To achieve the extreme high data rate and extreme coverage extension requirements of 6G wireless communication, new spectrum in sub-THz (100-300GHz) and non-terrestrial network (NTN) are two of the macro trends of 6G candidate technologies, respectively. However, non-linearity of power amplifiers (PA) is a critical challenge for both sub-THz and NTN. Therefore, high power efficiency (PE) or low peak to average power ratio (PAPR) waveform design becomes one of the most significant 6G research topics. Meanwhile, high spectral efficiency (SE) and low out-of-band emission (OOBE) are still important key performance indicators (KPIs) for 6G waveform design. Single-carrier waveform discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) has achieved many research interests due to its high PE, and it has been supported in 5G New Radio (NR) when uplink coverage is limited. So DFT-s-OFDM can be regarded as a candidate waveform for 6G. Many enhancement schemes based on DFT-s-OFDM have been proposed, including null cyclic prefix (NCP)/unique word (UW), frequency-domain spectral shaping (FDSS), and time-domain compression and expansion (TD-CE), etc. However, there is no unified framework to be compatible with all the enhancement schemes. This paper firstly provides a general description of the 6G candidate waveforms based on DFT-s-OFDM enhancement. Secondly, the more flexible TD-CE supporting methods for unified non-orthogonal waveform (uNOW) are proposed and discussed. Thirdly, a unified waveform framework based on DFT-s-OFDM structure is proposed. By designing the pre-processing and post-processing modules before and after DFT in the unified waveform framework, the three technical methods (NCP/UW, FDSS, and TD-CE) can be integrated to improve three KPIs of DFT-s-OFDM simultaneously with high flexibility. Then the implementation complexity of the 6G candidate waveforms are analyzed and compared. Performance of different DFT-s-OFDM enhancement schemes is investigated by link level simulation, which reveals that uNOW can achieve the best PAPR performance among all the 6G candidate waveforms. When considering PA back-off, uNOW can achieve 124% throughput gain compared to traditional DFT-s-OFDM.

Thin films of Teflon AF 1600 were prepared by an electron-assisted (e-assist) deposition method. IR analysis revealed that the e-assist deposition generates small amount of polar groups such as carboxylic acid in the molecular structure of the deposited films. The polar groups contributed to increase intermolecular interaction and led to remarkable improvement in the adhesion strength and robustness of the films especially when a bias voltage was applied to the substrate in the course of e-assist deposition. The vapor-deposited Teflon AF films had refractive indices of 1.35 to 1.38, and were effective for antireflection coatings. The use of e-assist deposition slightly increased the refractive index as a trade-off for the improvement of film robustness.

Improvement of the on/off ratio in organic field-effect transistors through the use of pentacene and molybdenum trioxide (MoO3) layers was attempted via the preparation of a discontinuous MoO3 layer using a mesh mask. We prepared three types of devices. Device A had a conventional top-contact structure with an n-type Si wafer and a 200-nm-thick SiO2 film onto which we deposited a 70-nm-thick pentacene film and a 30-nm-thick layer of Au top electrodes. Devices B and C had a similar structure to device A but received a continuous and a discontinuous MoO3 layer, respectively. The off current in Device B was remarkably high; in contrast, the off current in Device C was reduced and dependent on the separation of the MoO3 layer. It was deduced that the high resistance of the area without MoO3 contributed to the reduced off current.

In this report, solar cell characteristics were evaluated by doping the active layer CH3NH3PbI3 (MAPbI3) with 3.0 vol% and 6.0 vol% of potassium ion (KI) in an inverse-structured perovskite solar cells (PSCs). The Tauc plots of the absorbance characteristics and the ionization potential characteristics show that the top end of the valence band shifted by 0.21eV in the shallow direction from -5.34eV to -5.13eV, and the energy band gap decreased from 1.530eV to 1.525eV. Also, the XRD measurements show that the lattice constant decreased from 8.96Å to 8.93Å when KI was doped. The decrease in the lattice constant indicates that a part of the A site is replaced from methylammonium ion (MAI) to KI. In the J-V characteristics of the solar cell, the mean value of Jsc improved from 7.0mA/cm2 without KI to 8.8mA/cm2 with 3.0 vol% of KI doped and to 10.2mA/cm2 with 6.0 vol% of KI doped. As a result, the mean value of power-conversion efficiency (PCE) without KI was 3.5%, but the mean value of PCE improved to 5.2% with 3.0 vol% of KI doped and to 4.5% with 6.0 vol% of KI doped. Thus, it has shown that it is effective to dope KI to MAIPBI3, which serves as the active layer, even in the inverse-structured PSCs.

