There are continuous and strong demands for the DC-DC converter to reduce the size of passive components and increase the system power density. Advances in CMOS processes and GaN FETs enabled the switching frequency of DC-DC converters to be beyond 10MHz. The advancements of 3-D integrated magnetics will further reduce the footprint. In this paper, the overview of beyond-10MHz DC-DC converters will be provided first, and our recent achievements are introduced focusing on 3D-integration of Fe-based metal composite magnetic core inductor, and GaN FET control designs.

GaN microwave monolithic integrated circuit (MMIC) power amplifiers (PAs) technologies for millimeter-wave (mm-wave) applications are reviewed in this paper. In the mm-wave band, GaN PAs have achieved high-output power as much as traveling wave tube amplifiers used in satellite communications. Additionally, GaN PAs have been integrated enough to be used for 5G and Beyond-5G. In this paper, a high accuracy large-signal GaN-HEMT modeling technique including the trapping effects is introduced in mm-waves. The prototyped PAs designed with the novel modeling technique have achieved RF performance comparable to that of the state-of-the-art GaN PAs in mm-wave.

This paper proposes a topology of high power, MHz-frequency, half-bridge resonant inverter ideal for low-loss Gallium Nitride high electron mobility transistor (GaN-HEMT). General GaN-HEMTs have drawback of low drain-source breakdown voltage. This property has prevented conventional high-frequency series resonant inverters from delivering high power to high resistance loads such as 50Ω, which is typically used in radio frequency (RF) systems. High resistance load causes hard-switching also and reduction of power efficiency. The proposed topology overcomes these difficulties by utilizing a proposed ‘L-S network’. This network is effective combination of a simple impedance converter and a series resonator. The proposed topology provides not only high power for high resistance load but also arbitrary design of output wattage depending on impedance conversion design. In addition, the current through the series resonator is low in the L-S network. Hence, this series resonator can be designed specifically for harmonic suppression with relatively high quality-factor and zero reactance. Low-distortion sinusoidal 3kW output is verified in the proposed inverter at 13.56MHz by computer simulations. Further, 99.4% high efficiency is achieved in the power circuit in 471W experimental prototype.

Generative Adversarial Networks (GANs) are one of the most successful learning principles of generative models and were wildly applied to many generation tasks. In the beginning, the gradient penalty (GP) was applied to enforce the discriminator in GANs to satisfy Lipschitz continuity in Wasserstein GAN. Although the vanilla version of the gradient penalty was further modified for different purposes, seeking a better equilibrium and higher generation quality in adversarial learning remains challenging. Recently, DRAGAN was proposed to achieve the local linearity in a surrounding data manifold by applying the noised gradient penalty to promote the local convexity in model optimization. However, we show that their approach will impose a burden on satisfying Lipschitz continuity for the discriminator. Such conflict between Lipschitz continuity and local linearity in DRAGAN will result in poor equilibrium, and thus the generation quality is far from ideal. To this end, we propose a novel approach to benefit both local linearity and Lipschitz continuity for reaching a better equilibrium without conflict. In detail, we apply our synchronized activation function in the discriminator to receive a particular form of noised gradient penalty for achieving local linearity without losing the property of Lipschitz continuity in the discriminator. Experimental results show that our method can reach the superior quality of images and outperforms WGAN-GP, DiracGAN, and DRAGAN in terms of Inception Score and Fréchet Inception Distance on real-world datasets.

Recently, channel-aware steganography has been presented for high security. The corresponding selection-channel-aware (SCA) detecting algorithms have also been proposed for improving the detection performance. In this paper, we propose a novel detecting algorithm of JPEG steganography, where the embedding probability and block evaluation are integrated into the new probability. This probability can embody the change due to data embedding. We choose the same high-pass filters as maximum diversity cascade filter residual (MD-CFR) to obtain different image residuals and a weighted histogram method is used to extract detection features. Experimental results on detecting two typical steganographic methods show that the proposed method can improve the performance compared with the state-of-art methods.

In this paper, the high-power high-efficiency asymmetric Doherty power amplifiers based on high-voltage GaN HEMT devices with internal input matching for base station applications are proposed and described. For a three-way 1:2 asymmetric Doherty structures, an exceptionally high output power of 1 kW with a peak efficiency of 83% and a linear flat power gain of about 15 dB was achieved in a frequency band of 2.11-2.17 GHz, whereas an output power of 59.5 dBm with a peak efficiency of 78% and linear power gain of 12 dB and an output power of 59.2 dBm with a peak efficiency of 65% and a linear power gain of 13 dB were obtained across 1.8-2.2 GHz. To provide a high-efficiency broadband operation, the concept of inverted Doherty structure is applied and described in detail. By using a high-power broadband inverted Doherty amplifier architecture with a 2×120-W GaN HEMT transistor, a saturated power of greater than 54 dBm, a linear power gain of greater than 13 dB and a drain efficiency of greater than 50% at 7-dB power backoff in a frequency bandwidth of 1.8-2.7 GHz were obtained.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

