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.

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.

We propose a framework for the integration of heterogeneous networks in human pose estimation (HPE) with the aim of balancing accuracy and computational complexity. Although many existing methods can improve the accuracy of HPE using multiple frames in videos, they also increase the computational complexity. The key difference here is that the proposed heterogeneous framework has various networks for different types of frames, while existing methods use the same networks for all frames. In particular, we propose to divide the video frames into two types, including key frames and non-key frames, and adopt three networks including slow networks, fast networks, and transfer networks in our heterogeneous framework. For key frames, a slow network is used that has high accuracy but high computational complexity. For non-key frames that follow a key frame, we propose to warp the heatmap of a slow network from a key frame via a transfer network and fuse it with a fast network that has low accuracy but low computational complexity. Furthermore, when extending to the usage of long-term frames where a large number of non-key frames follow a key frame, the temporal correlation decreases. Therefore, when necessary, we use an additional transfer network that warps the heatmap from a neighboring non-key frame. The experimental results on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed FSPose achieves a better balance between accuracy and computational complexity than the competitor method. Our source code is available at https://github.com/Fenax79/fspose.

Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.

In order to improve crop production and efficiency of farming operations, an IoT (Internet of Things) system for remote monitoring has been attracting a lot of attention. The existing studies have proposed agricultural sensing systems such that environmental information is collected from many sensor nodes installed in farmland through wireless communications (e.g., Wi-Fi, ZigBee). Especially, Low-Power Wide-Area (LPWA) is a focus as a candidate for wireless communication that enables the support of vast farmland for a long time. However, it is difficult to achieve long distance communication even when using the LPWA because a clear line of sight is difficult to keep due to many obstacles such as crops and agricultural machinery in the farmland. In addition, a sensor node cannot run permanently on batteries because the battery capacity is not infinite. On the other hand, an Unmanned Aerial Vehicle (UAV) that can move freely and stably in the sky has been leveraged for agricultural sensor network systems. By utilizing a UAV as the gateway of the sensor network, the gateway can move to the appropriate location to ensure a clear line of sight from the sensor nodes. In addition, the coverage area of the sensor network can be expanded as the UAV travels over a wide area even when short-range and ultra-low-power wireless communication (e.g., Bluetooth Low Energy (BLE)) is adopted. Furthermore, various wireless technologies (e.g., wireless power transfer, wireless positioning) that have the possibility to improve the coverage area and the lifetime of the sensor network have become available. Therefore, in this study, we propose and develop two kinds of new agricultural sensing systems utilizing a UAV and various wireless technologies. The objective of the proposed system is to provide the solution for achieving the wide-area and long-term sensing for the vast farmland. Depending on which problem is in a priority, the proposed system chooses one of two designs. The first design of the system attempts to achieve the wide-area sensing, and so it is based on the LPWA for wireless communication. In the system, to efficiently collect the environmental information, the UAV autonomously travels to search for the locations to maintain the good communication properties of the LPWA to the sensor nodes dispersed over a wide area of farmland. In addition, the second design attempts to achieve the long-term sensing, so it is based on BLE, a typical short-range and ultra-low-power wireless communication technology. In this design, the UAV autonomously flies to the location of sensor nodes and supplies power to them using a wireless power transfer technology for achieving a battery-less sensor node. Through experimental evaluations using a prototype system, it is confirmed that the combination of the UAV and various wireless technologies has the possibility to achieve a wide-area and long-term sensing system for monitoring vast farmland.

Pose estimation is a research hot spot in computer vision tasks and the key to computer perception of human activities. The core concept of human pose estimation involves describing the motion of the human body through major joint points. Large receptive fields and rich spatial information facilitate the keypoint localization task, and how to capture features on a larger scale and reintegrate them into the feature space is a challenge for pose estimation. To address this problem, we propose a multi-scale convergence network (MSCNet) with a large receptive field and rich spatial information. The structure of the MSCNet is based on an hourglass network that captures information at different scales to present a consistent understanding of the whole body. The multi-scale receptive field (MSRF) units provide a large receptive field to obtain rich contextual information, which is then selectively enhanced or suppressed by the Squeeze-Excitation (SE) attention mechanism to flexibly perform the pose estimation task. Experimental results show that MSCNet scores 73.1% AP on the COCO dataset, an 8.8% improvement compared to the mainstream CMUPose method. Compared to the advanced CPN, the MSCNet has 68.2% of the computational complexity and only 55.4% of the number of parameters.

A new zero-forcing block diagonalization (ZF-BD) scheme that enables both a more simplified ZF-BD and further increase in sum rate of MU-MIMO channels is proposed in this paper. The proposed scheme provides the improvement in BER performance for equivalent SU-MIMO channels. The proposed scheme consists of two components. First, a permuted channel matrix (PCM), which is given by moving the submatrix related to a target user to the bottom of a downlink MIMO channel matrix, is newly defined to obtain a precoding matrix for ZF-BD. Executing QR decomposition alone for a given PCM provides null space for the target user. Second, a partial MSQRD (PMSQRD) algorithm, which adopts MSQRD only for a target user to provide improvement in bit rate and BER performance for the user, is proposed. Some numerical simulations are performed, and the results show improvement in sum rate performance of the total system. In addition, appropriate bit allocation improves the bit error rate (BER) performance in each equivalent SU-MIMO channel. A successive interference cancellation is applied to achieve further improvement in BER performance of user terminals.