We observed dynamical carrier motion in an OLED device under an external reverse bias application using ExTDR measurement. The rectangular wave pulses were used in our ExTDR to observe the transient impedance of the OLED sample. The falling edge of the transmission waveform reflects the transient impedance after applying pulse voltage during the pulse width. The observed pulse width variation at the falling edge waveform indicates that the frontline of the hole distribution in the hole transport layer was forced to move backward to the ITO electrode.

Conventional enzymatic biofuel cells (EBFCs) use glucose solution or glucose from human body. It is desirable to get glucose from a substance containing glucose because the glucose concentration can be kept at the optimum level. This work developed a biofuel cell that generates electricity from cellulose, which is the main components of plants, by using decomposing enzyme of cellulase. Cellulose nanofiber (CNF) was chosen for the ease of decomposability. It was confirmed by the cyclic voltammetry method that cellulase was effective against CNF. The maximum output of the optimized proposed method was 38.7 μW/cm2, which was 85% of the output by using the glucose solution at the optimized concentration.

Studies on intrinsic Josephson junctions (IJJs) of cuprate superconductors are reviewed. A system consisting of a few IJJs provides phenomena to test the Josephson phase dynamics and its interaction between adjacent IJJs within a nanometer scale, which is unique to cuprate superconductors. Quasiparticle density of states, which provides direct information on the Cooper-pair formation, is also revealed in the system. In contrast, Josephson plasma emission, which is an electromagnetic wave radiation in the sub-terahertz frequency range from an IJJ stack, arises from the synchronous phase dynamics of hundreds of IJJs coupled globally. This review summarizes a wide range of physical phenomena in IJJ systems having capacitive and inductive couplings with different nanometer and micrometer length scales, respectively.

Direct-current superconducting quantum interference device (dc-SQUID) based on intrinsic Josephson junction (IJJ) has been fabricated using Bi2Sr2CaCu2O8+δ (Bi-2212) films grown on MgO substrates with surface steps. The superconducting loop parallel to the film surface across the step edge contains two IJJ stacks along the edge. The number of crystallographically stacked IJJ for each SQUIDs were 40, 18 and 3. Those IJJ SQUIDs except for one with 40 stacked IJJs revealed clear periodic modulation of the critical current for the flux quanta through the loops. It is anticipated that phase locking of IJJ has an effect on the modulation depth of the IJJ dc-SQUID.

Intrinsic Josephson junctions (IJJs) in the high-Tc cuprate superconductors have several fascinating properties, which are superior to the usual Josephson junctions obtained from conventional superconductors with low Tc, as follows; (1) a very thin thickness of the superconducting layers, (2) a strong interaction between junctions since neighboring junctions are closely connected in an atomic scale, (3) a clean interface between the superconducting and insulating layers, realized in a single crystal with few disorders. These unique properties of IJJs can enlarge the applicable areas of the superconducting qubits, not only the increase of qubit-operation temperature but the novel application of qubits including the macroscopic quantum states with internal degree of freedom. I present a comprehensive review of the phase dynamics in current-biased IJJs and argue the challenges of superconducting qubits utilizing IJJs.

The demand for low-latency transmission of large-capacity video, such as 4K and 8K, is increasing for various applications such as live-broadcast program production, sports viewing, and medical care. In the broadcast industry, low-latency video transmission is required in remote production, an emerging workflow for outside broadcasting. For ideal remote production, long-distance transmission of uncompressed 8K60p video signals, ultra-low latency less than 16.7 ms, and PTP synchronization through network are required; however, no existing video-transmission system fully satisfy these requirements. We focused on optical transport technologies capable of long-distance and large-capacity communication, which were previously used only in telecommunication-carrier networks. To fully utilize optical transport technologies, we propose the first-ever video-transmission system architecture capable of sending and receiving uncompressed 8K video directly through large-capacity optical paths. A transmission timing control in seamless protection switching is also proposed to improve the tolerance to network impairment. As a means of implementation, we focused on whitebox transponder, an emerging type of optical transponder with a disaggregation configuration. The disaggregation configuration enables flexible configuration changes, additional implementations, and cost reduction by separating various functions of optical transponders and controlling them with a standardized interface. We implemented the ultra-low-latency video-transmission system utilizing whitebox transponder Galileo. We developed a hardware plug-in unit for video transmission (VideoPIU), and software to control the VideoPIU. In the video-transmission experiments with 120-km optical fiber, we confirmed that it was capable of transmitting uncompressed 8K60p video stably in 1.3 ms latency and highly accurate PTP synchronization through the optical network, which was required in the ideal remote production. In addition, the application to immersive sports viewing is also presented. Consequently, excellent potential to support the unprecedented applications is demonstrated.