This paper presents recent progress on high frequency and wide bandwidth GaN high power amplifiers (PAs) that are usable for high-data-rate wireless communications and modern radar systems. The key devices and design techniques for PA are described in this paper. The results of the state-of-the art GaN PAs for microwave to millimeter-wave applications and design methodology for ultra-wideband GaN PAs are shown. In order to realize high output power density, InAlGaN/GaN HEMTs were employed. An output power density of 14.8 W/mm in S-band was achieved which is 1.5 times higher than that of the conventional AlGaN/GaN HEMTs. This technique was applied to the millimeter-wave GaN PAs, and a measured power density at 96 GHz was 3 W/mm. The modified Angelov model was employed for a millimeter-wave design. W-band GaN MMIC achieved the maximum Pout of 1.15 W under CW operation. The PA with Lange coupler achieved 2.6 W at 94 GHz. The authors also developed a wideband PA. A power combiner with an impedance transformation function based on the transmission line transformer (TLT) technique was adopted for the wideband PA design. The fabricated PA exhibited an average Pout of 233 W, an average PAE of 42 %, in the frequency range of 0.5 GHz to 2.1 GHz.

A practical Doherty amplifier design method has been developed based on an asymmetric configuration scheme. By embedding a load modulation function into matching circuits of a carrier amplifier (CA) and a peaking amplifier (PA) in the Doherty amplifier, an issue of the Doherty amplifier design is boiled down to the CA and PA matching circuit design. The method can be applied to transistors with unknown parasitic elements if optimum termination impedance conditions for the transistor are obtained from a source-/load-pull technique in simulation or measurement. The design method was applied to GaN HEMT Doherty amplifier MMICs. The fabricated 4.5-GHz-band GaN HEMT Doherty amplifier MMIC exhibited a maximum drain efficiency of 66% and a maximum power-added efficiency (PAE) of 62% at 4.1GHz, with a saturation output power of 36dBm. In addition, PAE of 50% was achieved at 4.1GHz on a 7.2-dB output back-off (OBO) condition. The fabricated 8.5-GHz-band GaN HEMT Doherty amplifier MMIC exhibited a maximum drain efficiency of 53% and a maximum PAE of 44% at 8.6GHz, with a saturation output power of 36dBm. In addition, PAE of 35% was achieved at 8.6GHz on a 6.7-dB (OBO). And, the fabricated 12-GHz-band GaN HEMT Doherty amplifier MMIC exhibited a maximum drain efficiency of 57% and a maximum PAE of 52% at 12.4GHz, with a saturation output power of 34dBm. In addition, PAE of 32% was achieved at 12.4GHz on a 9.5-dB (OBO) condition.

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.

During the recent several years, deep learning has achieved excellent results in image recognition, voice processing, and other research areas, which has set off a new upsurge of research and application. Internal defects and external malicious attacks may threaten the safe and reliable operation of a deep learning system and even cause unbearable consequences. The technology of testing deep learning systems is still in its infancy. Traditional software testing technology is not applicable to test deep learning systems. In addition, the characteristics of deep learning such as complex application scenarios, the high dimensionality of input data, and poor interpretability of operation logic bring new challenges to the testing work. This paper focuses on the problem of test case generation and points out that adversarial examples can be used as test cases. Then the paper proposes MTGAN which is a framework to generate test cases for deep learning image classifiers based on Generative Adversarial Network. Finally, this paper evaluates the effectiveness of MTGAN.

Recently, image compression systems based on convolutional neural networks that use flexible nonlinear analysis and synthesis transformations have been developed to improve the restoration accuracy of decoded images. Although these methods that use objective metric such as peak signal-to-noise ratio and multi-scale structural similarity for optimization attain high objective results, such metric may not reflect human visual characteristics and thus degrade subjective image quality. A method using a framework called a generative adversarial network (GAN) has been reported as one of the methods aiming to improve the subjective image quality. It optimizes the distribution of restored images to be close to that of natural images; thus it suppresses visual artifacts such as blurring, ringing, and blocking. However, since methods of this type are optimized to focus on whether the restored image is subjectively natural or not, components that are not correlated with the original image are mixed into the restored image during the decoding process. Thus, even though the appearance looks natural, subjective similarity may be degraded. In this paper, we investigated why the conventional GAN-based compression techniques degrade subjective similarity, then tackled this problem by rethinking how to handle image generation in the GAN framework between image sources with different probability distributions. The paper describes a method to maximize mutual information between the coding features and the restored images. Experimental results show that the proposed mutual information amount is clearly correlated with subjective similarity and the method makes it possible to develop image compression systems with high subjective similarity.

