The impedance expansion method (IEM) is a circuit-modeling technique for electrically small devices based on the method of moments. In a previous study, a circuit model of a wireless power transfer (WPT) system was developed by utilizing the IEM and eigenmode analysis. However, this technique assumes that all the coupling elements (e.g., feeding loops and resonant coils) are in the absence of neighboring scatters (e.g., bodies of vehicles). This study extends the theory of the IEM to obtain the circuit model of a WPT system in the vicinity of a perfectly conducting scatterer (PCS). The numerical results show that the proposed method can be applied to the frequencies at which the dimension of the PCS is less than approximately a quarter wavelength. In addition, the yielded circuit model is found to be valid at the operating frequency band.

We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.

In this paper, we consider the collaborative editing of two-dimensional (2D) data such as handwritten letters and illustrations. In contrast to the editing of 1D data, which is generally realized by the combination of insertion/deletion of characters, overriding of strokes can have a specific meaning in editing 2D data. In other words, the appearance of the resulting picture depends on the reflection order of strokes to the shared canvas in addition of the absolute coordinate of the strokes. We propose a Peer-to-Peer (P2P) collaborative drawing system consisting of several nodes with replica canvas, in which the consistency among replica canvases is maintained through data channel of WebRTC. The system supports three editing modes concerned with the reflection order of strokes generated by different users. The result of experiments indicates that the proposed system realizes a short latency of around 120 ms, which is a half of a cloud-based system implemented with Firebase Realtime Database. In addition, it realizes a smooth drawing of pictures on remote canvases with a refresh rate of 12 fps.

Expectation propagation (EP) decoding is proposed for sparse superposition coding in orthogonal frequency division multiplexing (OFDM) systems. When a randomized discrete Fourier transform (DFT) dictionary matrix is used, the EP decoding has the same complexity as approximate message-passing (AMP) decoding, which is a low-complexity and powerful decoding algorithm for the additive white Gaussian noise (AWGN) channel. Numerical simulations show that the EP decoding achieves comparable performance to AMP decoding for the AWGN channel. For OFDM systems, on the other hand, the EP decoding is much superior to the AMP decoding while the AMP decoding has an error-floor in high signal-to-noise ratio regime.

Autonomous mobility machines, such as self-driving cars, transportation robots, and automated construction machines, are promising to support or enrich human lives. To further improve such machines, they will be connected to the network via wireless links to be managed, monitored, or remotely operated. The autonomous mobility machines must have self-status based on their positioning system to safely conduct their operations without colliding with other objects. The self-status is not only essential for machine operation but also it is valuable for wireless link quality management. This paper presents self-status-based wireless link quality prediction and evaluates its performance by using a prototype mobility robot combined with a wireless LAN system. The developed robot has functions to measure the throughput and receive signal strength indication and obtain self-status details such as location, direction, and odometry data. Prediction performance is evaluated in offline processing by using the dataset gathered in an indoor experiment. The experiments clarified that, in the 5.6 GHz band, link quality prediction using self-status of the robot forecasted the throughput several seconds into the future, and the prediction accuracies were investigated as dependent on time window size of the target throughput, bandwidth, and frequency gap.

Change-point detection is the problem of finding points of time when a probability distribution of samples changed. There are various related problems, such as estimating the number of the change-points and estimating magnitude of the change. Though various statistical models have been assumed in the field of change-point detection, we particularly deal with i.p.i.d. (independent-piecewise-identically-distributed) sources. In this paper, we formulate the related problems in a general manner based on statistical decision theory. Then we derive optimal estimators for the problems under the Bayes risk principle. We also propose efficient algorithms for the change-point detection-related problems in the i.p.i.d. sources, while in general, the optimal estimations requires huge amount of calculation in Bayesian setting. Comparison of the proposed algorithm and previous methods are made through numerical examples.

We propose an optimal power allocation scheme that maximizes the transmission rate of device-to-device (D2D) communications underlaying a cellular system based on orthogonal frequency division multiplexing (OFDM). The proposed algorithm first calculates the maximum allowed transmission power of a D2D transmitter to restrict the interference caused to a cellular link that share the same OFDM subchannels with the D2D link. Then, with a constraint on the maximum transmit power, an optimization of water-filling type is performed to find the optimal transmit power allocation across subchannels and within each subchannel. The performance of the proposed power allocation scheme is evaluated in terms of the average achievable rate of the D2D link.

