Effects of Al thickness in Ti/Al/Ti/Au ohmic contact on AlGaN/GaN heterostructures are studied. Samples having Al thickness of 30, 90 and 120 nm in Ti/Al/Ti/Au have been investigated by electrical and X-ray photoelectron spectroscopy (XPS) depth profile analysis. It is found that thick Al samples show lower resistance and formation of Al-based alloy under the oxidized Al layer.

Power line communication (PLC) provides a flexible-access, wide-distribution, and low-cost communication solution for distribution network services. However, the PLC self-organizing networking in distribution network faces several challenges such as diversified data transmission requirements guarantee, the contradiction between long-term constraints and short-term optimization, and the uncertainty of global information. To address these challenges, we propose a backpressure learning-based data transmission reliability-aware self-organizing networking algorithm to minimize the weighted sum of node data backlogs under the long-term transmission reliability constraint. Specifically, the minimization problem is transformed by the Lyapunov optimization and backpressure algorithm. Finally, we propose a backpressure and data transmission reliability-aware state-action-reward-state-action (SARSA)-based self-organizing networking strategy to realize the PLC networking optimization. Simulation results demonstrate that the proposed algorithm has superior performances of data backlogs and transmission reliability.

Makeup transfer is the process of applying the makeup style from one picture (reference) to another (source), allowing for the modification of characters’ makeup styles. To meet the diverse makeup needs of individuals or samples, the makeup transfer framework should accurately handle various makeup degrees, ranging from subtle to bold, and exhibit intelligence in adapting to the source makeup. This paper introduces a “3-level” adaptive makeup transfer framework, addressing facial makeup through two sub-tasks: 1. Makeup adaptation, utilizing feature descriptors and eyelid curve algorithms to classify 135 organ-level face shapes; 2. Makeup transfer, achieved by learning the reference picture from three branches (color, highlight, pattern) and applying it to the source picture. The proposed framework, termed “Face Shape Adaptive Makeup Transfer” (FSAMT), demonstrates superior results in makeup transfer output quality, as confirmed by experimental results.

We present a novel adversarial, end-to-end framework based on Creative-GAN to generate artistic music conditioned on dance videos. Our proposed framework takes the visual and motion posture data as input, and then adopts a quantized vector as the audio representation to generate complex music corresponding to input. However, the GAN algorithm just imitate and reproduce works what humans have created, instead of generating something new and creative. Therefore, we newly introduce Creative-GAN, which extends the original GAN framework to two discriminators, one is to determine whether it is real music, and the other is to classify music style. The paper shows that our proposed Creative-GAN can generate novel and interesting music which is not found in the training dataset. To evaluate our model, a comprehensive evaluation scheme is introduced to make subjective and objective evaluation. Compared with the advanced methods, our experimental results performs better in measureing the music rhythm, generation diversity, dance-music correlation and overall quality of generated music.

Deep learning techniques are used to transform the style of images and produce diverse images. In the text style transformation field, many previous studies attempted to generate stylized text using deep learning networks. However, to achieve multiple style transformations for text images, the methods proposed in previous studies require learning multiple networks or cannot be guided by style images. Thus, in this study we focused on multistyle transformation of text images using style images to guide the generation of results. We propose a multiple-style transformation network for text style transfer, which we refer to as the Multi-Style Shape Matching GAN (Multi-Style SMGAN). The proposed method generates multiple styles of text images using a single model by training the model only once, and allows users to control the text style according to style images. The proposed method implements conditions to the network such that all styles can be distinguished effectively in the network, and the generation of each styled text can be controlled according to these conditions. The proposed network is optimized such that the conditional information can be transmitted effectively throughout the network. The proposed method was evaluated experimentally on a large number of text images, and the results show that the trained model can generate multiple-style text in realtime according to the style image. In addition, the results of a user survey study indicate that the proposed method produces higher quality results compared to existing methods.

In the field of micro-LED displays consisting of UV or Blue-LED arrays and phosphors, where the chips used are very small, particle size of phosphors must be small to suppress variation in hue for each pixel. Especially, there is a strong demand for a red phosphor with small particle sizes. However, quantum yields of inorganic phosphors decrease as particles size of phosphors get smaller. On the other hand, in the case of organic phosphors and complexes, quantum yields don't decrease when particle size gets smaller because each molecule has a function of absorbing and emitting light. We focus on Eu(III) complexes as candidates of red phosphors for micro-LED displays because their color purities of photoluminescence spectra are high, and have been tried to enhance photoluminescence intensity by coordinating non-ionic ligand, specifically, newly designed phosphine oxide ligands. Non-ionic ligands have generally less influential on properties of complexes compared with ionic ligands, but have a high degree of flexibility in molecular design. We found novel molecular design concept of phosphine oxide ligands to enhance photoluminescence properties of Eu(III) complexes. This time, novel dinuclear Eu(III)-β-diketonates with a branched tetraphosphine tetraoxide ligand, TDPBPO and TDPPPO, were developed. They are designed to have two different phosphine oxide portions; one has aromatic substituents and the other has no aromatic substituent. TDPBPO and TDPPPO ligands have functions of increasing absolute quantum yields of Eu(III)-β-diketonates. Eu(III)-β-diketonates with branched tetraphosphine tetraoxide ligands have sharp red emissions and excellent quantum yields, and are promising candidates for micro LED displays, security media, and sensing for their pure and strong photoluminescence intensity.

