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.

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 propose a coding/decoding strategy that surpasses the symmetric information rate of a binary insertion/deletion (ID) channel and approaches the Markov capacity of the channel. The proposed codes comprise inner trellis codes and outer irregular low-density parity-check (LDPC) codes. The trellis codes are designed to mimic the transition probabilities of a Markov input process that achieves a high information rate, whereas the LDPC codes are designed to maximize an iterative decoding threshold in the superchannel (concatenation of the ID channels and trellis codes).

We show that imperfection in an IQ-modulator degrades the accuracy of the auto bias control (ABC) circuit connected to the modulator's complementary port. Theoretical analyses show that the IQ-modulator constructed by a nested Mach-Zehnder modulator with a low extinction ratio can distort a constellation of modulated light observed at the complementary port. We propose an auto calibration technique for the ABC circuit that can effectively suppress this degradation. Experimental results using 32-Gbaud, 16-QAM signals showed the measured Q-factor improved by 0.5dB with our proposed technique.

Currently, the top-k error ratio is one of the primary methods to measure the accuracy of multi-category classification. Top-k multiclass SVM was designed to minimize the empirical risk based on the top-k error ratio. Two SDCA-based algorithms exist for learning the top-k SVM, both of which have several desirable properties for achieving optimization. However, both algorithms suffer from a serious disadvantage, that is, they cannot attain the optimal convergence in most cases owing to their theoretical imperfections. As demonstrated through numerical simulations, if the modified SDCA algorithm is employed, optimal convergence is always achieved, in contrast to the failure of the two existing SDCA-based algorithms. Finally, our analytical results are presented to clarify the significance of these existing algorithms.

We developed a polarization-independent and reserved-band-less complementary spectral inverted optical phase conjugation (CSI-OPC) device using dual-band difference frequency generation based on highly efficient periodically poled LiNbO3 waveguide technologies. To examine the nonlinearity mitigation in a long-haul transmission using a large number of OPCs, we installed a CSI-OPC device in the middle of a pure silica core fiber-based recirculating loop transmission line with a length of 320km. First, we examined the fiber-input power tolerance after 5,120-km and 6,400-km transmission using 22.5-Gbaud PDM-16QAM 10-channel DWDM signals and found a Q-factor improvement of over 1.3dB along with enhanced power tolerance thanks to mitigating the fiber nonlinearity. We then demonstrated transmission distance extension using the CSI-OPC device. The use of multiple CSI-OPCs enables an obvious performance improvements attained by extending the transmission distance from 6,400km to 8,960km, which corresponds to applying the CSI-OPC device 28 times. Moreover, there was no Q-factor degradation for the link in a linear regime after applying the CSI-OPC device more than 16 times. These results demonstrate that the CSI-OPC device can improve the nonlinear tolerance of PDM-16QAM signals without an excess penalty.

Effective user accommodation will be more and more important in passive optical networks (PONs) in the next decade since the number of subscribers has been leveling off as well and it is becoming more difficult for network operators to keep sufficient numbers of maintenance workers. Drastically reducing the number of small-scale communication buildings while keeping the number of accommodated users is one of the most attractive solutions to meet this situation. To achieve this, we propose two types of long-reach repeater-free upstream transmission configurations for PON systems; (i) one utilizes a semiconductor optical amplifier (SOA) as a pre-amplifier and (ii) the other utilizes distributed Raman amplification (DRA) in addition to the SOA. Our simulations assuming 10G-EPON specifications and transmission experiments on a 10G-EPON prototype confirm that configuration (i) can add a 17km trunk fiber to a normal PON system with 10km access reach and 1 : 64 split (total 27km reach), while configuration (ii) can further expand the trunk fiber distance to 37km (total 47km reach). Network operators can select these configurations depending on their service areas.