A quadratic extension field (QEF) defined by F1 = Fp[α]/(α2+1) is typically used for a supersingular isogeny Diffie-Hellman (SIDH). However, there exist other attractive QEFs Fi that result in a competitive or rather efficient performing the SIDH comparing with that of F1. To exploit these QEFs without a time-consuming computation of the initial setting, the authors propose to convert existing parameter sets defined over F1 to Fi by using an isomorphic map F1 → Fi.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.

We fabricated an InP-based dual-polarization In-phase and Quadrature (DP-IQ) modulator consisting of a Mach-Zehnder (MZ) modulator array integrated with RF termination resistors and backside via holes for high-bandwidth coherent driver modulators and revealed its high reliability. These integrations allowed the chip size (Chip size: 4.4mm×3mm) to be reduced by 59% compared with the previous chip without these integrations, that is, the previous chip needed 8 chip-resistors for terminating RF signals and 12 RF electrode pads for the electrical connection with these resistors in a Signal-Ground-Signal configuration. This MZ modulator exhibited a 3-dB bandwidth of around 40 GHz as its electrical/optical response, which is sufficient for over 400 Gbit/s coherent transmission systems using 16-ary quadrature amplitude modulation (QAM) and 64QAM signals. Also, we investigated a rapid degradation which affects the reliability of InP-based DP-IQ modulators. This rapid degradation we called optical damage is caused by strong incident light power and a high reverse bias voltage condition at the entrance of an electrode in each arm of the MZ modulators. This rapid degradation makes it difficult to estimate the lifetime of the chip using an accelerated aging test, because the value of the breakdown voltage which induces optical damage varies considerably depending on conditions, such as light power, operation wavelength, and chip temperature. Therefore, we opted for the step stress test method to investigate the lifetime of the chip. As a result, we confirmed that optical damage occurred when photo-current density at the entrance of an electrode exceeded threshold current density and demonstrated that InP-based modulators did not degrade unless operation conditions reached threshold current density. This threshold current density was independent of incident light power, operation wavelength and chip temperature.

In this paper, we analyzed the pulse responses of dispersion medium with periodically conducting strips by using a fast inversion Laplace transform (FILT) method combined with point matching method (PMM) for both the TM and TE cases. Specifically, we investigated the influence of the width and number of the conducting strips on the pulse response and distribution of the electric field.

This paper proposes a voice conversion (VC) method based on a model that links linguistic and acoustic representations via latent phonological distinctive features. Our method, called speech chain VC, is inspired by the concept of the speech chain, where speech communication consists of a chain of events linking the speaker's brain with the listener's brain. We assume that speaker identity information, which appears in the acoustic level, is embedded in two steps — where phonological information is encoded into articulatory movements (linguistic to physiological) and where articulatory movements generate sound waves (physiological to acoustic). Speech chain VC represents these event links by using an adaptive restricted Boltzmann machine (ARBM) introducing phoneme labels and acoustic features as two classes of visible units and latent phonological distinctive features associated with articulatory movements as hidden units. Subjective evaluation experiments showed that intelligibility of the converted speech significantly improved compared with the conventional ARBM-based method. The speaker-identity conversion quality of the proposed method was comparable to that of a Gaussian mixture model (GMM)-based method. Analyses on the representations of the hidden layer of the speech chain VC model supported that some of the hidden units actually correspond to phonological distinctive features. Final part of this paper proposes approaches to achieve one-shot VC by using the speech chain VC model. Subjective evaluation experiments showed that when a target speaker is the same gender as a source speaker, the proposed methods can achieve one-shot VC based on each single source and target speaker's utterance.

Impact of sampling frequency and the number of quantization bit of analog-to-digital conversion (ADC) in a direct detection lightwave system using Kramers-Kronig (KK) relation, which has been attracting attention in recent years, are numerically investigated. We studied the effect of spectral broadening caused by nonlinear operations (logarithm, square root) of the KK algorithm when the frequency gap (shift frequency) between the modulated signal and the optical tone is varied. We found that reception performances depend on both the ADC bandwidth and the relative positions of the optical tone and the spectrum. Spectral broadening caused by the logarithm operation of the KK algorithm is found to be the dominant factor of signal distortion in an ADC bandwidth limited system. We studied the effect of the number of quantization bit on the error vector magnitude (EVM) of KK relation based reception in a carrier-to-signal power ratio (CSPR) adjustable transmission system. We found that performances of KK relation based receiver can be improved by increasing the number of quantization bits. For minimum-phase-condition satisfied KK receiver, the required number of quantization bit was found to be 5 bits or more for detection of QPSK, 16-QAM and 64-QAM-modulated signal after 20-km transmission.

