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In clinical practice, radiologists diagnose brain tumors with the help of different magnetic resonance imaging (MRI) sequences and judge the type and grade of brain tumors. It is hard to realize the brain tumor computer‐aided diagnosis system only with a single MRI sequence. However, the existing multiple MRI sequence fusion methods have limitations in the enhancement of tumor details. To improve fusion details of multi‐modality MRI images, a novel conditional generative adversarial fusion network based on three discriminators and a Staggered Dense Residual2 (SDR2) module, named SDR2Tr‐GAN, was proposed in this paper. In the SDR2Tr‐GAN network pipeline, the generator consists of an encoder, decoder, and fusion strategy that can enhance the feature representation. SDR2 module is developed with Res2Net into the encoder to extract multi‐scale features. In addition, a Multi‐Head Spatial/Channel Attention Transformer, as a fusion strategy to strengthen the long‐range dependencies of global context information, is integrated into our pipeline. A Mask‐based constraint as a novel fusion optimization mechanism was designed, focusing on enhancing salient feature details. The Mask‐based constraint utilizes the segmentation mask obtained by the pre‐trained Unet and Ground Truth to optimize the training process. Meanwhile, MI and SSIM loss jointly improve the visual perception of images. Extensive experiments were conducted on the public BraTS2021 dataset. The visual and quantitative results demonstrate that the proposed method can simultaneously enhance both global image quality and local texture details in multi‐modality MRI images. Besides, our SDR2Tr‐GAN outperforms the other state‐of‐the‐art fusion methods regarding subjective and objective evaluation. [ABSTRACT FROM AUTHOR]

Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model's characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety. [ABSTRACT FROM AUTHOR]

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies. [ABSTRACT FROM AUTHOR]

With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on a Convolutional Neural Network (CNN), but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through GrabCut. It shows a high performance through an image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images, instead of the actual input, as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed a higher performance than previous research, and image-based scores showed the best performance. [ABSTRACT FROM AUTHOR]

Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model's discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy. [ABSTRACT FROM AUTHOR]

Facial image synthesis using delineated face images is a complex task associated with computer vision. The delineated face image synthesis such as sketch to face generation or thermal to visual face generation using generative adversarial networks (GANs) is a widely accepted task due to its generative capability. The accurate and realistic sample generation by using GAN with an attention network shows a more realistic sample generation. Attention-based network improves the network's learning by prioritizing the learning over a specific region. Motivated by the success of attention mechanism in recent literature, we develop a new inception-based encoder–decoder self-attentive generative adversarial network (ISA-GAN) by incorporating an inception network with self-attention-based learning. The proposed network is embedded with parallel self-attention, which helps to generate high-quality images and converges faster in terms of training epochs. The proposed scenario has been experimented for face synthesis using delineated face images of sketch images over the CUHK dataset. We also test it over thermal to visual face synthesis using WHU-IIP and CVBL-CHILD datasets. The proposed ISA-GAN outperforms the state-of-the-art generative models for face synthesis. The proposed ISA-GAN shows on an average improvement of 9.95 % in SSIM score over CUHK dataset while 10.38 % , 12.58 % for WHU-IIP and CVBL-CHILD datasets, respectively. [ABSTRACT FROM AUTHOR]

Objective: This study aims to evaluate the mental health of college students who have failed in entrepreneurship by analyzing their electroencephalogram (EEG) signals. Long-term negative emotions are closely related to mental health problems. This study explores how to evaluate the mental health of college students who have failed in entrepreneurship through EEG signals and the use of generative adversarial networks (GANs) to improve classification accuracy. Methods: We first collected EEG data from college students who have failed in entrepreneurship. These data were used to extract multiple features, including time domain features, frequency domain features, and nonlinear features. To improve the accuracy and robustness of the classification model, we introduced GAN. GAN enhances the diversity of the training data set by generating more samples, thereby improving the performance of the EEG signal classification model. This process includes training the generator to generate samples similar to real data and training the discriminator to distinguish between real and generated data. Through this adversarial process, the classification model is continuously optimized. Experimental results show that the EEG signal analysis method based on GAN can effectively classify the long-term emotional state of college students who have failed in entrepreneurship and accurately identify negative emotions such as depression and anxiety. The additional samples generated by GAN significantly improve the classification model's ability to identify emotional states, making the assessment of mental health status more accurate. Conclusion: This study successfully improved the accuracy of EEG signal classification by introducing GAN technology, thus providing an effective tool for evaluating the mental health status of college students who failed in entrepreneurship. This method not only enhances the ability to detect negative emotions of college students but also provides a novel and practical solution for mental health assessment and intervention in colleges and universities, which helps to detect and intervene in potential mental health problems of college students who failed in entrepreneurship as early as possible. [ABSTRACT FROM AUTHOR]

Photon‐pair generation based on optical parametric down‐conversion has attracted for the application as a light source for quantum information. Highly efficient wavelength‐conversion devices require a polarity‐inversion structure when using nitride semiconductors. A transverse quasi‐phase‐matching (QPM) polarity‐inverted GaN bilayer channel waveguide device is suitable for efficient wavelength conversion. This study designed a cross‐section device to satisfy the modal dispersion phase‐matching condition between the TM02 mode pump light and the TM00 mode signal/idler light. Moreover, an AlN oxidation interlayer fabricates the Ga‐polar/N‐polar (+c/−c) GaN layers via metalorganic vapor‐phase epitaxy (MOVPE). A 145 nm thick film layer with a macro‐step‐free surface is grown by optimizing the −c‐GaN growth conditions and reducing the substrate off‐angle to 0.2°. Next, the AlN layer is oxidized in an electric furnace and MOVPE is used to regrow a 1500 nm thick +c‐GaN layer. A macrosteps‐free surface can be achieved by reducing the off‐angle to 0.2° and optimizing the −c‐GaN growth conditions to avoid hillock formation. These results pave the way for improving the efficiency of GaN transverse QPM wavelength‐conversion devices. [ABSTRACT FROM AUTHOR]