In the typical unmanned aircraft system (UAS), several unmanned aerial vehicles (UAVs) traveling at a velocity of 40-100km/h and with altitudes of 150-1,000m will be used to cover a wide service area. Therefore, Doppler shifts occur in the carrier frequencies of the transmitted and received signals due to changes in the line-of-sight velocity between the UAVs and the terrestrial terminal. By observing multiple Doppler shift values for different UAVs or observing a single UAV at different local times, it is possible to detect the user position on the ground. We conducted computer simulations for evaluating user position detection accuracy and Doppler shift distribution in several flight models. Further, a positioning accuracy index (PAI), which can be used as an index for position detection accuracy, was proposed as the absolute value of cosine of the inner product between two gradient vectors formed by Doppler shifts to evaluate the relationship between the location of UAVs and the position of the user. In this study, a maximum positioning error estimation method related to the PAI is proposed to approximate the position detection accuracy. Further, computer simulations assuming a single UAV flying on the curved routes such as sinusoidal routes with different cycles are conducted to clarify the effectiveness of the flight route in the aspects of positioning accuracy and latency by comparing with the conventional straight line fight model using the PAI and the proposed maximum positioning error estimation method.

A ring-resonator type of electrode (RRTE) has been proposed to detect the circulating tumor cell (CTC) for evaluation of the current cancer progression and malignancy in clinical applications. Main emphasis is placed on the identification sensitivity for the lossy materials that can be found in biomedical fields. At first, the possibility of the CTC detection was numerically considered to calculate the resonant frequency of the RRTE catching the CTC, and it was evident that the RRTE with the cell has the resonant frequency inherent in the cell featured by its complex permittivity. To confirm the numerical consideration, the BaTiO3 particle, whose size was similar to that of the CTC, was inserted in the RRTE instead of the CTC as a preliminary experiment. Next, the resonant frequencies of the RRTE with internal organs of the beef cattle such as liver, lung, and kidney were measured for evaluation of the lossy materials such as the CTC, and degraded Q curves were observed because the Q-factors inherent in the internal organs were usually low due to the poor loss tangents. To overcome such difficulty, the RRTE, the oscillator circuit consisting of the FET being added, was proposed to improve the identification sensitivity. Comparing the identification sensitivity of the conventional RRTE, it has been improved because the oscillation frequency spectrum inherent in an internal organ could be easily observed thanks to the oscillation condition with negative resistance. Thus, the validity of the proposed technique has been confirmed.

In this study, the transient characteristics on the super-steep subthreshold slope (SS) of a PN-body tied (PNBT) silicon-on-insulator field-effect transistor (SOI-FET) were investigated using technology computer-aided design and pulse measurements. Carrier charging effects were observed on the super-steep SS PNBT SOI-FET. It was found that the turn-on delay time decreased to nearly zero when the gate overdrive-voltage was set to 0.1-0.15 V. Additionally, optimizing the gate width improved the turn-on delay. This has positive implications for the low speed problems of this device. However, long-term leakage current flows on turn-off. The carrier lifetime affects the leakage current, and the device parameters must be optimized to realize both a high on/off ratio and high-speed operation.

This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

Over the past two decades, irregular low-density parity-check (LDPC) codes have not been able to decode information corrupted by insertion and deletion (ID) errors without markers. In this paper, we bring to light the existence of irregular LDPC codes that approach the symmetric information rates (SIR) of the channel with ID errors, even without markers. These codes have peculiar shapes in their check-node degree distributions. Specifically, the check-node degrees are scattered and there are degree-2 check nodes. We propose a code construction method based on the progressive edge-growth algorithm tailored for the scattered check-node degree distributions, which enables the SIR-approaching codes to progress in the finite-length regime. Moreover, the SIR-approaching codes demonstrate asymptotic and finite-length performance that outperform the existing counterparts, namely, concatenated coding of irregular LDPC codes with markers and spatially coupled LDPC codes.

The task of image annotation is becoming enormously important for efficient image retrieval from the web and other large databases. However, huge semantic information and complex dependency of labels on an image make the task challenging. Hence determining the semantic similarity between multiple labels on an image is useful to understand any incomplete label assignment for image retrieval. This work proposes a novel method to solve the problem of multi-label image annotation by unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance. The unified Laplacian regularization model is implemented to address the missing labels efficiently by generating the contextual similarity between labels both internally and externally through their semantic similarities, which is the main contribution of this study. Specifically, we generate similarity matrices between labels internally by using Hayashi's quantification method-type III and externally by using the word2vec method. The generated similarity matrices from the two different methods are then combined as a Laplacian regularization term, which is used as the new objective function of the deep CNN. The Regularization term implemented in this study is able to address the multi-label annotation problem, enabling a more effectively trained neural network. Experimental results on public benchmark datasets reveal that the proposed unified regularization model with deep CNN produces significantly better results than the baseline CNN without regularization and other state-of-the-art methods for predicting missing labels.

