Triboelectric generator is attracting much attention as a power source of electronics application. Electromotive force induced by rubbing is a key for triboelectric generator. From dielectric physics point of view, there are two microscopic origins for electromotive force, i.e., electronic charge displacement and dipolar rotation. A new way for evaluating these two origins is an urgent task. We have been developing an optical second-harmonic generation (SHG) technique as a tool for probing charge displacement and dipolar alignment, selectively, by utilizing wavelength dependent response of SHG to the two origins. In this paper, an experimental way that identifies polarity of electronic charge displacement, i.e., positive charge and negative charge, is proposed. Results showed that the use of local oscillator makes it possible to identify the polarity of charges by means of SHG. As an example, positive and negative charge distribution created by rubbing polyimide surface is illustrated.

In vacuum-deposited film composed of organic polar molecules, polarization charges appear on the film surface owing to spontaneous orientation of the molecule. Because its density (σpol) determines an amount of accumulation charge (σacc) in organic light-emitting diodes and output power in polar molecular-based vibrational energy generators (VEGs), control of molecular orientation is highly required. Recently, several groups have reported that dipole-dipole interaction between polar molecules induces anti-parallel orientation which does not contribute to σpol. In other words, perturbation inducing the attenuation of the dipole interaction is needed to enhance σpol. In this study, to investigate an effect of light irradiation on σpol, we prepared 1,3,5-tris(1-phenyl-1H-benzimidazol-2-yl)benzene (TPBi) film under illumination during its deposition, and evaluated the σacc in TPBi-based bilayer device, which equals to σpol. We found that the σacc was increased by light irradiation, indicating that average orientation of TPBi is enhanced. These results suggest that light irradiation during device fabrication is promising process for organic electronic devices including polar molecule-based VEGs.

In this study, two modification methods that employ graphene-coated carbon fiber woven fabric (GCFC) as an electrode and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) as a mediator were used to evaluate cathode performance. In addition, a prototype of an atmosphere-exposed ascorbic-acid enzyme biofuel cell (AAEBFC) consisting of an improved GCFC cathode and ABTS was evaluated. No modification was made in the anode region, and only the cathode region was coated with the enzyme of bilirubin oxidase (BOD). As a result of implementing an ABTS-modified cathode in the AAEBFC, an output of 721μW/cm2 was obtained at 0.189V. When the gel thickness was changed, an output of 1200μW/cm2 was obtained at 0.17V. To the best of our knowledge, this is currently the highest reported output for an AAEBFC fueled by ascorbic acid.

In recent years, wireless LANs (WLANs) are closely deployed which means they interfere with each other. Mobile stations (MSs) like smart phones that connect to such WLANs are also increasing. In such interfering environments, radio interference frequency depends on MS position. In addition, as MSs and their applications become diverse, frame generation rates from MSs are also becoming various. Thus, sufficient frame transmission opportunities should be assigned to MSs regardless of their radio interference frequencies and frame generation rates. One key technology to deal with this issue is uplink orthogonal frequency division multiple access (OFDMA) transmission introduced in IEEE 802.11ax. However, existing works do not consider the differences of the interference frequencies and frame generation rates among MSs in an integrated manner. This paper proposes an uplink frame transmission method for interfering WLAN environments that effectively uses the OFDMA transmission to assign enough transmission opportunities to MSs regardless of their own interference frequencies and frame generation rates, while efficiently using the channel resource. Considering the combined problem, this proposed method allocates resource units (RUs), created by dividing the channel, to MSs. In addition, based on a mathematical analysis of required frame transmission duration, the proposed method flexibly selects the OFDMA transmission or conventional frame transmission with CSMA/CA, which is also not considered in the existing works.

Biofuel cells (BFCs) using graphene-coated carbon fiber cloth electrodes and glucose gel fuel were fabricated and evaluated. A new structure using fuel gel, in which the anode was embedded in gel and the cathode was exposed to the atmosphere, was adopted. Air-exposed biofuel cells using gel have already been reported, however, adhesion between the anode and the gel was improved by the proposed structure. In addition, the enlargement of the gel area prevented its drying. These innovations improved the power density and lifetime of the BFCs. The anode was modified with a glucose oxidase (GOD) enzyme and a mediator (ferrocene) and the cathode was modified with a bilirubin oxidase enzyme. The power density of the proposed structure was 176.4 µW/cm2 at 0.19 V, which was approximately 3.8 times higher than that of BFCs using liquid fuel (45.9 µW/cm2).