This paper presents an analysis-based design method for designing the class-Φ22 wireless power transfer (WPT) system, taking its subsystems as a whole into account. By using the proposed design method, it is possible to derive accurate design values which can make sure the class-E Zero-Voltage-Switching/Zero-Derivative-Switching (ZVS/ZDS) to obtain without applying any tuning processes. Additionally, it is possible to take the effects of the switch on resistance, diode forward voltage drop, and equivalent series resistances (ESRs) of all passive elements on the system operations into account. Furthermore, design curves for a wide range of parameters are developed and organized as basic data for various applications. The validities of the proposed design procedure and derived design curves are confirmed by LTspice simulation and circuit experiment. In the experimental measurements, the class-Φ22 WPT system achieves 78.8% power-transmission efficiency at 6.78MHz operating frequency and 7.96W output power. Additionally, the results obtained from the LTspice simulation and laboratory experiment show quantitative agreements with the analytical predictions, which indicates the accuracy and validity of the proposed analytical method and design curves given in this paper.

A high-efficiency uniform/selective heating microwave oven was developed. Because the power amplifier requires high-efficiency characteristics to function as a microwave source, a free-standing Gallium Nitride (GaN) substrate was applied in this study. By applying a harmonic tuning circuit, an output power of 71 W and PAE of 73% were achieved in pulsed operation, and an output power of 63 W and PAE of 69% were achieved in CW operation. Moreover, we fabricated a prototype PA module that consists of an oscillator, a driver amplifier, PA, and other RF circuits. The output power was controlled by pulse width modulation to maintain high efficiency regardless of output power. We evaluated the arrangement of antenna polarizations to isolate each antenna. By suppressing the interference of output from adjacent antennas, it is possible to irradiate the object on the top surface of the antenna, thereby demonstrating heating characteristics with small temperature unevenness. The prototype microwave oven successfully demonstrated uniform/selective heating.

Due to the global outbreak of coronaviruses, people are increasingly wearing masks even when photographed. As a result, photos uploaded to web pages and social networking services with the lower half of the face hidden are less likely to convey the attractiveness of the photographed persons. In this study, we propose a method to complete facial mask regions using StyleGAN2, a type of Generative Adversarial Networks (GAN). In the proposed method, a reference image of the same person without a mask is prepared separately from a target image of the person wearing a mask. After the mask region in the target image is temporarily inpainted, the face orientation and contour of the person in the reference image are changed to match those of the target image using StyleGAN2. The changed image is then composited into the mask region while correcting the color tone to produce a mask-free image while preserving the person's features.

High-performance deep learning-based object detection models can reduce traffic accidents using dashcam images during nighttime driving. Deep learning requires a large-scale dataset to obtain a high-performance model. However, existing object detection datasets are mostly daytime scenes and a few nighttime scenes. Increasing the nighttime dataset is laborious and time-consuming. In such a case, it is possible to convert daytime images to nighttime images by image-to-image translation model to augment the nighttime dataset with less effort so that the translated dataset can utilize the annotations of the daytime dataset. Therefore, in this study, a GAN-based image-to-image translation model is proposed by incorporating self-attention with cycle consistency and content/style separation for nighttime data augmentation that shows high fidelity to annotations of the daytime dataset. Experimental results highlight the effectiveness of the proposed model compared with other models in terms of translated images and FID scores. Moreover, the high fidelity of translated images to the annotations is verified by a small object detection model according to detection results and mAP. Ablation studies confirm the effectiveness of self-attention in the proposed model. As a contribution to GAN-based data augmentation, the source code of the proposed image translation model is publicly available at https://github.com/subecky/Image-Translation-With-Self-Attention

The issue of a low minority class identification rate caused by data imbalance in anomaly detection tasks is addressed by the proposal of a GAN-SR-based intrusion detection model for industrial control systems. First, to correct the imbalance of minority classes in the dataset, a generative adversarial network (GAN) processes the dataset to reconstruct new minority class training samples accordingly. Second, high-dimensional feature extraction is completed using stacked asymmetric depth self-encoder to address the issues of low reconstruction error and lengthy training times. After that, a random forest (RF) decision tree is built, and intrusion detection is carried out using the features that SNDAE retrieved. According to experimental validation on the UNSW-NB15, SWaT and Gas Pipeline datasets, the GAN-SR model outperforms SNDAE-SVM and SNDAE-KNN in terms of detection performance and stability.

