Control of GaN inverted pyramids growth on c-plane patterned sapphire substrates

Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985 ℃ and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

IMTNet:Improved Multi-Task Copy-Move Forgery Detection Network with Feature Decoupling and Multi-Feature Pyramid

Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

Experimental Study on Improving Performance and Productivity of Pyramid Solar StillUsing Rotation Technique

Globally,potable water scarcity is pervasive problem.The solar distillation device is a straightforward apparatus that has been purposefully engineered to convert non-potable water into potable water.The experimental study is distinctive due to the implementation of a rotational mechanism within the pyramidal solar still(PSS),which serves to enhance the evaporation and condensation processes.The objective of this research study is to examine the impact of integrating rotational motion into pyramidal solar stills on various processes:water distillation,evaporation,condensation,heat transfer,and energy waste reduction,shadow effects,and low water temperature in saline environments.Ultimately,the study aims to enhance the production of distilled water.An economic evaluation was undertaken in order to ascertain the extent of cost reduction.Experiments measuring freshwater productivity and thermal performance were conducted over a three-month period at the University of Science and Technology in Tehran.The entire pyramid structure was rotated using a direct current motor driven by a photovoltaic cell.The research methodology entailed the operation of a PSS with varying rotational speeds(0.125,0.25,1,and 1.5 rpm)and without rotation,from 9 am to 4 pm.The findings suggested that the productivity of the distillation apparatus in terms of distilled water increased as the rotation speed rose,with the most pronounced increase occurring at 1 rpm in comparison to the other conditions.The presence of turbulence in the water enhanced the heat transfer occurring between the absorber plate and thewater.At 2:00 p.m.on an experimental day,this effect was observed when the absorber plate temperature reached 79.1°C at 1.5 rpm.In contrast,its temperature decreased to 78°C when not in a state of rotation,as the intensity of solar radiation was higher in the non-rotation state.At 1 rpm,the solar pyramid distiller achieved a 30.2%increase in output compared to its non-rotating state.At 1 rpm,the distiller achieved a 20.6%increase in output compared to 0.25 revolutions per minute.In addition to the control condition,the thermal efficiency of the solar still varied as follows:at 1,1.5,0.25,and 0.125 rpm,it was 46.2%;at 44.2%,37.8%;at 35.3%;and at 36.6%,respectively.Furthermore,distilled water generated by a pyramid solar still with rotation(PSSR)is priced at$0.03 per liter,whereas it costs$0.0317 per liter when produced by a pyramid solar still without rotation(PSS without R).

Energy and Exergy Analysis of Pyramid-Type Solar Still Coupled with Magnetic and Electrical Effects by UsingMatlab Simulation

In the face of an escalating global water crisis,countries worldwide grapple with the crippling effects of scarcity,jeopardizing economic progress and hindering societal advancement.Solar energy emerges as a beacon of hope,offering a sustainable and environmentally friendly solution to desalination.Solar distillation technology,harnessing the power of the sun,transforms seawater into freshwater,expanding the availability of this precious resource.Optimizing solar still performance under specific climatic conditions and evaluating different configurations is crucial for practical implementation and widespread adoption of solar energy.In this study,we conducted theoretical investigations on three distinct solar still configurations to evaluate their performance under Baghdad’s climatic conditions.The solar stills analyzed include the passive solar still,themodified solar still coupled with a magnetic field,and themodified solar still coupled with bothmagnetic and electrical fields.The results proved that the evaporation heat transfer coefficient peaked at 14:00,reaching 25.05 W/m^(2).℃for the convention pyramid solar still(CPSS),32.33 W/m^(2).℃for the magnetic pyramid solar still(MPSS),and 40.98 W/m^(2).℃for elecro-magnetic pyramid solar still(EMPSS),highlighting their efficiency in converting solar energy to vapor.However,exergy efficiency remained notably lower,at 1.6%,5.31%,and 7.93%for the three still types,even as energy efficiency reached its maximum of 18.6%at 14:00 with a corresponding peak evaporative heat of 162.4 W/m^(2).

