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.

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.

An electroactive supercoiled polymer artificial muscle, which is made from a conductive sewing thread using self-coiling caused by inserting a twist with a hanged appropriate weight, is 1/4-1/3 of the thread in length. Therefore, it is necessary to move the weight vertically about two or three times as long as the desired electroactive supercoiled polymer artificial muscle, resulting in a large vertical dimension of the fabrication equipment. This study has attempted to solve this problem by using constant-load springs that enable horizontal table-top fabrication equipment. It has been also demonstrated that inserting a twist into the bundled threads results in a strong electroactive supercoiled polymer artificial muscle.

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.

We have developed multilayered polymer-based inverted organic light emitting diodes (iOLED) using transfer-printing and push-coating techniques. We obtained the higher efficiency and lower operation voltage with push-coated blue light emitting polymer and hole transporting polymer than the devices with spin-coated film. The β-phase obtained for blue emitting layer is attributable to the improved performance of relatively efficient bule and white iOLEDs with an external quantum efficiency (EQE) of above 2%.

Lead bromide-based perovskite organic-inorganic quantum-well films incorporated polycyclic aromatic chromophores into the organic layer (in other words, hybrid quantum-wells combined lead bromide semiconductor and organic semiconductors) were prepared by use of the spin-coating technique from the DMF solution in which PbBr2 and alkyl ammonium bromides which were linked polycyclic aromatics, pyrene, phenanthrene, and anthracene. When the pyrene-linked methyl ammonium bromide, which has a relatively small molecular cross-section with regard to the inorganic semiconductor plane, was employed, a lead bromide-based perovskite structure was successfully formed in the spin-coated films. When the phenanthrene-linked and anthracene-linked ammonium bromides, whose chromophore have large molecular cross-sections, were employed, lead bromide-based perovskite structures were not formed. However, the introduction of longer alkyl chains into the aromatics-linked ammonium bromides made it possible to form the perovskite structure.

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.

This paper proposes an efficient reference image sharing method for the image-division parallel video encoding architecture. This method efficiently reduces the amount of data transfer by using pre-transfer with area prediction and on-demand transfer with a transfer management table. Experimental results show that the data transfer can be reduced to 19.8-35.3% of the conventional method on average without major degradation of coding performance. This makes it possible to reduce the required bandwidth of the inter-chip transfer interface by saving the amount of data transfer.

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.

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.

One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.

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.

Fully homomorphic encryption (FHE) enables secret computations. Users can perform computation using data encrypted with FHE without decryption. Uploading private data without encryption to a public cloud has the risk of data leakage, which makes many users hesitant to utilize a public cloud. Uploading data encrypted with FHE avoids this risk, while still providing the computing power of the public cloud. In many cases, data are stored in HDDs because the data size increases significantly when FHE is used. One important data analysis is Apriori data mining. In this application, two files are accessed alternately, and this causes long-distance seeking on its HDD and low performance. In this paper, we propose a new striping layout with reservations for write areas. This method intentionally fragments files and arranges blocks to reduce the distance between blocks in a file and another file. It reserves the area for intermediate files of FHE Apriori. The performance of the proposed method was evaluated based on the I/O processing of a large FHE Apriori, and the results showed that the proposed method could improve performance by up to approximately 28%.

A Boolean network (BN) is well known as a discrete model for analysis and control of complex networks such as gene regulatory networks. Since complex networks are large-scale in general, it is important to consider model reduction. In this paper, we consider model reduction that the information on fixed points (singleton attractors) is preserved. In model reduction studied here, the interaction graph obtained from a given BN is utilized. In the existing method, the minimum feedback vertex set (FVS) of the interaction graph is focused on. The dimension of the state is reduced to the number of elements of the minimum FVS. In the proposed method, we focus on complement and absorption laws of Boolean functions in substitution operations of a Boolean function into other one. By simplifying Boolean functions, the dimension of the state may be further reduced. Through a numerical example, we present that by the proposed method, the dimension of the state can be reduced for BNs that the dimension of the state cannot be reduced by the existing method.

Energy management in buildings is vital for reducing electricity costs and maximizing the comfort of occupants. Excess solar generation can be used by combining a battery storage system and a heating, ventilation, and air-conditioning (HVAC) system so that occupants feel comfortable. Despite several studies on the scheduling of appliances, batteries, and HVAC, comprehensive and time scalable approaches are required that integrate such predictive information as renewable generation and thermal comfort. In this paper, we propose an thermal-comfort aware online co-scheduling framework that incorporates optimal energy scheduling and a prediction model of PV generation and thermal comfort with the model predictive control (MPC) approach. We introduce a photovoltaic (PV) energy nowcasting and thermal-comfort-estimation model that provides useful information for optimization. The energy management problem is formulated as three coordinated optimization problems that cover fast and slow time-scales by considering predicted information. This approach reduces the time complexity without a significant negative impact on the result's global nature and its quality. Experimental results show that our proposed framework achieves optimal energy management that takes into account the trade-off between electricity expenses and thermal comfort. Our sensitivity analysis indicates that introducing a battery significantly improves the trade-off relationship.

A hopping rover is a robot that can move in low gravity planets by the characteristic motion called the hopping motion. For its autonomous explorations, the so-called SLAM (Simultaneous Localization and Mapping) is a basic function. SLAM is the combination of estimating the position of a robot and creating a map of an unknown environment. Most conventional methods of SLAM are based on odometry to estimate the position of the robot. However, in the case of the hopping rover, the error of odometry becomes considerably large because its hopping motion involves unpredictable bounce on the rough ground on an unexplored planet. Motivated by the above discussion, this paper addresses a problem of finding an optimal movement of the hopping rover for the estimation performance of the SLAM. For the problem, we first set the model of the SLAM system for the hopping rover. The problem is formulated as minimizing the expectation of the estimation error at a pre-specified time with respect to the sequence of control inputs. We show that the optimal input sequence tends to force the final position to be not at the landmark but in front of the landmark, and furthermore, the optimal input sequence is constant on the time interval for optimization.