In a free-space method using a pair of horn antennas with dielectric lenses, we demonstrated that the permittivity of a sample can be estimated with good accuracy by equalizing a measured transmission coefficient of a sample to a transmission coefficient for a Gaussian beam, which is approximately equal to the transmission coefficient for a plane wave multiplied by a term that changes the phase. In this permittivity estimation method, because the spot size at the beam waist in a Gaussian beam needs to be determined, we proposed an estimation method of the spot size by employing the measurement of the Line in Thru-Reflect-Line calibration; thus, no additional measurement is required. The permittivity estimation method was investigated for the E-band (60-90 GHz), and it was demonstrated that the relative permittivity of air with a thickness of 2mm and a sample with the relative permittivity of 2.05 and a thickness of 1mm is estimated with errors less than ±0.5% and ±0.2%, respectively. Moreover, in measuring a sample without displacing the receiving horn antenna to avoid the error in measurement, we derived an expression of the permittivity estimation for S parameters measured using a vector network analyzer, and demonstrated that the measurement of a sample without antenna displacement is valid.

Many deep convolutional neural network (CNN) inference accelerators on the field-programmable gate array (FPGA) platform have been widely adopted due to their low power consumption and high performance. In this paper, we develop the following to improve performance and power efficiency. First, we use a high bandwidth memory (HBM) to expand the bandwidth of data transmission between the off-chip memory and the accelerator. Second, a fully-pipelined manner, which consists of pipelined inter-layer computation and a pipelined computation engine, is implemented to decrease idle time among layers. Third, a multi-core architecture with shared-dual buffers is designed to reduce off-chip memory access and maximize the throughput. We designed the proposed accelerator on the Xilinx Alveo U280 platform with in-depth Verilog HDL instead of high-level synthesis as the previous works and explored the VGG-16 model to verify the system during our experiment. With a similar accelerator architecture, the experimental results demonstrate that the memory bandwidth of HBM is 13.2× better than DDR4. Compared with other accelerators in terms of throughput, our accelerator is 1.9×/1.65×/11.9× better than FPGA+HBM2 based/low batch size (4) GPGPU/low batch size (4) CPU. Compared with the previous DDR+FPGA/DDR+GPGPU/DDR+CPU based accelerators in terms of power efficiency, our proposed system provides 1.4-1.7×/1.7-12.6×/6.6-37.1× improvement with the large-scale CNN model.

The increasing attention to the interpretability of machine learning models has led to the development of methods to explain the behavior of black-box models in a post-hoc manner. However, such post-hoc approaches generate a new explanation for every new input, and these explanations cannot be checked by humans in advance. A method that selects decision rules from a finite ruleset as explanation for neural networks has been proposed, but it cannot be used for other models. In this paper, we propose a model-agnostic explanation method to find a pre-verifiable finite ruleset from which a decision rule is selected to support every prediction made by a given black-box model. First, we define an explanation model that selects the rule, from a ruleset, that gives the closest prediction; this rule works as an alternative explanation or supportive evidence for the prediction of a black-box model. The ruleset should have high coverage to give close predictions for future inputs, but it should also be small enough to be checkable by humans in advance. However, minimizing the ruleset while keeping high coverage leads to a computationally hard combinatorial problem. Hence, we show that this problem can be reduced to a weighted MaxSAT problem composed only of Horn clauses, which can be efficiently solved with modern solvers. Experimental results showed that our method found small rulesets such that the rules selected from them can achieve higher accuracy for structured data as compared to the existing method using rulesets of almost the same size. We also experimentally compared the proposed method with two purely rule-based models, CORELS and defragTrees. Furthermore, we examine rulesets constructed for real datasets and discuss the characteristics of the proposed method from different viewpoints including interpretability, limitation, and possible use cases.

Many countries are facing the aging problem caused by the growth of the elderly population. Nursing home (NH) is a common solution to long-term care for the elderly. This paper develops a simulator to model elder behavior in an NH, which considers public areas where elders interact and imitates their general, group, and special activities. Elders have their preferences to decide activities taken by them. The simulator takes account of the movement of elders and abnormal events. Based on the simulator, two seeking methods are proposed for caregivers to search lost elders efficiently, which helps them fast find out elders who may incur accidents.