Image deformations caused by different steganographic methods are typically extremely small and highly similar, which makes their detection and identification to be a difficult task. Although recent steganalytic methods using deep learning have achieved high accuracy, they have been made to detect stego images to which specific steganographic methods have been applied. In this letter, a staganalytic method is proposed that uses hierarchical residual neural networks (ResNet), allowing detection (i.e. classification between stego and cover images) and identification of four spatial steganographic methods (i.e. LSB, PVD, WOW and S-UNIWARD). Experimental results show that using hierarchical ResNets achieves a classification rate of 79.71% in quinary classification, which is approximately 23% higher compared to using a plain convolutional neural network (CNN).

Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.

High dynamic range (HDR) imaging refers to digital image processing that modifies the range of color and contrast to enhance image visibility. To create an HDR image, two or more images that include various information are needed. In order to convert low dynamic range (LDR) images to HDR images, we consider the possibility of using a generative adversarial network (GAN) as an appropriate deep neural network. Deep learning requires a great deal of data in order to build a module, but once the module is created, it is convenient to use. In this paper, we propose a weight map for local luminance based on learning to reconstruct locally tone-mapped images.

Recently, GaN devices are often adopted in microwave power amplifiers to improve the performances. And many new design methods of microwave power amplifier were proposed. As a result, a high-efficiency and super compact microwave signal source has become easily available. It opens up the way for new microwave heating systems. In this paper, the recent progress on design methods of microwave power amplifier and the applications for microwave heating are described. In the first, a device model of GaN transistor is explained. An equivalent thermal model is introduced into the electrical non-linear equivalent device model. In the second, an active load-pull (ALP) measurement system to design a high-efficiency power amplifier is explained. The principle of the conventional closed-loop ALP system is explained. To avoid the risk of oscillation for the closed-loop ALP system, novel ALP systems are proposed. In the third, a microwave heating system is explained. The heating system monitors the reflection wave. Then, the frequency of the signal source and the phase difference between antennas are controlled to minimize the reflection wave. Absorption efficiency of more than 90% was obtained by the control of frequency and phase. In the last part, applications for a medical instrument is described.

Nurimisaki and Sashigane are Nikoli's pencil puzzles. We study the computational complexity of Nurimisaki and Sashigane puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

In this study, simulations are performed to design an optimal device for thinning the GaN channel layer on the semi-insulating layer in HEMT. When the gate length is 50nm, the thickness of the undoped channel must be thinner than 300nm to observe the off state. When the GaN channel layer is an Fe-doped, an on/off ratio of ~300 can be achieved even with a gate length of 25nm, although the transconductance is slightly reduced.

A new design approach of broadband RF power amplifier (PA) is introduced in this work with combination of large signal X-parameter and Real-Frequency Technique (RFT). A theoretical analysis of large signal X-parameter is revisited, and a simplification method is introduced to determine the optimum large signal impedances of a Gallium Nitride HEMT (GaN HEMT) device. With the optimum impedance extraction over the wide frequency range (0.3 to 2.0 GHz), a wideband matching network is constructed employing RFT and the final design is implemented with practical mixed-lumped elements. The prototype broadband RF PA demonstrates an output power of 40 dBm. The average drain efficiency of the PA is found to be more than 60%; while exhibiting acceptable flat gain performance (12±0.25 dB) over the frequency band of (0.3-2.0 GHz). The PA designed using the proposed approach yields in small form factor and relatively lower production cost over those of similar PAs designed with the classical methods. It is expected that the newly proposed design method will be utilized to construct power amplifiers for radio communications applications.

TCAD simulation was performed to investigate the material properties of an AlGaN/GaN structure in Deep Acceptor (DA)-rich and Deep Donor (DD)-rich GaN cases. DD-rich semi-insulating GaN generated a positively charged area thereof to prevent the electron concentration in 2DEG from decreasing, while a DA-rich counterpart caused electron depletion, which was the origin of the current collapse in AlGaN/GaN HFETs. These simulation results were well verified experimentally using three nitride samples including buffer-GaN layers with carbon concentration ([C]) of 5×1017, 5×1018, and 4×1019 cm-3. DD-rich behaviors were observed for the sample with [C]=4×1019 cm-3, and DD energy level EDD=0.6 eV was estimated by the Arrhenius plot of temperature-dependent IDS. This EDD value coincided with the previously estimated EDD. The backgate experiments revealed that these DD-rich semi-insulating GaN suppressed both current collapse and buffer leakage, thus providing characteristics desirable for practical usage.