Selective area growth and epitaxial lateral overgrowth (ELOG) of semipolar (11¯$1 \left(\right. \macr \left.\right)$01) GaN stripes are demonstrated on a trench patterned vicinal (001) Si substrate fabricated by a maskless photolithography‐based process. High precision alignment enables selective mask formation to one sidewall of the trench. Selective area growth of GaN stripes is conducted from the (111) plane sidewall of Si, and ELOG region reaches ≈13 μm. The ELOG GaN crystal is dislocation‐free at most areas. The semipolar GaN stripes with atomically flat surface morphology are uniformly obtained. Light‐emitting diode structures with InGaN/GaN multiple quantum wells are grown on the 13‐μm ELOG (11¯$1 \left(\right. \macr \left.\right)$01) GaN stripes and a single photoluminescence emission peaked at 485 nm is obtained, suggesting potential for the cost‐effective semipolar micro light‐emitting diode fabrication technologies. [ABSTRACT FROM AUTHOR]

ScAlMgO4 (SAM) substrates have attracted significant attention as template substrates for fabricating bulk GaN crystals. Radio‐frequency plasma‐assisted molecular beam epitaxy (RF‐MBE) is preferred for the growth of the first GaN layer. However, the metastable zincblende (ZB) phase is mixed on the SAM substrate with a smaller terrace width, coupled with lower growth temperatures in MBE. Herein, the time evolution of GaN growth on SAM substrates is investigated to elucidate the mechanism underlying ZB mixing. ZB‐GaN is generated as stacking faults during the coalescence of the initially grown wurtzite‐ (WZ‐) GaN islands on adjacent SAM terraces, owing to the extraordinarily large step height close to three bilayers of WZ‐GaN. Therefore, high‐temperature growth on a SAM substrate with wider terraces suppresses the generation of the ZB phase in the initial growth stage. ZB‐GaN also decreases with an increase in the growth time. Based on these results, a 940 nm‐thick GaN layer is grown at 750 °C on the SAM substrate with a terrace width of 820 nm. A pure WZ‐GaN layer from the GaN/SAM interface with a flat surface with a root mean square value of below 1 nm and a dislocation density of 7.3 × 109 cm−2 is obtained. [ABSTRACT FROM AUTHOR]

Metal–organic vapor phase epitaxy of GaN on sapphire (0001) substrates without using low‐temperature (LT) buffer layers is demonstrated. The growth of GaN is achieved by pretreatment of sapphire with trimethylaluminum (TMA) at a high temperature (1050 °C) in a H2 + N2 gas mixture. The TMA pretreatment forms AlN, which acts as nucleation seeds for the subsequent growth of GaN at the same temperature. When AlN created by the TMA pretreatment is three‐dimensional, similar to conventional LT buffer layers, the GaN layers exhibit good structural properties such as atomically smooth surfaces and narrow X‐ray diffraction line widths, comparable to those of GaN on LT buffer layers. In addition, the growth evolution of GaN on TMA‐pretreated sapphire is similar to that on GaN or AlN LT buffer layers. These similarities between the TMA pretreatment and conventional LT buffer‐layer technologies might offer an opportunity to further generalize the heteroepitaxy growth model of GaN. [ABSTRACT FROM AUTHOR]

Subbandgap optical absorption (SOA) in undoped GaN and 90 nm‐thick Al1−xInxN thin films grown on sapphire substrates is investigated using photothermal deflection spectroscopy (PDS) and photoluminescence (PL). An Al1−xInxN alloy (x = 0.17) is grown on a GaN/sapphire template by metal–organic chemical vapor deposition (MOCVD), and the SOA is observed using PDS. To develop an estimation method for the absorption coefficient (α) of SOA in GaN and Al1−xInxN thin films, the use of a thick GaN substrate is proposed, which is grown by hydride vapor‐phase epitaxy, as a converter of the PDS signal intensity to α, and the accuracies of the estimated α are discussed. Comparing the PDS and PL results, it is revealed that nonradiative recombination centers leading to the reduction of the near‐band‐edge PL intensity are not the dominant sources of SOAs in GaN. Other in‐gap states formed by impurities and/or their complexes with vacancy‐type defects are possible sources of a large SOA in the MOCVD‐grown GaN template. Considering the PDS results and reported peak reflectivity of Al1−xInxN/GaN distributed Bragg reflectors, the α value of sub‐100 nm‐thick Al1−xInxN alloy grown on GaN/sapphire template is expected to be ≈100 cm−1 or less below 3.0 eV. [ABSTRACT FROM AUTHOR]

Focusing on a VGa–VN complex vacancy defect in GaN, the adiabatic potential for conformational changes of the VGa–VN is investigated using the density functional theory. There are two types of configurations due to the symmetry of the crystal, it is confirmed that there is almost no difference in their stability or electronic state. Using these configurations as the initial and final states, the defect reaction is analyzed when VGa–VN moves using the climbing‐image nudged elastic band method. The reaction barrier for the migration of VGa (VN) is found to be 2.2 (3.3) eV. It is concluded that these reactions occur frequently as GaN is annealed since the atomic diffusion potential barrier is similar to that in other semiconductors. These results suggest that VGa–VN in GaN moves through the crystal as a pair of vacancies by alternately repeating the VGa and the VN migration. These results provide important knowledge for elucidating the microscopic mechanism of the experimentally observed VGa–VN aggregation reaction. [ABSTRACT FROM AUTHOR]