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.

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.

A frontal and realistic face image was synthesized from a single profile face image. It has a wide range of applications in face recognition. Although the frontal face method based on deep learning has made substantial progress in recent years, there is still no guarantee that the generated face has identity consistency and illumination consistency in a significant posture. This paper proposes a novel pixel-based feature regression generative adversarial network (PFR-GAN), which can learn to recover local high-frequency details and preserve identity and illumination frontal face images in an uncontrolled environment. We first propose a Reslu block to obtain richer feature representation and improve the convergence speed of training. We then introduce a feature conversion module to reduce the artifacts caused by face rotation discrepancy, enhance image generation quality, and preserve more high-frequency details of the profile image. We also construct a 30,000 face pose dataset to learn about various uncontrolled field environments. Our dataset includes ages of different races and wild backgrounds, allowing us to handle other datasets and obtain better results. Finally, we introduce a discriminator used for recovering the facial structure of the frontal face images. Quantitative and qualitative experimental results show our PFR-GAN can generate high-quality and high-fidelity frontal face images, and our results are better than the state-of-art results.

In this paper, we propose improved Generative Adversarial Networks with attention module in Generator, which can enhance the effectiveness of Generator. Furthermore, recent work has shown that Generator conditioning affects GAN performance. Leveraging this insight, we explored the effect of different normalization (spectral normalization, instance normalization) on Generator and Discriminator. Moreover, an enhanced loss function called Wasserstein Divergence distance, can alleviate the problem of difficult to train module in practice.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

Human skin visualization in the beauty industry with a smart-phone based on deep learning was discussed. Skin was photographed with a medical camera that could simultaneously capture RGB and UV images of the same area. Smartphone RGB images were converted into versions similar to medical RGB and UV images via a deep learning method called cycle-GAN, which was trained with the medical and the smartphone images. After converting the smartphone image into a version similar to a medical RGB image using cycle-GAN, the processed image was also converted into a pseudo-UV image via a deep learning method called U-NET. Hidden age spots were effectively visualized by this image. RGB and UV images similar to medical images can be captured with a smartphone. Provided the neural network on deep learning is trained, a medical camera is not required.

A detailed investigation of DC and RF performance of AlGaN/GaN HEMT on 3C-SiC/low resistive silicon (LR-Si) substrate by introducing a thick GaN layer is reported in this paper. The hetero-epitaxial growth is achieved by metal organic chemical vapor deposition (MOCVD) on a commercially prepared 6-inch LR-Si substrate via a 3C-SiC intermediate layer. The reported HEMT exhibited very low RF loss and thermally stable amplifier characteristics with the introduction of a thick GaN layer. The temperature-dependent small-signal and large-signal characteristics verified the effectiveness of the thick GaN layer on LR-Si, especially in reduction of RF loss even at high temperatures. In summary, a high potential of the reported device is confirmed for microwave applications.

In this study, AM-PM compensation of the cross-coupled capacitance neutralization technique is discussed. Cgd neutralization leads to AM-PM compensation of a power amplifier with negligible change of AM-AM characteristics. AM-PM compensation was confirmed via circuit analysis and measurements. The formulation analysis showed that AM-PM compensation can be derived via gm variation against input power with capacitance neutralization. A differential power amplifier with capacitance neutralization was fabricated with GaN high-electron-mobility transistors. The AM-PM characteristic of the fabricated differential power amplifier was measured at 17.7 GHz. It showed AM-PM reduction of 22° at compared to a single-phase power amplifier without capacitance neutralization at output power of 35 dBm.

In this research, the effect of Ar/N2-plasma sputtering gas pressure on the LaBxNy tunnel and block layer was investigated for pentacene-based floating-gate memory with an amorphous rubrene (α-rubrene) passivation layer. The influence of α-rubrene passivation layer for memory characteristic was examined. The pentacene-based metal/insulator/metal/insulator/semiconductor (MIMIS) diode and organic field-effect transistor (OFET) were fabricated utilizing N-doped LaB6 metal layer and LaBxNy insulator with α-rubrene passivation layer at annealing temperature of 200°C. In the case of MIMIS diode, the leakage current density and the equivalent oxide thickness (EOT) were decreased from 1.2×10-2 A/cm2 to 1.1×10-7 A/cm2 and 3.5 nm to 3.1 nm, respectively, by decreasing the sputtering gas pressure from 0.47 Pa to 0.19 Pa. In the case of floating-gate type OFET with α-rubrene passivation layer, the larger memory window of 0.68 V was obtained with saturation mobility of 2.2×10-2 cm2/(V·s) and subthreshold swing of 199 mV/dec compared to the device without α-rubrene passivation layer.