基于多尺度融合金字塔焦点网络的接触网零部件检测

作为高铁牵引供电系统的重要组成部分,接触网系统承担着向动车组传输电能的重要功能.实际工程运营表明,受弓网交互产生的持续冲击以及外部环境的影响,接触网支撑部件可能会出现“松、脱、断、裂”等缺陷,导致接触网结构可靠性下降,严重影响接触网系统稳定运行.因此,及时精确定位接触网支撑部件(CSCs),对保障高铁安全运行和完善接触网检修维护策略具有重大意义.然而,CSCs的检测通常面临着零部件种类多、尺度差异大、部分零部件微小的问题.针对以上问题,本文提出一种基于多尺度融合金字塔焦点网络的接触网零部件检测算法,将平衡模块和特征金字塔模块相结合,提高对小目标的检测性能.首先,设计了可分离残差金字塔聚合模块(SRPAM),用于优化模型多尺度特征提取能力、扩大感受野,缓解CSCs检测的多尺度问题;其次,设计了一种基于平衡特征金字塔的路径聚合网络(PA-BFPN),用于提升跨层特征融合效率和小目标检测性能.最后,通过对比试验、可视化实验和消融实验证明了所提方法的有效性和优越性.其中,所提的MFPFCOS在CSCs数据集上的检测精度(mAP)能够在达到48.6%的同时,实现30的FLOPs(Floating point operations per second),表明所提方法能够在检测精度和检测速度之间保持良好的平衡.

基于GAN和多尺度空间注意力的多模态医学图像融合

针对多模态医学图像融合过程中多尺度特征和纹理细节信息丢失的问题,提出一种基于生成对抗网络和多尺度空间注意力机制的图像融合算法。首先,生成器采用自编码器结构,分别利用编码器和解码器对输入图像进行特征提取、融合和重建,生成融合图像;其次,整个对抗网络框架采用双鉴别器结构,使得生成器生成的融合图像同时保留多个模态图像的显著特征;最后,构建一种多尺度空间注意力机制作为编码器进行特征提取的基本模块,利用多尺度结构充分捕获并保留源图像的多尺度特征,并且引入空间注意力机制更好地保留源图像的结构和细节信息。哈佛大学全脑图谱数据库上的实验结果表明:所提算法生成的融合图像不仅纹理细节更为丰富,有助于人类视觉观察,而且在3种不同类型的医学图像融合任务上平均梯度、峰值信噪比、互信息、视觉信息保真度等客观评价指标的平均值分别达到0.3023、20.7207、1.4414、0.6498,与其他先进的算法相比具有一定的优势。

Steerable Pyramid分解在储层断裂检测中的应用

在隐蔽油气藏勘探中,正确刻画储层断裂对于油气勘探和开发都有着至关重要的作用.储层断裂在地震数据中表现为边缘特征.但现今多尺度边缘检测的基本理论和方法都有其本身的局限性.本文详细阐述了Steerable Pyramid分解用于储层断裂检测的基本原理和方法.输入的三维地震切片经过Steerable Pyramid分解后,不仅可以分析不同尺度不同方向上断裂的特征及走向,还可以通过重构得到经S函数控制的断裂信息,该断裂信息不仅仅是断裂的边缘检测,而是增强的断裂本身.将该算法应用于实际地震切片的断裂检测,得到了清晰的断裂信息,完整细致地展现了这些地质类型的特征,为构造解释、地震相表征及储层预测提供了有用信息.

分布式存储系统中基于Pyramid码的局部性修复编码

为了提高分布式存储系统的存储可靠性以及故障节点的修复效率,提出一种基于Pyramid码的局部性修复编码方案。该编码方案采用Pyramid码的最小可实现编码结构,划分局部修复组,确保较低的修复局部性并实现故障节点的快速修复。性能分析表明,基于Pyramid码的局部性修复编码可实现存储系统中多个故障节点的快速修复,具有较低的修复局部性,且相对于三副本复制策略以及简单再生码,基于Pyramid码的局部性修复编码在存储开销和修复带宽开销方面的性能更优。