Molecular beam epitaxy is applied for the GaN/InGaN growth under In‐bilayer stabilized conditions, which require substrate temperatures above 580 °C for sufficient indium desorption. Under these conditions the indium content is limited to xIn≤0.3$x_{\text{In}} \leq 0.3$, as determined by a novel reflection high energy electron diffraction technique, which is based on indium adsorption and subsequent consumption at varying gallium flux. As‐grown structures show degraded surfaces after indium desorption. This effect is eliminated by applying excess gallium before and after InGaN growth, which results in morphologies and dislocation densities comparable to GaN/AlGaN structures. [ABSTRACT FROM AUTHOR]

An in situ control of a center wavelength in a 40‐pair AlInN/GaN distributed Bragg reflector (DBR) for GaN‐based vertical‐cavity surface‐emitting lasers (VCSELs) with an in situ reflectivity spectra measurement is proposed and developed. A procedure of the in situ control consists of the following three steps: 1) to grow an initial 5‐pair DBR; 2) to obtain the center wavelength value just after the initial DBR growth by the in situ reflectivity spectra measurement; and 3) to adjust growth times as a result of the obtained center wavelength value and continue the remaining 35‐pair DBR. It is experimentally demonstrated that the in situ control of the center wavelength in the 40‐pair DBRs with the ±2% thickness deviations in the initial 5‐pair DBR results in reductions of the deviation down to ±0.3%. The in situ DBR center wavelength control is a key technique for highly reproducible GaN‐based VCSELs. [ABSTRACT FROM AUTHOR]

With a view to device applications as a new material, n‐GaN grown on Si substrates is anodized at 4 V in an electrolyte solution for different energization times (15–240 min), and the dependence of the changes in electrical properties on the anodization time is evaluated. Anodization at 4 V increases the resistivity of n‐GaN with increasing energization time. To investigate the origin of the increase in resistivity by anodization, the results of the temperature dependence of the Hall mobility and electron density are discussed. The Hall mobility decreases with increasing energization time. The decrease in Hall mobility is because anodization forms neutral defects and increases neutral‐impurity scattering. The electron density decreases with increasing energization time. The decrease in electron density occurs because anodization deepens the original donor level and generates a deeper level that captures electrons. Thus, anodization changes the electrical properties of n‐GaN grown on Si substrate, and their dependence on energization time is discussed in detail. [ABSTRACT FROM AUTHOR]

Theoretical investigations for the adsorption behavior of O atoms on both Ga‐polar (+c) surface and N‐polar (−$-$c) GaN surfaces using ab initio calculations to clarify the influence of adsorption behavior on oxygen incorporation are presented. The calculations demonstrate that the O atom is stabilized by forming Ga−$-$OH bond on the flat +c plane GaN(0001) surface, while the topmost N atom is replaced by the O atom on the flat −$-$c plane GaN(0001¯$\bar{1}$) surface. The calculations using the nudged elastic band method also reveal that the energy barriers for oxygen incorporation on the flat −$-$c plane surface are comparable to that on the flat +c plane surface. Furthermore, the adsorption energy on the −$-$c plane surface drastically increases on the vicinal surface with step edges and kinks, indicating that step edges and kinks enhance the incorporation of O atoms on the −$-$c plane surfaces. The calculated results offer better understanding of the atom‐scale mechanisms for the orientation dependence of oxygen concentrations in GaN. [ABSTRACT FROM AUTHOR]

The depth profiles of the Mg‐acceptor or Si‐donor concentration in GaN are visualized using stepwise etching and photoluminescence (PL) measurements. Low‐damage etching techniques are applied: multistep‐bias inductively coupled plasma reactive ion etching for p‐type GaN and electrochemical etching for n‐type GaN. To determine the concentration calibration curve, the PL intensity ratios of Mg‐neutral acceptor or Si‐neutral donor‐bound excitons to free excitons are plotted against Mg or Si atomic concentrations in etched GaN epitaxial layers having depth distributions of dopant concentrations, as estimated by secondary ion mass spectrometry. The quantitative accuracy of the calibration curve is ensured by the appropriate sample temperature and excitation density ranges for PL. The stepwise dry etching and the Mg acceptor quantification by PL using this calibration curve are applied to Mg‐ion‐implanted GaN after ultra‐high‐pressure annealing at 1300 °C for 1 min. The obtained depth profile of the Mg‐acceptor concentration is consistent with the Mg atomic concentration profile in the region deeper than the implantation peak, whereas the acceptor concentration in the shallow region is lower than the Mg atomic concentration. This analysis is useful for revealing the site substitution ratio of dopants in GaN materials partially involving inactive dopants. [ABSTRACT FROM AUTHOR]

Low‐angle bevel mesa structures are the key technological ideas needed to produce high‐quality vertical GaN devices. Nevertheless, the literature lacks sufficient information regarding the techniques that can yield the best results for obtaining the bevel angles smaller than approximately 5°.[1] Notably, for high p–n junctions NA/ND ratios, such low angles are necessary for the application of negative beveled edge termination and the efficient lowering of electric field at the surface.[1–6] In this work, the optimization of the beveled‐mesa fabrication technique for vertical GaN devices is demonstrated employing a plasma etching of the photoresist mask fabricated using a reflow process. By adjusting the resist reflow process parameters, in particular various BCl3 Cl2−1 ratios and inductively coupled plasma–reactive ion etching plasma power, a range of beveled mask angles as low as 10°–12° is achieved. However, those processes all show etching selectivity close to 1:1 between GaN and the photoresist. Even lower bevel mesa angles are reported for a novel process optimization that resulted in improving GaN to photoresist selectivity by a factor of 5–10. As a result, even smaller bevel mesa angles close to 1°–2° are able to be attained. The developed approach has the advantage of being relatively simple and cost effective. [ABSTRACT FROM AUTHOR]