The performance of video action recognition has improved significantly in recent decades. Current recognition approaches mainly utilize convolutional neural networks to acquire video feature representations. In addition to the spatial information of video frames, temporal information such as motions and changes is important for recognizing videos. Therefore, the use of convolutions in a spatiotemporal three-dimensional (3D) space for representing spatiotemporal features has garnered significant attention. Herein, we introduce recent advances in 3D convolutions for video action recognition.

In this paper, we evaluate a prediction method of regions including dislocation clusters which are crystallographic defects in a photoluminescence (PL) image of multicrystalline silicon wafers. We applied a method of a transfer learning of the convolutional neural network to solve this task. For an input of a sub-region image of a whole PL image, the network outputs the dislocation cluster regions are included in the upper wafer image or not. A network learned using image in lower wafers of the bottom of dislocation clusters as positive examples. We experimented under three conditions as negative examples; image of some depth wafer, randomly selected images, and both images. We examined performances of accuracies and Youden's J statistics under 2 cases; predictions of occurrences of dislocation clusters at 10 upper wafer or 20 upper wafer. Results present that values of accuracies and values of Youden's J are not so high, but they are higher results than ones of bag of features (visual words) method. For our purpose to find occurrences dislocation clusters in upper wafers from the input wafer, we obtained results that randomly select condition as negative examples is appropriate for 10 upper wafers prediction, since its results are better than other negative examples conditions, consistently.

Various cross-modal retrieval methods that can retrieve images related to a query sentence without text annotations have been proposed. Although a high level of retrieval performance is achieved by these methods, they have been developed for a single domain retrieval setting. When retrieval candidate images come from various domains, the retrieval performance of these methods might be decreased. To deal with this problem, we propose a new domain adaptive cross-modal retrieval method. By translating a modality and domains of a query and candidate images, our method can retrieve desired images accurately in a different domain retrieval setting. Experimental results for clipart and painting datasets showed that the proposed method has better retrieval performance than that of other conventional and state-of-the-art methods.

In medical fields, detecting traumatic bleedings has always been a difficult task due to the small size, low contrast of targets and large number of images. In this work we propose an automatic traumatic bleeding detection approach from contrast enhanced CT images via deep CNN networks, containing segmentation process and classification process. CT values of DICOM images are extracted and processed via three different window settings first. Small 3D patches are cropped from processed images and segmented by a 3D CNN network. Then segmentation results are converted to point cloud data format and classified by a classifier. The proposed pre-processing approach makes the segmentation network be able to detect small and low contrast targets and achieve a high sensitivity. The additional classification network solves the boundary problem and short-sighted problem generated during the segmentation process to further decrease false positives. The proposed approach is tested with 3 CT cases containing 37 bleeding regions. As a result, a total of 34 bleeding regions are correctly detected, the sensitivity reaches 91.89%. The average false positive number of test cases is 1678. 46.1% of false positive predictions are decreased after being classified. The proposed method is proved to be able to achieve a high sensitivity and be a reference of medical doctors.

Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.

Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.

Various graph algorithms have been developed with multiple random walks, the movement of several independent random walkers on a graph. Designing an efficient graph algorithm based on multiple random walks requires investigating multiple random walks theoretically to attain a deep understanding of their characteristics. The first meeting time is one of the important metrics for multiple random walks. The first meeting time on a graph is defined by the time it takes for multiple random walkers to meet at the same node in a graph. This time is closely related to the rendezvous problem, a fundamental problem in computer science. The first meeting time of multiple random walks has been analyzed previously, but many of these analyses focused on regular graphs. In this paper, we analyze the first meeting time of multiple random walks in arbitrary graphs and clarify the effects of graph structures on expected values. First, we derive the spectral formula of the expected first meeting time on the basis of spectral graph theory. Then, we examine the principal component of the expected first meeting time using the derived spectral formula. The clarified principal component reveals that (a) the expected first meeting time is almost dominated by $n/(1+d_{ m std}^2/d_{ mavg}^2)$ and (b) the expected first meeting time is independent of the starting nodes of random walkers, where n is the number of nodes of the graph. davg and dstd are the average and the standard deviation of weighted node degrees, respectively. Characteristic (a) is useful for understanding the effect of the graph structure on the first meeting time. According to the revealed effect of graph structures, the variance of the coefficient dstd/davg (degree heterogeneity) for weighted degrees facilitates the meeting of random walkers.