Steerable Pyramid分解地震随机噪声衰减——基于局部Laplace先验概率密度模型

简单介绍了具有多尺度与多方向性特点的Steerable Pyramid分解和重构的基本原理。采用softLMAP阈值将其应用于地震数据随机噪声衰减中,进行了仿真计算和实际资料的处理并与自适应BayesShrink阈值及小波域softLMAP阈值去噪进行比较。结果证明利用Steerable Pyramid分解softLMAP阈值能比较彻底地去掉噪声,去噪后的图像边缘保持良好,滤除噪声同时还保留了有效部分,去噪效果良好,且易于实现,在地震资料处理中具有一定的可行性和应用前景。

EXPyramid:一种灵活的基于阵列结构的高容错低修复成本编码方案

在诸如P2P存储、网络存储以及云存储等大规模分布式存储系统中,带宽往往成为系统的性能瓶颈之一.纠删码拥有很高的存储空间利用率,但是过高的修复成本和读取成本使得纠删码技术在大规模分布式存储系统中的应用受到限制.为此,从降低系统修复成本的角度出发,在Pyramid码的基础上,提出了一种新的基于阵列结构的编码方案EXPyramid,并从理论上证明了判断失效模式是否为不可完全修复模式的充分条件.此外,针对EXPyramid码分别提出了一种最小修复成本算法和基于广度优先搜索的最小读取成本算法.分析结果表明,EXPyramid码能够有效地降低系统的修复成本和读取成本,有较强的容错能力,且构造灵活,结构规则易实现,具有很强的实际应用价值.

基于LWT-QR分解和Laplacian Pyramid的水印算法

目的为了提高数字水印的鲁棒性和不可见性,提出一种基于Laplacian Pyramid和LWT-QR分解的水印算法。方法首先对宿主图像进行2层Laplacian Pyramid分解,取其第2层Laplacian残差图像进行一层LWT分解,取其低频子带进行大小为4×4的无重叠分块处理。然后,基于提升小波系数的相关属性,再对每个选中的低频子块进行QR分解,取分解后R矩阵的第1行为目标进行水印的嵌入,同时对水印进行Arnold置乱,置乱后的水印图像嵌入到R矩阵的第1行元素中。结果嵌入水印后图像的PSNR能够达到45 d B,而且该方案对常见的信号处理攻击有较好的鲁棒性,NC均值在0.9以上。结论理论分析和大量的实验数据表明,该方案能够很好地改善图像操作过程中的鲁棒性和不可见性。

Steerable Pyramid分解方法及其在断层检测中的应用

分析了Steerable Pyramid分解用于断层检测的基本原理.输入的三维地震时间切片经过Steerable Pyramid分解后,不仅可以分析不同尺度不同方向上断层的特征及走向,还可以通过重构得到断层信息.该信息不仅是断层的边缘检测,而且将断层直接提取出来.在去除了断层信息后,该方法还可用于河道砂的沉积成像.将该方法应用于一张实际的地震振幅属性切片,结果证明将Steerable Pyramid分解用于储层断层检测与沉积成像是成功的.但是,处理结果对空间平滑参数都很敏感,参数的最优选择要根据输入图像的特征而变.

Enhancement of Rice Germplasm by Pyramiding the Cultivars with Multi-resistance to Orseolia oryzae (Wood-Mason)

[ Objective] This study was to breed rice cultivars with multi-resistance to Orseolia oryzae （Wood-Mason）. [ Method] The Guangxi local cultivar GX-M001 （Jiangchao） with high resistance to Orseolia oryzae （Wood-Mason） was used to hybrid with the known resistance cultivars ＂Kangwenqingzhan＂ （harboring GM5 gene）, OB677（ harboring GM3 gene） from Sri Lanka, HT1350 and high yield end quality cultivar ＂ Guiruanzhan＂. [ Result] Through pyramiding the multi-resistant genes via routine hybridization, the general resistances of the hybrids were remarkably enhanced. The grades of resistance were also improved, many of the combinations were endowed with a resistance at immune level （grade 0） ; and interestingly, the respective hybridization of GX-M001 （high resistance） with OB677（ medium resistance） and HT1350（suscepti- ble） also generate two lines at immune level, which is probably the effects of additive effects of genes.[ Conclusion] By routine hybridization, multiple genes were successfully pyramided, thus generating novel rice lines with multiple resistances. For the rice breeding scientists at the grass-roots level, the resistance-resistance pyramiding is an effective approach to breed high resistance cultivars.