Low‐resistivity Ohmic contacts to Gallium‐face (G‐aface) and/or Nitrogen‐face (‐Nface) n‐GaN are undoubtedly needed for high‐quality vertical power devices. Contrary to Ga‐face n‐GaN, for which the Ohmic contact formation is a well‐established process, the formation of low‐resistivity Ohmic contacts to N‐face n‐GaN is much more difficult due to the different surface properties. Despite many efforts, obtaining low‐resistivity ohmic contacts, with ρc < 1 × 10−5 Ω cm2 to N face n‐GaN, still remains difficult to achieve. Herein, results of fabrication and characterization of Ti/Al/TiN/Au ohmic contacts to Ga‐ and N‐face n‐GaN for vertical power devices are presented. The optimum annealing temperature is examined and it is noticed that Ohmic contacts to Ga‐face n‐GaN (n ≈ 5 × 1018 cm−3 , 200 nm on bulk GaN substrate) are formed after 550 °C annealing and continuously lower their ρc up to temperature of 750 °C for which ρc is ≈1.86 × 10−6 Ω cm2. Ohmic contact to N‐face n‐GaN (bulk ammonothermally grown substrate, ρ ≈ 10−3–10−2 Ω cm2) becomes quasilinear after annealing at 650 °C and becomes linear after annealing at 700 °C, reaching a minimum value of ρc 6 × 10−6 Ω cm2. Further annealing at higher temperatures increases the contact resistivity to the value of ≈5.8 × 10−5 cm2 for annealing at 800 °C. The electrical measurements results were accompanied with the results of structural characterization for determination of Ohmic contact formation mechanism. [ABSTRACT FROM AUTHOR]

Effects of the ultrathin 1 nm thick AlGaN regrown layer before gate insulator deposition on the performance of ZrO2/AlGaN/GaN metal‐insulator‐semiconductor (MIS) structures are investigated. In comparison with the reference ZrO2/AlGaN/GaN MIS high‐electron‐mobility transistors (MIS‐HEMTs), MIS‐HEMTs with AlGaN regrown layer exhibit increased maximum drain current (>1050 mA mm−1) and broader full‐width at half maximum (FWHM) of transconductance profile (11 V). Moreover, the gate leakage current in the forward direction is reduced by about four orders of magnitude while the hysteresis of the transfer curves is cut‐down to more than 40%. Compared with the control MIS‐capacitors (MIS‐caps), MIS‐caps with regrowth show highly evident sudden increase in capacitance known as "spill‐over" in the capacitance–voltage (C–V) profiles, suggesting improved ZrO2/AlGaN interfaces. Extracted electron states from the C–V profiles, reveal 6 times to 14 times reduction of interface states density within 0.2 to 0.8 eV from the AlGaN conduction band edge. Using photo‐assisted C–V measurements, the near mid‐gap electron states density of MIS‐caps is extracted with regrowth to be at least three times less than those of reference MIS‐caps. Findings in this work are very similar to those recently reported Al2O3/regrown‐AlGaN interfaces, suggesting that the regrowth technique could be universal in reducing electron states along insulator/AlGaN interfaces. [ABSTRACT FROM AUTHOR]

Herein, epitaxially regrown n+ (In)GaN source/drain layers for radio frequency high electron mobility transistors, addressing material and electrical characterization, are reported. A range of n+ GaN and n+ InGaN layers with indium 4–12 at% and silicon 0–5.1 × 1020 cm−3 are evaluated. The active carrier concentration in n+ InGaN exceeds 2 × 1020 cm−3. The layers exhibit so‐called V‐defects, observed by atomic force microscope and transmission electron microscope (TEM), which are associated with local composition changes. In addition to the high Si doping levels, nitrogen vacancies are also considered to contribute toward their net carrier concentration. Due to its relevance for device processing, selectivity analysis is performed, and the optimal process conditions for selective regrowth are identified. Regrowth under high temperature (800 °C) is found to be conducive to improved selectivity. However, a high thermal budget negatively impacts the overall regrowth process, as reported here for In0.17Al0.83N and Al0.26Ga0.74N barriers: whereas the InAlN barrier suffers from intermixing, the AlGaN barrier demonstrates high‐temperature stability. The impact of intermixing is studied from complementary TEM and DC electrical measurements. A low overall contact resistance of 75 Ω μm is obtained with the regrown n+ InGaN layers. [ABSTRACT FROM AUTHOR]

Silicon nitride (SiNx) is used for device passivation and capacitance dielectric in GaN monolithic microwave integrated circuits. However, this is a conflicting requirement as passivation requires SiNx to cause tensile strain, and capacitance dielectric primarily demands a high breakdown voltage and large dielectric constant for SiNx, leading to a damaged AlGaN surface during deposition. Two independent SiNx depositions under two different conditions (silicon‐ and nitrogen‐rich) are usually carried out to meet both requirements. Herein, a solution for a unified deposition through interfacial strain analysis on an AlGaN/GaN heterostructure grown on 6H‐SiC imposed by thin film silicon nitride (SiNx), deposited using inductively coupled plasma chemical vapor deposition system is proposed. The strain analysis is done using Raman spectroscopy. The surface morphology of the SiNx is studied using atomic force microscopy. The breakdown characteristics are ascertained from measurements on high electron mobility transistors and metal–insulator–metal capacitors. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to examine the DC characteristics of a vertical PIN diode featuring AlGaN as the drift layers. In the investigation, observing the changes in DC characteristics with the variation of Al content (0%–4%) in the drift layers as well as its thickness is focused on. By applying AlGaN as the drift layer, the semi‐insulating properties are augmented and the detrimental background carbon impurity is reduced, leading to a reduction in the leakage current and an improvement in the breakdown voltage characteristics of the device. Room‐temperature and low‐temperature photoluminescence measurements confirm a reduction in the intensity of both yellow luminescence and blue luminescence, which can be attributed to the decrease in defect concentration. Furthermore, to minimize the occurrence of premature breakdown voltage, p‐type GaN is meticulously etched into a bevel shape, and subsequent confirmation through the utilization of a scanning electron microscope reveals an inclination of 13°. The outcome of the analysis on the DC characteristic indicates an improvement in reverse‐bias characteristics with the increase in the Al ratio in the drift layer for the 2 μm series. Furthermore, for the 20 μm Al0.04Ga0.96N, a breakdown voltage of 2540 V is recorded. [ABSTRACT FROM AUTHOR]