High-frequency wireless communication is drawing attention because of its potential to actualize huge transmission capacity in the next generation wireless system. The use of high-frequency bands requires dense deployment of access points to compensate for significant distance attenuation and diffraction loss. Dense deployment of access points in a mobility environment triggers an increase in the frequency of handover because the number of candidate access points increases. Therefore, simple handover schemes are needed. High-frequency wireless systems enable station position to be determined using their wideband and highly directional communication signals. Thus, simple handover based on position information estimated using the communication signal is possible. Interruptions caused by handover are also a huge barrier to actualizing stable high-frequency wireless communications. This paper proposes a seamless handover scheme using multiple radio units. This paper evaluates the combination of simple handover and the proposed scheme based on experiments using a formula racing car representing the fastest high-speed mobility environment. Experimental results show that seamless handover and high-speed wireless transmission over 200Mbps are achieved over a 400-m area even at station velocities of greater than 200km/h.

We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.

To overcome the challenge in micro-expression recognition that it only emerge in several small facial regions with low intensity, some researchers proposed facial region partition mechanisms and introduced group sparse learning methods for feature selection. However, such methods have some shortcomings, including the complexity of region division and insufficient utilization of critical facial regions. To address these problems, we propose a novel Group Sparse Reduced Rank Tensor Regression (GSRRTR) to transform the fearure matrix into a tensor by laying blocks and features in different dimensions. So we can process grids and texture features separately and avoid interference between grids and features. Furthermore, with the use of Tucker decomposition, the feature tensor can be decomposed into a product of core tensor and a set of matrix so that the number of parameters and the computational complexity of the scheme will decreased. To evaluate the performance of the proposed micro-expression recognition method, extensive experiments are conducted on two micro expression databases: CASME2 and SMIC. The experimental results show that the proposed method achieves comparable recognition rate with less parameters than state-of-the-art methods.

Authenticated-Encryption with Associated-Data (AEAD) plays an important role in guaranteeing confidentiality, integrity, and authenticity in network communications. To meet the requirements of high-performance applications, several AEADs make use of AES New Instructions (AES-NI), which can conduct operations of AES encryption and decryption dramatically fast by hardware accelerations. At SAC 2013, Wu and Preneel proposed an AES-based AEAD scheme called AEGIS-128/128L/256, to achieve high-speed software implementation. At FSE 2016, Jean and Nikolić generalized the construction of AEGIS and proposed more efficient round functions. At ToSC 2021, Sakamoto et al. further improved the constructions of Jean and Nikolić, and proposed an AEAD scheme called Rocca for beyond 5G. In this study, we first evaluate the security of the initialization phases of Rocca and AEGIS family against differential and integral attacks using MILP (Mixed Integer Linear Programming) tools. Specifically, according to the evaluation based on the lower bounds for the number of active S-boxes, the initialization phases of AEGIS-128/128L/256 are secure against differential attacks after 4/3/6 rounds, respectively. Regarding integral attacks, we present the integral distinguisher on 6 rounds and 6/5/7 rounds in the initialization phases of Rocca and AEGIS-128/128L/256, respectively. Besides, we evaluate the round function of Rocca and those of Jean and Nikolić as cryptographic permutations against differential, impossible differential, and integral attacks. Our results indicate that, for differential attacks, the growth rate of increasing the number of active S-boxes in Rocca is faster than those of Jean and Nikolić. For impossible differential and integral attacks, we show that the round function of Rocca achieves the sufficient level of the security against these attacks in smaller number of rounds than those of Jean and Nikolić.

This paper addresses short-length sparse superposition codes (SSCs) over the additive white Gaussian noise channel. Damped approximate message-passing (AMP) is used to decode short SSCs with zero-mean independent and identically distributed Gaussian dictionaries. To design damping factors in AMP via deep learning, this paper constructs deep-unfolded damped AMP decoding networks. An annealing method for deep learning is proposed for designing nearly optimal damping factors with high probability. In annealing, damping factors are first optimized via deep learning in the low signal-to-noise ratio (SNR) regime. Then, the obtained damping factors are set to the initial values in stochastic gradient descent, which optimizes damping factors for slightly larger SNR. Repeating this annealing process designs damping factors in the high SNR regime. Numerical simulations show that annealing mitigates fluctuation in learned damping factors and outperforms exhaustive search based on an iteration-independent damping factor.

This study focuses on creating arithmetical stories as a sub-task of problem posing and proposes a game named “Tri-prop scrabble” as a learning environment based on a fusion method of learning and game. The problem-posing ability has a positive relationship with mathematics achievement and understanding the mathematical structure of problems. In the proposed game, learners are expected to experience creating and concatenating various arithmetical stories by integrating simple sentences. The result of a preliminary feasibility study shows that the participants were able to pose and concatenate a variety of types of arithmetic stories and accept this game is helpful for learning arithmetic word problems.