Neuromorphic computing with a spiking neural network (SNN) is expected to provide a complement or alternative to deep learning in the future. The challenge is to develop optimal SNN models, algorithms, and engineering technologies for real use cases. As a potential use cases for neuromorphic computing, we have investigated a person monitoring and worker support with a video surveillance system, given its status as a proven deep neural network (DNN) use case. In the future, to increase the number of cameras in such a system, we will need a scalable approach that embeds only a few neuromorphic devices in a camera. Specifically, this will require a shallow SNN model that can be implemented in a few neuromorphic devices while providing a high recognition accuracy comparable to a DNN with the same configuration. A shallow SNN was built by converting ResNet, a proven DNN for image recognition, and a new configuration of the shallow SNN model was developed to improve its accuracy. The proposed shallow SNN model was evaluated with a few neuromorphic devices, and it achieved a recognition accuracy of more than 80% with about 1/130 less energy consumption than that of a GPU with the same configuration of DNN as that of SNN.

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.

We discuss the spectral efficiency of orthogonal frequency-division multiplexing (OFDM) signals widely adopted in practical systems from a viewpoint of their power spectral density property. Since the conventional OFDM does not make use of pulse shaping filter, its out-of-band (OOB) spectrum may not be negligible especially when the number of subcarriers is small. Thus, in practice, windowing is applied to mitigate OOB emission by smoothing the transition of consecutive OFDM symbols, but its effectiveness has not been well investigated. Furthermore, OFDM signal suffers from nonlinear distortion associated with its high signal peak-to-average power ratio (PAPR), which also leads to OOB radiation. We examine how power amplifier nonlinearity affects the spectral efficiency based on the theoretical results developed in the literature.

This paper attempts to identify which side of the road a bicycle is currently riding on using a common camera for realizing an advanced bicycle navigation system and bicycle riding safety support system. To identify the roadway area, the proposed method performs semantic segmentation on a front camera image captured by a bicycle drive recorder or smartphone. If the roadway area extends from the center of the image to the right, the bicyclist is riding on the left side of the roadway (i.e., the correct riding position in Japan). In contrast, if the roadway area extends to the left, the bicyclist is on the right side of the roadway (i.e., the incorrect riding position in Japan). We evaluated the accuracy of the proposed method on various road widths with different traffic volumes using video captured by riding bicycles in Tsuruoka City, Yamagata Prefecture, and Saitama City, Saitama Prefecture, Japan. High accuracy (>80%) was achieved for any combination of the segmentation model, riding side identification method, and experimental conditions. Given these results, we believe that we have realized an effective image segmentation-based method to identify which side of the roadway a bicycle riding is on.

To simultaneously enhance data rate and physical layer security (PLS) for low-luminance smartphone screen to camera uplink communication, space division multiplexing using high-luminance cell-size reduction arrangement is numerically analyzed and experimentally verified. The uplink consists of a low-luminance smartphone screen and an indoor telephoto camera at a long distance of 3.5 meters. The high-luminance cell-size reduction arrangement avoids the influence of spatial inter-symbol interference (ISI) and ambient light to obtain a stable low-luminance screen. To reduce the screen luminance without decreasing the screen pixel value, the arrangement reduces only the high-luminance cell area while keeping the cell spacing. In this study, two technical issues related to high-luminance cell-size reduction arrangement are solved. First, a numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more effective in reducing the spatial ISI at low luminance than the conventional low-luminance cell arrangement. Second, in view point of PLS enhancement at wide angles, symbol error rate should be low in front of the screen and high at wide angles. A numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more suitable for enhancing PLS at wide angles than the conventional low-luminance cell arrangement.

In quasi-synchronous FH multiple-access (QS-FHMA) systems, no-hit-zone frequency-hopping sequences (NHZ-FHSs) can offer interference-free FHMA performance. But, outside the no-hit-zone (NHZ), the Hamming correlation of traditional NHZ-FHZs maybe so large that the performance becomes not good. And in high-speed mobile environment, Doppler shift phenomenon will appear. In order to ensure the performance of FHMA, it is necessary to study the NHZ-FHSs in the presence of transmission delay and frequency offset. In this paper, We derive a lower bound on the maximum time-frequency two-dimensional Hamming correlation outside of the NHZ of NHZ-FHSs. The Zeng-Zhou-Liu-Liu bound is a particular situation of the new bound for frequency shift is zero.