Flexible light‐emitting devices have attracted attention as a novel bio‐interface connecting living tissues with electronics due to their high brightness, low power consumption, and durability in humid environments. Introduction of vertically current‐injected micro‐light‐emitting diodes (MicroLEDs) into this film can enhance the MicroLED effective area and improve device characteristics. In this study, the MicroLED transfer technology onto conductive materials is investigated. The feasibility of batch transferring MicroLEDs onto a conductive polymer is demonstrated by PEDOT:PSS layer. For non‐Ohmic characteristics between n‐GaN and PEDOT:PSS, a backside‐open MicroLED hollow structure is proposed, enabling the formation of Ti/Au electrodes on the backside of MicroLED. By transferring the fabricated vertically current‐injected MicroLEDs onto the PEDOT:PSS layer, a flexible vertically current‐injected LED film is achieved, observing uniform blue light emission. The developed MicroLED film holds promise as a new neuroscience tool for targeting specific areas of the brain with light. [ABSTRACT FROM AUTHOR]

The buffer leakage phenomena have a large effect on high‐power and fast‐switching GaN high electron mobility transistor devices. Herein, the buffer leakage mechanism in a GaN‐on‐Si substrate with AlGaN buffer layers is investigated by using samples with various carbon concentrations. Comparing the leakage current with crystallinity and impurity concentration, it is found that the vertical leakage current is dominated by both threading dislocations and bulk impurities. Increasing the carbon concentration of buffer layers suppresses leakage current in the bulk conducting regime in high electric fields but increases it in the dislocation conducting regime in low electric fields. These results indicate that the optimal carbon concentration of buffer layers depends on the target electric field. [ABSTRACT FROM AUTHOR]

The epitaxial growth of heavily Ge‐doped GaN films using pulsed sputtering deposition (PSD) on AlN (0001)/sapphire substrates is presented and the correlations among their structural, electrical, and optical properties are investigated. High‐quality Ge‐doped PSD‐GaN films are grown with electron concentrations of 0.33–4.4 × 1020 cm−3. Notably, cathode luminescence spectra reveal the absence of defect‐induced emissions associated with Ga vacancies (2.3–2.4 eV), consistent with the high electron mobilities of the films. As the electron concentration increases, the optical bandgap also increases. This correlation can be explained by combining the theoretical formulas for the Burstein–Moss shift and the bandgap renormalization effects. Remarkably, the optical bandgap reaches 3.84 eV at 4.4 × 1020 cm−3 carrier concentration, marking the highest value reported for the optical bandgap of GaN. These results indicate the potential of heavy Ge doping in GaN via PSD for applications in highly transparent conductive layers and tunneling junctions within GaN‐based optoelectronic devices. [ABSTRACT FROM AUTHOR]

The mechanisms underlying the stability of the intensity imbalance between the left‐ and right‐circulating lasing modes in a GaN hexagonal microdisk resonator are investigated. Finite‐difference time‐domain simulations reveal that the imbalance is determined by the position of the point light source (seed light for lasing) inside the resonator. The coupling mechanism between the resonance modes and light from the source is clarified using numerical calculations. In addition, it is confirmed that light is highly coupled with one of the circulation modes when the source is placed off the bilateral symmetry axis of the mode near the side of the resonator, without any influence of the polarization of the source. The results indicate that the imbalance of the lasing modes observed in the experiments is determined by the position of the seed light near the side of the resonator, which must be spontaneous emission from impurities or defects, such as donor–acceptor pairs, donor‐bounded excitons, basal plane stacking faults, or the transition of free electrons in the conduction band to acceptor levels et al. [ABSTRACT FROM AUTHOR]

This is the first report on the current saturation behavior observed in the forward characteristics of polarization superjunction (PSJ)‐based hybrid PiN‐Schottky GaN power diodes fabricated on Sapphire. In the current saturation region, most of the applied anode voltage is dropped across the regions immediately adjacent to the edge of the doped P‐GaN region closest to the cathode. This significant potential drop occurs within a short distance, resulting in a high electric field and depletion of electrons, causing the current saturation behavior via velocity saturation in these PSJ hybrid diodes. [ABSTRACT FROM AUTHOR]

Flexible MicroLEDs hold great promise as a novel tool for advancing optogenetics, enabling applications in biology. Such applications require the technology to assemble MicroLEDs onto ultra‐thin biocompatible films that conform to the delicate and uneven brain and biological tissues. In this study, a batch transfer technique using thermal release sheets is propsed. By optimizing the gate structures that support the hollow structure, a method to transfer MicroLEDs to the film without causing damage is established. It is successfully demonstrated that the flexible MicroLED films, fabricated using this technique, can be placed on the brain surface of transgenic mice and induce neural activity through optogenetic stimulation. [ABSTRACT FROM AUTHOR]

The Na‐flux method is expected to be a key GaN growth technique for obtaining ideal bulk GaN crystals. Herein, the structural quality of the latest GaN crystals grown using the Na‐flux method and, for the first time, the characteristics of a vertical transistor fabricated on a GaN substrate grown using this method are discussed. Vertical transistors exhibit normally off operation with a gate voltage threshold exceeding 2 V and a maximum drain current of 3.3 A during the on‐state operation. Additionally, it demonstrates a breakdown voltage exceeding 600 V and a low leakage current during off‐state operation. It is also described that the variation in the on‐resistance can be minimized using GaN substrates with minimal off‐angle variations. This is crucial for achieving the large‐current chips required for future demonstration of actual devices. In addition, the reverse I–V characteristics of the parasitic p–n junction diode (PND) structures indicate a reduction in the number of devices with a significant leakage current compared to commercially available GaN substrates. Finally, a circular GaN substrate with a diameter of 161 mm, surpassing 6 inches, grown using the Na‐flux method is demonstrated, making it the largest GaN substrate aside from those produced through the tiling technique. [ABSTRACT FROM AUTHOR]