For 360-degree video streaming, a 360-degree video is divided into segments temporally (i.e. some seconds). Each segment consists of multiple video tiles spatially. In this paper, we propose a tile quality selection method for tile-based video streaming. The proposed method suppresses the spatial quality variation within the viewport caused by a change of the viewport region due to user head movement. In the proposed method, the client checks whether the difference in quality level between the viewport and the region around the viewport is large, and if so, reduces it when assigning quality levels. Simulation results indicate that when the segment length is long, quality variation can be suppressed without significantly reducing the perceived video quality (in terms of bitrate). In particular, the quality variation within the viewport can be greatly suppressed. Furthermore, we verify that the proposed method is effective in reducing quality variation within the viewport and across segments without changing the total download size.

In this paper, we present a novel portrait impression estimation method using nine pairs of semantic impression words: bitter-majestic, clear-pure, elegant-mysterious, gorgeous-mature, modern-intellectual, natural-mild, sporty-agile, sweet-sunny, and vivid-dynamic. In the first part of the study, we analyzed the relationship between the facial features in deformed portraits and the nine semantic impression word pairs over a large dataset, which we collected by a crowdsourcing process. In the second part, we leveraged the knowledge from the results of the analysis to develop a ranking network trained on the collected data and designed to estimate the semantic impression associated with a portrait. Our network demonstrated superior performance in impression estimation compared with current state-of-the-art methods.

In this study, we devised a biofuel cell (BFC) by impregnating sheet-like cellulose nanofiber (CNF) with liquid fuel (fructose) and sandwiching it with the electrodes, making the structure simple and compact. CNF was considered as a suitable material for BFC because it is biocompatible, has a large specific surface area, and exhibits excellent properties as a catalyst and an adsorbent. In this BFC device, graphene-coated carbon fiber woven cloth (GCFC) was used as the material for preparing the electrodes, and the amount of enzyme modification on the surface of each electrode was enhanced. Further, as the distance between the electrodes was same as the thickness of the sheet-shaped CNF, it facilitated the exchange of protons between the electrodes. Moreover, the cathode, which requires an oxidation reaction, was exposed to the atmosphere to enhance the oxygen uptake. The maximum power density of the CNF-type BFC was recorded as 114.5 µW/cm2 at a voltage of 293 mV. This is more than 1.5 times higher than that of the liquid-fuel-type BFC. When measured after 24 h, the maximum power density was recorded as 44.9 µW/cm2 at 236 mV, and the output was maintained at 39% of that observed at the beginning of the measurement. However, it is not the case with general BFCs, where the power generation after 24 h is less than 5%. Therefore, the CNF-type BFCs have a longer lifespan and are fuel efficient.

The hybrid precoding (HP) technique has been widely considered as a promising approach for millimeter wave communication systems. In general, the existing HP structure with a complicated high-resolution phase shifter network can achieve near-optimal spectral efficiency, however, it involves high energy consumption. The HP architecture with an energy-efficient switch network can significantly reduce the energy consumption. To achieve maximum energy efficiency, this paper focuses on the HP architecture with switch network and considers a novel adaptive analog network HP structure for such mmWave MIMO systems, which can provide potential array gains. Moreover, a multiuser adaptive coordinate update algorithm is proposed for the HP design problem of this new structure. Simulation results verify that our proposed design can achieve better energy efficiency than other recently proposed HP schemes when the number of users is small.