We demonstrated 1-Tb/s-class transmissions of field-deployed large-core low-loss fiber links, which is compliant with ITU-T G.654.E, using our newly developed real-time transponder consisting of a state-of-the-art 16-nm complementary metal-oxide-semiconductor (CMOS) based digital signal processing application-specific integrated circuit (DSP-ASIC) and an indium phosphide (InP) based high-bandwidth coherent driver modulator (HB-CDM). In this field experiment, we have achieved record transmission distances of 1122km for net data-rate 1-Tb/s transmission with dual polarization-division multiplexed (PDM) 32 quadrature amplitude modulation (QAM) signals, and of 336.6 km for net data-rate 1.2-Tb/s transmission with dual PDM-64QAM signals. This is the first demonstration of applying hybrid erbium-doped fiber amplifier (EDFA) and backward-distributed Raman amplifier were applied to terrestrial G.654.E fiber links. We also confirmed the stability of signal performance over field fiber transmission in wavelength division multiplexed (WDM) condition. The Q-factor fluctuations respectively were only less than or equal to 0.052dB and 0.07dB for PDM-32QAM and PDM-64QAM signals within continuous measurements for 60 minutes.

LSTM network have shown to outperform in facial expression recognition of video sequence. In view of limited representation ability of single-layer LSTM, a hierarchical attention model with enhanced feature branch is proposed. This new network architecture consists of traditional VGG-16-FACE with enhanced feature branch followed by a cross-layer LSTM. The VGG-16-FACE with enhanced branch extracts the spatial features as well as the cross-layer LSTM extracts the temporal relations between different frames in the video. The proposed method is evaluated on the public emotion databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.

In inter-data center networks where high transmission capacity and spectral efficiency are required, a 16QAM format is deployed. On the other hand, in intra-data center networks, a PAM4 format is deployed to meet the demand for a simple and low-cost transceiver configuration. For a seamless and effective connection of such heterogeneous networks without using optical-electrical-optical conversion, an all-optical modulation format conversion technique is required. In this paper, we propose an all-optical PAM4 to 16QAM modulation format conversion using nonlinear optical loop mirror. The successful conversion operation from 2 × 26.6-Gbaud PAM4 signals to a 100-Gbps class 16QAM signal is verified by numerical simulation. Compared with an ideal 16QAM signal, the power penalty of the converted 16QAM signal can be kept within 0.51dB.

A high degree of freedom in spectral domain allows us to accommodate additional optical signal processing for wavelength division multiplexing in photonic analog-to-digital conversion. We experimentally verified a spectral compression to save a necessary bandwidth for soliton self-frequency shift based optical quantization through the cascade of the four-wave mixing based and the sum-frequency generation based spectral compression. This approach can realize 0.03 nm individual bandwidth correspond to save up to more than 85 percent of bandwidth for 7-bit optical quantization in C-band.

Wavelength division multiplexing (WDM) scheme is used widely in photonic metro-core networks. In a WDM network, wavelength continuity constraint is employed to simply construct relay nodes. This constraint reduces the wavelength usage efficiency of each link. To improve the same, an all-optical wavelength converter (AO-WC) has been attracting attention in recent years. In particular, an AO-WC is a key device because it enables simultaneous conversion of multiple wavelengths of signal lights to other wavelengths, independent of the modulation format. However, each AO-WC requires installation of multiple laser sources with narrow bandwidth because the lights emitted by the laser sources are used as pump lights when the wavelengths of the signal lights are converted by the four-wave mixing (FWM) process. To reduce the number of laser sources, we propose a remote pumped AO-WC, in which the laser sources of the pump lights are aggregated into several relay nodes. When the request for the wavelength conversion from the relay node without the laser source is conveyed, the relay node with the laser source transmits the pump light through the optical link. The proposed scheme enables reduction in the number of laser sources of the pump lights. Herein we analyze the distortion of the pump light by propagating it through the optical link We also evaluate the effect of the noise in optical amplifiers and nonlinearities in optical fibers using numerical simulations employing the representative parameters for a practical WDM network.

High dynamic range (HDR) imaging refers to digital image processing that modifies the range of color and contrast to enhance image visibility. To create an HDR image, two or more images that include various information are needed. In order to convert low dynamic range (LDR) images to HDR images, we consider the possibility of using a generative adversarial network (GAN) as an appropriate deep neural network. Deep learning requires a great deal of data in order to build a module, but once the module is created, it is convenient to use. In this paper, we propose a weight map for local luminance based on learning to reconstruct locally tone-mapped images.