An AlSiO/AlN‐interlayer gate stack formed on c‐plane GaN metal‐oxide‐semiconductor (MOS) devices was previously developed to enhance the interface of the gate insulator. By utilizing this gate stack structure, a channel mobility of over 200 cm2 Vs−1 on c‐plane was obtained. However, the threshold voltage was negative because of the polarization charge at the AlN/GaN interface. This study extends the application of this gate stack structure to the nonpolar m‐plane, which is obtained from a trench sidewall. Both a high channel mobility of 150 cm2 Vs−1 and a threshold voltage of 1.3 V is successfully achieved, normally‐off operation. This achievement holds significant promise for the gate structure of a GaN trench‐gate MOS field‐effect transistor (MOSFET). The limiting factor of the channel mobility is Coulomb scattering in a low electric field, whereas surface roughness scattering is dominant in a higher field. [ABSTRACT FROM AUTHOR]

The scale of white foreign fibers in bobbin yarn is small, resulting in multiple types of data imbalance in the dataset. These imbalances include a severe imbalance of foreign fiber pixels compared to background pixels and an imbalance in the size target scale. Consequently, conventional semantic segmentation networks struggle to segment these fibers effectively. First, in tackling the scarcity of white foreign fiber instances within bobbin yarn samples, this research utilizes original foreign fiber images to train the DCGAN and generate adequate training samples. Secondly, a multiscale residual U-Net is constructed to extract foreign fiber features from different scales. The network is encouraged to learn semantic features at each scale and each layer of the decoding stage. This overcomes the problem of scale imbalance in the foreign fiber dataset and enhances the model's capability to extract weak semantic information from small targets. Thirdly, a weighted binary cross-entropy loss function is integrated into the network's training phase to rectify the class imbalance and refine segmentation performance. This function adjusts the weighting of foreign fiber pixel data, thereby addressing the disproportionate distribution between foreign fibers and background pixels within the dataset. Finally, the proposed method is experimentally validated using a dataset of white foreign fibers. The experimental results show that the proposed method achieves better results in the critical evaluation metrics, as evidenced by the accuracy of 97.52 %, the MIoU of 95.26 %, the DICE coefficient of 81.29 %, and the F1 Score of 84.92 %. These statistics demonstrate the method's efficacy in achieving high-precision segmentation of white foreign fibers. [ABSTRACT FROM AUTHOR]

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Prognostics and health management of general rotating machinery have been studied over time to improve system stability. Recently, the excellent abnormal diagnosis performance of artificial intelligence (AI) was demonstrated, and therefore, AI-based intelligent diagnosis is now being implemented in these systems. AI models are trained using large volumes of data. Therefore, we propose a transformer-based generative adversarial network (GAN) model with a multi-resolution short-time Fourier transform (multi-STFT) loss function to augment the vibration data of rotating machinery to facilitate the successful learning of deep learning models. We constructed a model with a conditional GAN structure, which is transformer based, for learning the feature points of vibration data in the time-series domain. In addition, we applied the multi-STFT loss function to capture the frequency features of the vibration data. The generated data, which adequately captured the frequency features, were used to augment the training data to improve the performance of a deep learning classifier. Furthermore, by visualizing the generated vibration data and comparing the visualizations to those of the vibration data obtained from real machinery, we demonstrated that the generated data were indistinguishable from the actual data. [ABSTRACT FROM AUTHOR]

With the immense growth of Artificial Intelligence (AI), especially in image processing, many people have taken advantage of its numerous capabilities. It is used in different domains, including generative imaging (GenI). Some of the crucial applications of GenI include content creation, shadow synthesis, and in-painting. However, image synthesis comes with flaws. Due to the complexity of generating images, the process can have very high processing times. This paper analyzes the shortcomings by profiling LaMa,? an in-painting model run on CPUs. We provide insights by highlighting the bottlenecks that hinder performance. Our main finding is that the mkldnn_convolution layer takes up most of the time, indicating that speeding up this layer's computations could drastically improve overall processing time. [ABSTRACT FROM AUTHOR]

In this article, we propose StylishGAN, a generative adversarial network that generates a fashion illustration sketch given an actual photo of a human model. The generated stylish sketches not only capture the image style from real photos to hand drawings with a cleaner background, but also adjust model's body into a perfectly proportioned shape. StylishGAN learns proportional transformation and texture information through a proposed body-shaping attentional module. Furthermore, we introduce a contextual fashionable loss that augments the design details, especially the fabric texture, of the clothing. To implement our method, we prepare a new fashion dataset, namely, StylishU, that consists of 3578 paired photo–sketch images. In each pair, we have one real photo collected from the fashion show and one corresponding illustration sketch created by professional fashion illustrators. Extensive experiments show the performance of our method qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation systems and information technology, the security of road traffic signal systems has increasingly attracted the attention of managers and researchers. This paper proposes a new method for detecting attacks on traffic signal systems based on game theory and Generative Adversarial Networks (GAN). First, a game theory model was used to analyze the strategic game between the attacker and the defender, revealing the diversity and complexity of potential attacks. A Bayesian game model was employed to calculate and analyze the attacker's choice of position. Then, leveraging the advantages of GAN, an adversarial training framework was designed. This framework can effectively generate attack samples and enhance the robustness of the detection model. Using empirical research, we simulated the mapping of real traffic data, road network data, and network attack data into a simulation environment to validate the effectiveness of this method. In a comparative experiment, we contrasted the method proposed in this paper with the traditional Support Vector Machine (SVM) algorithm, demonstrating that the model presented here can achieve efficient detection and recognition across various attack scenarios, with significantly better recall and F1 scores compared to traditional methods. Finally, this paper also discusses the application prospects of this method and its potential value in future intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Graphic design, as a product of the burgeoning new media era, has seen its users' requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image's color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%–10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design. [ABSTRACT FROM AUTHOR]

