each pixel is assigned an unique motion vector. Experimental results prove that the proposed matching system shows average matching accuracy higher than 95% which is 5.88% higher than the existing high frame rate and ultra-low delay matching system. As for hardware performance, the proposed matching system processes VGA videos (640×480 pixels/frame) at the speed of 784 frame/second (fps) with a delay of 0.978 ms/frame.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactions, because it guarantees high-quality experiences for users. Existing image matching algorithms always generate mismatches which heavily weaken the performance the human-machine-interactive systems. Although many mismatch removal algorithms have been proposed, few of them achieve real-time speed with high frame rate and low delay, because of complicated arithmetic operations and iterations. This paper proposes a temporal constraints and block weighting judgement based high frame rate and ultra-low delay mismatch removal system. The proposed method is based on two temporal constraints (proposal #1 and proposal #2) to firstly find some true matches, and uses these true matches to generate block weighting (proposal #3). Proposal #1 finds out some correct matches through checking a triangle route formed by three adjacent frames. Proposal #2 further reduces mismatch risk by adding one more time of matching with opposite matching direction. Finally, proposal #3 distinguishes the unverified matches to be correct or incorrect through weighting of each block. Software experiments show that the proposed mismatch removal system achieves state-of-the-art accuracy in mismatch removal. Hardware experiments indicate that the designed image processing core successfully achieves real-time processing of 784fps VGA (640×480 pixels/frame) video on field programmable gate array (FPGA), with a delay of 0.858 ms/frame.

This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.

In this paper, we propose homomorphic encryption based device owner equality verification (HE-DOEV), a new method to verify whether the owners of two devices are the same. The proposed method is expected to be used for credential sharing among devices owned by the same user. Credential sharing is essential to improve the usability of devices with hardware-assisted trusted environments, such as a secure element (SE) and a trusted execution environment (TEE), for securely storing credentials such as private keys. In the HE-DOEV method, we assume that the owner of every device is associated with a public key infrastructure (PKI) certificate issued by an identity provider (IdP), where a PKI certificate is used to authenticate the owner of a device. In the HE-DOEV method, device owner equality is collaboratively verified by user devices and IdPs that issue PKI certificates to them. The HE-DOEV method verifies device owner equality under the condition where multiple IdPs can issue PKI certificates to user devices. In addition, it can verify the equality of device owners without disclosing to others any privacy-related information such as personally identifiable information and long-lived identifiers managed by an entity. The disclosure of privacy-related information is eliminated by using homomorphic encryption. We evaluated the processing performance of a server needed for an IdP in the HE-DOEV method. The evaluation showed that the HE-DOEV method can provide a DOEV service for 100 million users by using a small-scale system in terms of the number of servers.

The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneficial to machine learning, data mining, and artificial intelligence applications. However, the previous work suffers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a unified vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classification tasks are conducted, results show that the proposed framework can significantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method[15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classification task.

Collaborative filtering (CF) is one of the most popular approaches to building Recommender systems (RS) and has been extensively implemented in many online applications. But it still suffers from the new user cold start problem that users have only a small number of items interaction or purchase records in the system, resulting in poor recommendation performance. Thus, we design a new similarity model which can fully utilize the limited rating information of cold users. We first construct a new metric, Popularity-Mean Squared Difference, considering the influence of popular items, average difference between two user's common ratings and non-numerical information of ratings. Moreover, the second new metric, Singularity-Difference, presents the deviation degree of favor to items between two users. It considers the distribution of the similarity degree of co-ratings between two users as weight to adjust the deviation degree. Finally, we take account of user's personal rating preferences through introducing the mean and variance of user ratings. Experiment results based on three real-life datasets of MovieLens, Epinions and Netflix demonstrate that the proposed model outperforms seven popular similarity methods in terms of MAE, precision, recall and F1-Measure under new user cold start condition.

Pedestrian detection is a critical problem in computer vision with significant impact on many real-world applications. In this paper, we introduce an fast dual-task pedestrian detector with integrated segmentation context (DTISC) which predicts pedestrian location as well as its pixel-wise segmentation. The proposed network has three branches where two main branches can independently complete their tasks while useful representations from each task are shared between two branches via the integration branch. Each branch is based on fully convolutional network and is proven effective in its own task. We optimize the detection and segmentation branch on separate ground truths. With reasonable connections, the shared features introduce additional supervision and clues into each branch. Consequently, the two branches are infused at feature spaces increasing their robustness and comprehensiveness. Extensive experiments on pedestrian detection and segmentation benchmarks demonstrate that our joint model improves the performance of detection and segmentation against state-of-the-art algorithms.

This paper reviews our developed wide band human body communication technology for wearable and implantable robot control. The wearable and implantable robots are assumed to be controlled by myoelectric signals and operate according to the operator's will. The signal transmission for wearable robot control was shown to be mainly realized by electrostatic coupling, and the signal transmission for implantable robot control was shown to be mainly determined by the lossy frequency-dependent dielectric properties of human body. Based on these basic observations on signal transmission mechanisms, we developed a 10-50MHz band impulse radio transceiver based on human body communication technology, and applied it for wireless control of a robotic hand using myoelectric signals in the first time. In addition, we also examined its applicability to implantable robot control, and evaluated the communication performance of implant signal transmission using a living swine. These experimental results showed that the proposed technology is well suited for detection and transmission of biological signals for wearable and implantable robot control.