Synthesis and photoluminescence properties of single-crystal ZnO hexagonal pyramids by PEG400-assisted thermal decomposition route

Large-scale synthesis of ZnO hexagonal pyramids was achieved by a simple thermal decomposition route of precursor at 240 oC in the presence of PEG400. The precursor was obtained by room-temperature solid-state grinding reaction between Zn（CH3COO）2-2H2O and Na2CO3. Crystal structure and morphology of the products were analyzed and characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, transmission electron microscopy （TEM） and high-resolution transmission electron microscopy （HRTEM）. The results of further experiments show that PEG400 has an important role in the formation of ZnO hexagonal pyramids. Difference between the single and double hexagonal pyramid structure may come from the special thermal decomposition reaction. The photoluminescence （PL） spectra of ZnO hexagonal pyramids exhibit strong near-band-edge emission at about 386 nm and weak green emission at about 550 nm. The Raman-active vibration at about 435 cm-1 suggests that the ZnO hexagonal pyramids have high crystallinity.

融合注意力机制的荔枝轻量化检测方法研究

针对荔枝果实个体小、生长密集和遮挡严重等特点,为了快速准确地实现荔枝的检测和计数,提出了一种融合注意力机制和多尺度特征图的网络模型。为了提高模型对遮挡和阴影环境下果实的识别准确率,将Coordinate Attention(CA)注意力机制嵌入至YOLOv4-Tiny模型。为了提高模型对小目标果实的检测精度,在特征金字塔Feature Pyramid Networks(FPN)结构中生成了两个更大尺度的特征图。试验结果表明:融合注意力机制的荔枝轻量化检测模型的准确率(Precision)、召回率(Recall)和平均精度(mAP)分别为92.92%、76.09%和88.51%。与YOLOv4-Tiny和YOLOv3模型相比,所构建的融合注意力机制的荔枝轻量化检测模型的平均检测精度分别高出8.84个百分点和3.87个百分点。该模型能够快速、精准地检测出果园环境中的荔枝,适用于果园中荔枝的识别和计数。

Steerable Pyramid分解域地震强随机噪声衰减

为解决油气地球物理勘探中地震资料的噪声问题,研究了Steerable Pyramid分解阈值去噪对地震资料强随机噪声背景下弱信号识别的影响。根据地震资料弱信号的Steerable Pyramid分解系数值较小的特点,从Steerable Pyramid分解域硬阈值去噪出发,采用降低阈值的方法保护Steerable Pyramid分解系数值较小的弱信号,同时针对时域噪声残留采用均值平滑的方法保证去噪效果。经过理论模型和实际资料实验,得出在Steerable Pyramid分解域采用硬阈值降阈值+均值滤波的方法可以从较强的噪声背景中识别出弱信号,并且保证信噪比有足够的提高;在去噪的同时,不但连续性好,反射强的同相轴得到了增强,而且即使连续性差,反射弱的同相轴也得到了有效的保留。采用本方法去噪伪影较少,剖面的保真度较高。

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

一种针对SAR图像的舰船目标检测算法

由于环境复杂、舰船目标散焦和尺度的多样性,基于SAR图像的舰船目标检测仍然存在一些问题。提出了一种针对SAR图像的舰船目标检测算法。首先,基于可变形卷积构建舰船目标特征细化模块,提高对大长宽比姿态的舰船目标的特征提取能力;其次,在主干网络末尾引入了舰船空间金字塔聚合结构,增强对舰船目标的全局特征提取能力;最后,设计了尺度扩展特征金字塔网络,增强舰船浅层和深层特征信息的交互,提高对多尺度舰船目标的检测能力。实验结果表明,所提算法在HRSID数据集上的mAP达到了93.72%,F1分数达到了89.70%,优于所有比较算法,具有良好的检测效果。