Purpose: The visualization of an anomaly area is easier in anomaly detection methods that use generative models rather than classification models. However, achieving both anomaly detection accuracy and a clear visualization of anomalous areas is challenging. This study aimed to establish a method that combines both detection accuracy and clear visualization of anomalous areas using a generative adversarial network (GAN). Methods: In this study, StyleGAN2 with adaptive discriminator augmentation (StyleGAN2-ADA), which can generate high-resolution and high-quality images with limited number of datasets, was used as the image generation model, and pixel-to-style-to-pixel (pSp) encoder was used to convert images into intermediate latent variables. We combined existing methods for training and proposed a method for calculating anomaly scores using intermediate latent variables. The proposed method, which combines these two methods, is called high-quality anomaly GAN (HQ-AnoGAN). Results: The experimental results obtained using three datasets demonstrated that HQ-AnoGAN has equal or better detection accuracy than the existing methods. The results of the visualization of abnormal areas using the generated images showed that HQ-AnoGAN could generate more natural images than the existing methods and was qualitatively more accurate in the visualization of abnormal areas. Conclusion: In this study, HQ-AnoGAN comprising StyleGAN2-ADA and pSp encoder was proposed with an optimal anomaly score calculation method. The experimental results show that HQ-AnoGAN can achieve both high abnormality detection accuracy and clear visualization of abnormal areas; thus, HQ-AnoGAN demonstrates significant potential for application in medical imaging diagnosis cases where an explanation of diagnosis is required. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a broadband high‐efficiency power amplifier (PA) with filtering characteristics is proposed. This PA is designed using a series of inverse continuous modes (SICMs) and adds a novel coupled microstrip line filter structure. The terminal of this structure is composed of microstrip lines cascaded to improve the out‐of‐band suppression effect. This filter can achieve low in‐band insertion loss, high passband selectivity, as well as deep stopband rejection performance simultaneously. A PA was designed and implemented using a 10 W GaN HEMT device, verifying its wideband characteristics. The measurement result shows that the 39.6–41.6 dBm output power (Pout), 61.6%–73.7% drain efficiency (DE), and 8.6–10.8 dB gain are achieved at 2.2–2.9 GHz. [ABSTRACT FROM AUTHOR]

X-ray weld images often suffer from low contrast, poor quality, and other limitations. Additionally, due to factors such as inspection equipment and cost constraints, the number of available samples for weld images is limited, which may not suffice for deep learning training requirements. To address these challenges and enable intelligent weld seam image detection, a comprehensive approach is proposed, comprising a data enhancement method and an enhanced YOLOv8s defect detection algorithm. The method unfolds as follows: firstly, 4k+ images are reorganised into weld defect images with consistent size utilising a weld defect image feature reorganisation method, which retains as much of the weld defect information. Subsequently, data enhancement of X-ray weld defect images is carried out through translation, luminance, noise, mirroring, and cropping degradation models and the improved SRGAN model. Finally, a novel data enhancement method is proposed based on the Multi-Head Self-Attention (MHSA), the detection head, and the Channel-wise Convolutional Spatial Gating (C2f_SGE) in the improved YOLOv8s model. This enhanced defect detection method, YOLOv8sDMS, compared with the YOLOv8s model, indicates improvements in mAP50 by 0.035, mAP50:95 by 0.021, Precision by 0.037, and Recall by 0.050. The proposed improved method achieves superior results in weld defect detection. [ABSTRACT FROM AUTHOR]

With the advancement of Generative Adversarial Networks (GANs), generated face images by models like AttGAN have become more realistic, posing challenges in detecting fake faces from real ones. In this paper, we explore the transferability of pretrained Convolutional Neural Networks (CNNs) for the task of detecting fake face images generated by the AttGAN model. In this study, we investigate the effectiveness of pretrained ResNet-50 and VGG-19 models trained on ImageNet and VGG face dataset in extracting useful features to be used for classifying whether a given face image is genuine or fake face images. In particular, the performance of pretrained models is evaluated in terms of accuracy, precision and recall. Our experimental results demonstrate the potential of pretrained ResNet-50 model with ImageNet weights for detecting fake face images generated by AttGAN and highlight their transferability for this challenging task. That is, a unified model based on the pretrained ResNet-50 model with ImageNet weights designed to detect fake face images with various attribute modifications achieved an average precision of 96.9% and a recall rate of 97.2%. In comparison, the model developed specifically for detecting fake face images with a single attribute modification achieved a precision of 96.7% and a recall rate of 96.9% on LFW dataset. The study demonstrates that employing a unified model for attribute detection in facial analysis tasks yields promising results, showcasing its potential as a simpler and more efficient alternative to developing separate models for individual attributes. [ABSTRACT FROM AUTHOR]

AbstractUtilizing Technology Computer Aided Design (TCAD) software, this study determines the optimal thickness and doping concentration for the intrinsic gallium nitride (GaN) layer in a p-i-n GaN diode to improve detection efficiency and reliability. The study examines energy loss and the transient current response induced by alpha and 3H particles within the GaN p-i-n diode. Additionally, TCAD elucidates the transient current behavior arising from electron-hole pair generation, utilizing the Stopping and Range of Ions in Matter SRIM-2013 linear energy transfer distribution as a guide. The simulations disclose that the tailing effect in the transient current response results from radiation-induced hole accumulation. Elevated applied bias results in increased transient current pulse amplitude, attributed to an extended depletion region that boosts carrier collection. These insights are expected to drive advancements in GaN-based neutron detector technology, essential for advancing nuclear and space science applications in the next generation. [ABSTRACT FROM AUTHOR]