An offline sensor gain-phase errors calibration method for a linear array using a source in unknown location is proposed. The proposed method is realized through three steps. First, based on the observed covariance matrix, we construct a function related to direction, and it is proved that when the function takes the minimum value, the corresponding value should be the direction of the calibration source. Second, the direction of calibration source is estimated by locating the valley from the constructed function. Third, the gain-phase errors are obtained based on the estimated direction. The proposed method offers a number of advantages. First, the accurate direction measurement of the calibration source is not required. Second, only one calibration source needs to be arranged. Third, it does not require an iterative procedure or a two-dimensional (2D) spectral search. Fourth, the method is applicable to linear arrays, not only to uniform linear arrays (ULAs). Numerical simulations are presented to verify the efficacy of the proposed method.

This study presents a method for improving the heartbeat interval accuracy of photoplethysmographic (PPG) sensors at ultra-low sampling rates. Although sampling rate reduction can extend battery life, it increases the sampling error and degrades the accuracy of the extracted heartbeat interval. To overcome these drawbacks, a sampling-error compensation method is proposed in this study. The sampling error is reduced by using linear interpolation and autocorrelation based on the waveform similarity of heartbeats in PPG. Furthermore, this study introduces two-line approximation and first derivative PPG (FDPPG) to improve the waveform similarity at ultra-low sampling rates. The proposed method was evaluated using measured PPG and reference electrocardiogram (ECG) of seven subjects. The results reveal that the mean absolute error (MAE) of 4.11ms was achieved for the heartbeat intervals at a sampling rate of 10Hz, compared with 1-kHz ECG sampling. The heartbeat interval error was also evaluated based on a heart rate variability (HRV) analysis. Furthermore, the mean absolute percentage error (MAPE) of the low-frequency/high-frequency (LF/HF) components obtained from the 10-Hz PPG is shown to decrease from 38.3% to 3.3%. This error is small enough for practical HRV analysis.

Assurance cases are documents for arguing that systems satisfy required properties such as safety and security in the given environment based on sufficient evidence. As systems become complex and networked, the importance of assurance cases has become significant. However, we observe that creating assurance cases has some essential difficulties, and unfortunately it seems that assurance cases have not been widely used in industries. For this problem, we have been developing assurance cases creation methods and opening workshops based on the creation methods. This paper presents an assurance cases creation method called “D-Case Steps” which is based on d* framework[1], an agent-based assurance case method, and reports the results of workshops. The results indicate that our workshops have been improved and our activities on assurance cases facilitates use of them in Japan. This paper is an extended version of [2]. We add detailed background and related works, workshops results and evaluation, and lessons learned from our a decade experiences.

A laser imaging detection and ranging (LIDAR) is one of the key sensors for autonomous driving. In order to improve its performance of the measurable distance, especially toward the front-side direction of the vehicle, this paper presents rapid revolution speed control of a brushless DC (BLDC) motor with a cyclostationary command signal. This enables the increase of the signal integration time for the designated direction, and thus improves the signal-to-noise ratio (SNR), while maintaining the averaged revolution speed. We propose the use of pre-emphasis circuits to accelerate and decelerate the revolution speed of the motor rapidly, by modifying the command signal so as to enhance the transition of the speed. The adaptive signal processing can adjust coefficients of the pre-emphasis filter automatically, so that it can compensate for the decayed response of the motor and its controller. Experiments with a 20-W BLDC motor prove that the proposed technique can achieve the actual revolution speed output to track the designated speed profile ranging from 600 to 1400 revolutions per minute (rpm) during one turn.

In machine translation (MT) mediated human-to-human communication, it is not an easy task to select the languages and translation services to be used as the users have various language backgrounds and skills. Our previous work introduced the best-balanced machine translation mechanism (BBMT) to automatically select the languages and translation services so as to equalize the language barriers of participants and to guarantee their equal opportunities in joining conversations. To assign proper languages to be used, however, the mechanism needs information of the participants' language skills, typically participants' language test scores. Since it is important to keep test score confidential, as well as other sensitive information, this paper introduces agents, which exchange encrypted information, and secure computation to ensure that agents can select the languages and translation services without destroying privacy. Our contribution is to introduce a multi-agent system with secure computation that can protect the privacy of users in multilingual communication. To our best knowledge, it is the first attempt to introduce multi-agent systems and secure computing to this area. The key idea is to model interactions among agents who deal with user's sensitive data, and to distribute calculation tasks to three different types of agents, together with data encryption, so no agent is able to access or recover participants' score.

Wireless medical telemetry service (WMTS) is an important wireless communication system in healthcare facilities. Recently, the potential for electromagnetic interference by noise emitted by switching regulators installed in light-emitting diode (LED) lamps has been a serious problem. In this study, we evaluated the characteristics of the electromagnetic noise emitted from LED lamps and its effect on WMTS. Switching regulators generally emit wide band impulsive noise whose bandwidth reaches 400MHz in some instances owing to the switching operation, but this impulsive nature is difficult to identify in the reception of WMTS because the bandwidth of WMTS is much narrower than that of electromagnetic noise. Gaussian approximation (GA) can be adopted for band-limited electromagnetic noise whose characteristics have no repetitive variation. On the other hand, GA with the impulsive correction factor (ICF) can be adopted for band-limited electromagnetic noise that has repetitive variation. We investigate the minimum receiver sensitivity of WMTS for it to be affected by electromagnetic noise emitted from LED lamps. The required carrier-to-noise power ratio (CNR) of Gaussian noise and electromagnetic noise for which GA can be adopted was approximately 15dB, but the electromagnetic noise for which GA with the ICF can be adopted was 3 to 4dB worse. Moreover, the spatial distribution of electromagnetic noise surrounding an LED lamp installation was measured. Finally, we roughly estimated the offset distance between the receiving antenna of WMTS and LED lamps when a WMTS signal of a certain level was added in a clinical setting using our experimental result for the required CNR.