Many studies of deep neural networks have reported inference accelerators for improved energy efficiency. We propose methods for further improving energy efficiency while maintaining recognition accuracy, which were developed by the co-design of a filter-by-filter quantization scheme with variable bit precision and a hardware architecture that fully supports it. Filter-wise quantization reduces the average bit precision of weights, so execution times and energy consumption for inference are reduced in proportion to the total number of computations multiplied by the average bit precision of weights. The hardware utilization is also improved by a bit-parallel architecture suitable for granularly quantized bit precision of weights. We implement the proposed architecture on an FPGA and demonstrate that the execution cycles are reduced to 1/5.3 for ResNet-50 on ImageNet in comparison with a conventional method, while maintaining recognition accuracy.

With increasing technology scaling and the use of lower voltages, more research interest is being shown in variability-tolerant analog front end design. In this paper, we describe an adaptive amplitude control transmitter that is operated using differential signaling to reduce the temperature variability effect. It enables low power, low voltage operation by synergy between adaptive amplitude control and Vth temperature variation control. It is suitable for high-speed interface applications, particularly cable interfaces. By installing an aggressor circuit to estimate transmitter jitter and changing its frequency and activation rate, we were able to analyze the effects of the interface block on the input buffer and thence on the entire system. We also report a detailed estimation of the receiver clock-data recovery (CDR) operation for transmitter jitter estimation. These investigations provide suggestions for widening the eye opening of the transmitter.

Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

For automotive millimeter radar, a method using a multi-input multi-output (MIMO) array antenna is essential for high angle resolution with module miniaturization. MIMO enables us to extend an antenna array with virtual antennas, and a large antenna array aperture enables high resolution angle estimation. Time division multiplex (TDM) MIMO, which is a method to generate virtual array antennas, makes it easy to design radar system integrated circuits. However, this method leads to two issues in signal processing; the phase error reduces the accuracy of angle estimation of a moving target, and the maximum detectable velocity decreases in inverse proportion to the number of Tx antennas. We analytically derived this phase error and proposed a method to correct the error. Because the phase error of TDM-MIMO is proportional to the target velocity, accurate estimation of the target velocity is an important issue for phase error correction. However, the decrease of the maximum detectable velocity in TDM-MIMO reduces the accuracy of both velocity estimation and angle estimation. To solve these issues, we propose new signal processing for range-velocity estimation for TDM-MIMO radar. By using the feedback result of the estimated direction of arrival (DoA), we can avoid decreasing the maximum detectable velocity. We explain our method with our simulation results.

This paper presents a design of CMOS transimpedance amplifier (TIA) and peaking inductor for high speed, low power and small area. To realize high density integration of optical I/O, area reduction is an important figure as well as bandwidth, power and so on. To determine design parameters of multi-stage inverter-type TIA (INV-TIA) with peaking inductors, we derive a simplified model of the bandwidth and the energy per bit. Multi-layered on-chip inductors are designed for area-effective inductive peaking. A 5-stage INV-TIA with 3 peaking inductors is fabricated in a 65-nm CMOS. By using multi-layered inductors, 0.02 mm2 area is achieved. Measurement results show 45 Gb/s operation with 49 dBΩ transimpedance gain and 4.4 mW power consumption. The TIA achieves 98 fJ/bit energy efficiency.

In this paper, we present non-Markovian availability models for capturing the dynamics of system behavior of an operational software system that undergoes aperiodic time-based software rejuvenation and checkpointing. Two availability models with rejuvenation are considered taking account of the procedure after the completion of rollback recovery operation. We further proceed to investigate whether there exists the optimal rejuvenation schedule that maximizes the steady-state system availability, which is derived by means of the phase expansion technique, since the resulting models are not the trivial stochastic models such as semi-Markov process and Markov regenerative process, so that it is hard to solve them by using the common approaches like Laplace-Stieltjes transform and embedded Markov chain techniques. The numerical experiments are conducted to determine the optimal rejuvenation trigger timing maximizing the steady-state system availability for each availability model, and to compare both two models.