To solve the problems of feature loss and color difference after image dehazing and poor dehazing effect in real hazy images, a method UVCGAN-Dehaze is proposed for unpaired image dehazing. In the proposed model, the generator learns the relationship between image features and low-frequency features at multiple scales by combining the U-Net with the transformer. We strengthen the connection between spatial and channel features by adding Convolutional Block Attention Module(CBAM) before the skip connection of each layer of U-Net of the generator and adaptively adjusting the weight allocation of the feature map, so that the image after dehazing can better retain the detailed information of the image. Simultaneously, in order to reduce the feature loss and color difference after image dehazing, we propose a loss function named Feature Laplacian Perception (FLP) loss, which jointly constrains the generator by fusing the perception loss and Laplacian loss, so that the generated image reduces the feature loss and color difference. The method not only generates dehazing images with high perceptual quality in terms of important regions and details, but also avoids content and color loss. Extensive experiments verify the effectiveness of the method on synthetic images and real hazy images, and the proposed method achieves better dehazing results when compared with the experimental results of previous typical methods. [ABSTRACT FROM AUTHOR]

GaN nanostructures hold significant promise in advancing nanoscale light‐emitting devices. However, significant progress remains elusive, possibly due to the absence of innovative approaches. Beyond the smaller size of GaN nanostructures, the main interest lies in growing InGaN active layers on semi‐ and non‐polar orientations. Nevertheless, it is evident that while leveraging such a method holds initial promise, this approach may eventually reach its limits, prompting the need for fresh perspectives. In this manuscript, electrochemical etching is utilized to fabricate porous GaN nanopyramids. Given the absence of prior studies on electrochemical etching of GaN nanostructures, its etching behavior is investigated, revealing its selectivity concerning doping concentration and geometry. Furthermore, how the choice of electrolyte can differently impact the optical response of the etched template is explored. Subsequently, an InGaN/GaN quantum wells shell is grown as the active region. The significance of the approach is evidenced by a tenfold enhancement in photoluminescence emission. Porous nanopyramids demonstrate a higher internal quantum efficiency of 44.7% and faster radiative recombination time. Additionally, finite difference time domain simulation reveals a twofold increase in light extraction efficiency. This research will lead the way toward the development of a new class of nanostructures offering potential advancements in optoelectronic devices. [ABSTRACT FROM AUTHOR]

The formation of laser induced periodic surface structures (LIPSS) on GaN thin film (thickness ~ 1.6 μm) was investigated using a picosecond laser (pulse duration about 40 ps) at a wavelength of 355 nm. Through a parametric study (variations of pulse number from 10 to 1000 and of fluence from 130 up to 300 mJ.cm− 2), the existence domain of low spatial frequency LIPSS (LSFL with period Λ = 265 nm) was determined. Optimized parameters were found to be a beam fluence of 230 mJ.cm− 2 for 100 pulses to obtain highly regular LIPSS without damaging the film. Moreover, original LSFL structures on GaN thin film were found to be decorated with circular nano-structures (diameter ⌀ about 70 nm) referred to as nano-puddles. Investigation of the nano-puddles on the LIPSS was performed using scanning electron microscopy (SEM) and atomic force microscopy (AFM). Finally, the morphologies of the decorated LIPSS formed under vacuum (pressure P = 10− 4 mbar) were compared to the one formed under atmospheric pressure and the phenomena leading to the formation of the nano-puddles are discussed. Highlights: LIPSS formation on GaN film under vacuum with a picosecond laser. Influence of vacuum on LSFL size. Nano-puddle formation on GaN LSFL. [ABSTRACT FROM AUTHOR]

Electron transport characteristics were measured at room temperature applying nanosecond duration electrical pulses on two-dimensional electron gas (2DEG) channel in AlGaN/GaN heterostructures grown on SiC substrate. Crystal lattice self-heating effects were minimised by usage of nanosecond duration pulses being as short as 2 ns. We were able to reach highest current densities of 1.24 A/mm and 1.80 A/mm for different type of AlGaN/AlN/GaN and AlGaN/GaN heterostructures under the electric field of 57 kV/cm and 69 kV/cm, respectively. Even stronger electric field usage was allowed by the pulse source employed (up to 75 kV/cm for channel length of 12.5 μ m), however, that was not needed as the electric current near saturation was observed. The three-dimensional (3D) electron channels in GaN epilayer on GaN substrate were also investigated under pulsed electric field reaching up to 216 kV/cm. The electron drift velocity was found from experimental charge current density characteristics assuming that Hall electron density does not change in the sample under high electric fields. Maximum drift velocities for the 2DEG channels in AlGaN/GaN and AlGaN/AlN/GaN samples were found to be up to 1.2 × 10 7 cm/s at 69 kV/cm and 1.1 × 10 7 cm/s at 57 kV/cm, respectively, while, the highest value of ∼ 2.6 × 10 7 cm/s at 216 kV/cm was obtained for the 3D GaN channels. In the region of high electric fields the electron drift velocity in 2DEG channel was smaller than that in 3D channel as the result of stronger hot-phonon effect. [ABSTRACT FROM AUTHOR]

A new model called the Transformer-Unet Generative Adversarial Network (TUGAN) is proposed for super-resolution reconstruction of digital elevation models (DEMs). Digital elevation models are used in many fields, including environmental science, geology and agriculture. The proposed model uses a self-similarity Transformer (SSTrans) as the generator and U-Net as the discriminator. SSTrans, a model that we previously proposed, can yield good reconstruction results in structurally complex areas but has little advantage when the surface is simple and smooth because too many additional details have been added to the data. To resolve this issue, we propose the novel TUGAN model, where U-Net is capable of multilayer jump connections, which enables the discriminator to consider both global and local information when making judgments. The experiments show that TUGAN achieves state-of-the-art results for all types of terrain details. [ABSTRACT FROM AUTHOR]