In this paper, we report the recent progress in visible light positioning and communication systems using light-emitting diodes (LEDs). Due to the wide deployment of LEDs for indoor illumination, visible light positioning (VLP) and visible light communication (VLC) using existing LEDs fixtures have attracted great attention in recent years. Here, we review our recent works on visible light positioning and communication, including image sensor-based VLP, photodetector-based VLP, integrated VLC and VLP (VLCP) systems, and heterogeneous radio frequency (RF) and VLC (RF/VLC) systems.

This paper proposes multi-input physical layer network coding (multi-input PLNC) for high speed wireless communication in two-dimensional wireless multihop networks. In the proposed PLNC, all the terminals send their packets simultaneously for the neighboring relays to maximize the network throughput in the first slot, and all the relays also do the same to the neighboring terminals in the second slot. Those simultaneous signal transmissions cause multiple signals to be received at the relays and the terminals. Signal reception in the multi-input PLNC uses multichannel filtering to mitigate the difficulties caused by the multiple signal reception, which enables the two-input PLNC to be applied. In addition, a non-linear precoding is proposed to reduce the computational complexity of the signal detection at the relays and the terminals. The proposed multi-input PLNC makes all the terminals exchange their packets with the neighboring terminals in only two time slots. The performance of the proposed multi-input PLNC is confirmed by computer simulation. The proposed multi-input physical layer network coding achieves much higher network throughput than conventional techniques in a two-dimensional multihop wireless network with 7 terminals. The proposed multi-input physical layer network coding attains superior transmission performance in wireless hexagonal multihop networks, as long as more than 6 antennas are placed on the terminals and the relays.

Defect prediction approaches have been greatly contributing to software quality assurance activities such as code review or unit testing. Just-in-time defect prediction approaches are developed to predict whether a commit is a defect-inducing commit or not. Prior research has shown that commit-level prediction is not enough in terms of effort, and a defective commit may contain both defective and non-defective files. As the defect prediction community is promoting fine-grained granularity prediction approaches, we propose our novel class-level prediction, which is finer-grained than the file-level prediction, based on the files of the commits in this research. We designed our model for Python projects and tested it with ten open-source Python projects. We performed our experiment with two settings: setting with product metrics only and setting with product metrics plus commit information. Our investigation was conducted with three different classifiers and two validation strategies. We found that our model developed by random forest classifier performs the best, and commit information contributes significantly to the product metrics in 10-fold cross-validation. We also created a commit-based file-level prediction for the Python files which do not have the classes. The file-level model also showed a similar condition as the class-level model. However, the results showed a massive deviation in time-series validation for both levels and the challenge of predicting Python classes and files in a realistic scenario.

Supersingular isogeny Diffie-Hellman (SIDH) is attractive for its relatively small public key size, but it is still unsatisfactory due to its efficiency, compared to other post-quantum proposals. In this paper, we focus on the performance of SIDH when the starting curve is E6 : y2 = x3 + 6x2 + x, which is fixed in Round-3 SIKE implementation. Inspired by previous works [1], [2], we present several tricks to accelerate key generation of SIDH and each process of SIKE. Our experimental results show that the performance of this work is at least 6.09% faster than that of the SIKE implementation, and we can further improve the performance when large storage is available.

This paper considers tracking of a non-cooperative emitter based on a single sensor. To this end, the direct target motion analysis (DTMA) approach, where the target state is straightforwardly achieved from the received signal, is exploited. In order to achieve observability, the sensor has to perform a maneuver relative to the emitter. By suitably building an approximated likelihood function, the unscented Kalman filter (UKF), which is able to work under high nonlinearity of the measurement model, is adopted to recursively estimate the target state. Besides, the posterior Cramér-Rao bound (PCRB) of DTMA, which can be used as performance benchmark, is also achieved. The effectiveness of proposed method is verified via simulation experiments.

Person images captured by surveillance cameras in real scenes often have low resolution (LR), which suffers from severe degradation in recognition performance when matched with pre-stocked high-resolution (HR) images. There are existing methods which typically employ super-resolution (SR) techniques to address the resolution discrepancy problem in person re-identification (re-ID). However, SR techniques are intended to enhance the human eye visual fidelity of images without caring about the recovery of pedestrian identity information. To cope with this challenge, we propose an orthogonal depth feature decomposition network. And we decompose pedestrian features into resolution-related features and identity-related features who are orthogonal to each other, from which we design the identity-preserving loss and resolution-invariant loss to ensure the recovery of pedestrian identity information. When compared with the SOTA method, experiments on the MLR-CUHK03 and MLR-VIPeR datasets demonstrate the superiority of our method.

Graphene has been expected as an alternative material for copper interconnects in which resistance increases and reliability deteriorates in nanoscale. While the principle advantages are verified by simulations and experiments, they have not been put into practical use due to the immaturity of the manufacturing process leading to mass production. On the other hand, recent steady progress in the fabrication process has increased the possibility of practical application. In this paper, I will review the recent advances and the latest prospects for conductor applications of graphene centered on interconnects. The possibility of further application utilizing the unique characteristics of graphene is discussed.