We previously developed a new terahertz (THz) wave detection method that utilizes the effect of nonlinear optical (NLO) polymers. The new method provided us with a gapless detection, a wide detection bandwidth, and a simpler optical geometry in the THz wave detection. In this paper, polarization dependences in THz wave detection by the Stark effect were investigated. The projection model was employed to analyze the polarization dependences and the consistency with experiments was observed qualitatively, surely supporting the use of the first-order Stark effect in this method. The relations between THz wave detection by the Stark effect and Stark spectroscopy or electroabsorption spectroscopy are also discussed.

In this paper, we propose a direct digital RF transmitter with a 1-bit band-pass delta-sigma modulator (BP-DSM) that uses high order image components of the 7th Nyquist zone in Manchester coding for microwave and milimeter wave application. Compared to the conventional non-return-to-zero (NRZ) coding, in which the high order image components of 1-bit BP-DSM attenuate severely in the form of sinc function, the proposed 1-bit direct digital RF transmitter in Manchester code can improve the output power and signal-to-noise ratio (SNR) of the image components at specific (4n-1)th and (4n-2)th Nyquist Zone, which is confirmed by calculating of the power spectral density. Measurements are made to compare three types of 1-bit digital-to-analog converter (DAC) signal in output power and SNR; NRZ, 50% duty return-to-zero (RZ) and Manchester coding. By using 1 Vpp/8Gbps DAC output, 1-bit signals in Manchester coding show the highest output power of -20.3dBm and SNR of 40.3dB at 7th Nyquist Zone (26GHz) in CW condition. As a result, compared to NRZ and RZ coding, at 7th Nyquist zone, the output power is improved by 8.1dB and 6dB, respectively. Meanwhile, the SNR is improved by 7.6dB and 4.9dB, respectively. In 5Mbps-QPSK condition, 1-bit signals in Manchester code show the lowest error vector magnitude (EVM) of 2.4% and the highest adjacent channel leakage ratio (ACLR) of 38.2dB with the highest output power of -18.5dBm at 7th Nyquist Zone (26GHz), respectively, compared to the NRZ and 50% duty RZ coding. The measurement and simulation results of the image component of 1-bit signals at 7th Nyquist Zone (26GHz) are consistent with the calculation results.

We fabricated silicon nanowires (SiNWs) using a metal-catalyzed electroless etching method, which is known to be a low-cost and simple technique. The SiNW arrays with a length of 540 nm were used as a substrate of SiNWs/PEDOT:PSS hybrid solar cell. Furthermore, gold nanoparticles (AuNPs) were used to improve the light absorption of the device due to localized surface plasmon excitation. The results show that the short-circuit current density and the power conversion efficiency increased from 22.1 mA/cm2 to 26.0 mA/cm2 and 6.91% to 8.56%, respectively. The advantage of a higher interface area between the organic and inorganic semiconductors was established by using SiNW arrays and higher absorption light incorporated with AuNPs for improving the performance of the developed solar cell.

In this paper, we explore the physical layer security of an Internet of Things (IoT) network comprised of multiple relay-user pairs in the presence of multiple malicious eavesdroppers and channel estimation error (CEE). In order to guarantee secure transmission with channel estimation error, we propose a channel estimation error oriented joint relay-user pair and friendly jammer selection (CEE-JRUPaFJS) scheme to improve the physical layer security of IoT networks. For the purpose of comparison, the channel estimation error oriented traditional round-robin (CEE-TRR) scheme and the channel estimation error oriented traditional pure relay-user pair selection (CEE-TPRUPS) scheme are considered as benchmark schemes. The exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the CEE-TRR and CEE-TPRUPS schemes as well as the CEE-JRUPaFJS scheme are derived over Rayleigh fading channels, which are employed to characterize network reliability and security, respectively. Moreover, the security-reliability tradeoff (SRT) is analyzed as a metric to evaluate the tradeoff performance of CEE-JRUPaFJS scheme. It is verified that the proposed CEE-JRUPaFJS scheme is superior to both the CEE-TRR and CEE-TPRUPS schemes in terms of SRT, which demonstrates our proposed CEE-JRUPaFJS scheme are capable of improving the security and reliability performance of IoT networks in the face of multiple eavesdroppers. Moreover, as the number of relay-user pairs increases, CEE-TPRUPS and CEE-JRUPaFJS schemes offer significant increases in SRT. Conversely, with an increasing number of eavesdroppers, the SRT of all these three schemes become worse.