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Effects of abnormal beams on writing qualities in massively-parallel e-beam systems

JOURNAL OF VACUUM SCIENCE & TECHNOLOGY B 2019年第6期37卷 061609-061609页

作者： Hasan, Md Nabid Lee, Soo-Young Ahn, Byung-Sup Choi, Jin Kim, Seom-Beom Jeon, Chan-Uk Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Samsung Elect Mask Dev Team 16 Banwol Dong Hwasung 18448 Kyunggi Do South Korea

While electron-beam (e-beam) lithography is widely used in transferring fine-feature patterns onto a substrate, its major drawback is the low throughput, especially for large-scale patterns. To increase the writing throughput, e-beam machines with massively-parallel beams were recently developed. In such a system, it is highly likely that some beams may not be normal, e.g., permanently on or off, a significant current fluctuation, beam-positioning error, etc. Therefore, it is crucial to understand how abnormal beams affect writing qualities. In this study, the effects of abnormal beams on the writing qualities are analyzed through an extensive simulation, comparing three different writing methods, single-row writing I, single-row writing ii, and multirow writing, to suggest ways of reducing their negative effects.

关键词： Lithography Electron beams Monte Carlo methods Transfer function matrix Electron scattering image processing

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parallel Harris Corner Detection on Heterogeneous Architecture 18th

Parallel Harris Corner Detection on Heterogeneous Architectu...

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18th International Conference on Computational Science (ICCS)

作者： He, Yiwei Ma, Yue Liu, Dalian Chen, Xiaohua Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing Peoples R China Beijing Union Univ Deans Off Beijing Peoples R China

ISBN: (纸本)9783319937014;9783319937007

Corner detection is a fundamental step for many image processing applications including image enhancement, object detection and pattern recognition. Recent years, the quality and the number of images are higher than before, and applications mainly perform processing on videos or image flow. With the popularity of embedded devices, the real-time processing on the limited computing resources is an essential problem in high-performance computing. In this paper, we study the parallel method of Harris corner detection and implement it on a heterogeneous architecture using OpenCL. We also adopt some optimization strategy on the many-core processor. Experimental results show that our parallel and optimization methods highly improve the performance of Harris algorithm on the limited computing resources.

关键词： Harris corner detection Heterogeneous architecture parallel computing OpenCL

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image processing and Machine Learning to investigate fibre distribution on fibre-reinforced shotcrete Round Determinate Panels

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CONSTRUCTION AND BUILDING MATERIALS 2018年 190卷 870-880页

作者： Manca, Mirko Karrech, Ali Dight, Phil Ciancio, Daniela Univ Western Australia Sch Civil Environm & Min Engn 35 Stirling Highway Crawley WA 6009 Australia

Fibres in fibre reinforced concrete (FRC) and fibre reinforced shotcrete (FRS) are known to increase the toughness of the composite material thanks to their ability to transfer tensile stresses even after cracks have opened. However, fibres are deemed responsible for uncertainty in mechanical performance of FRC and FRS. This uncertainty is ascribed to how the fibres are physically located with respect to the crack. In this study a novel approach is presented to measure the distribution of fibres from digital images. A computer code that features image processing and Machine Learning algorithms has been developed to extract: i) the 2D location, ii) the mode of failure, iii) the orientation, iv) the pull-out length for each single fibre and v) the total number of fibres bridging the crack. The Machine Learning Algorithm is trained with a database that is attached to this article. This methodology, apart from giving an understanding of how fibres are distributed over the crack, provides the main input of several numerical models that simulate the behaviour of FRC/FRS taking into account the position and orientation of each single fibre. The main advantage of this approach over existing methods is that the hardware required to carry out the analysis consists of a simple smartphone camera while an output with errors within a few thousands parts of the actual measures. The algorithm is employed to analyse the fibres' distribution for 9 Round Determinate Panels (RDP) made of wet-mix shotcrete. The spatial 2D-location of fibres in the crack is tested for randomness with a Monte Carlo procedure and the fibres' distribution is found to follow an Independent Random Process. For fibres that pulled-out from one side of the crack, a further 3D investigation has been carried out. The analysis on the fibres' orientation confirmed the conclusion found by other authors that fibres tend to align perpendicular to the direction of spraying. Furthermore, it is unlikely to find fibres per

关键词： image processing Machine Learning Fibre reinforced shotcrete Statistical distribution of fibres Monte Carlo test

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Informed Prefetching for distributed Multi-Level Storage Systems

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2018年第4期90卷 619-640页

作者： Al Assaf, Maen M. Jiang, Xunfei Qin, Xiao Abid, Mohamed Riduan Qiu, Meikang Zhang, Jifu Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Earlham Coll Dept Comp Sci Richmond IN 47374 USA Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA Al Akhawayn Univ Dept Comp Sci Ifrane Morocco Pace Univ Dept Comp Sci New York NY 10038 USA Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China

In this paper, we present an informed prefetching technique called IPODS that makes use of application-disclosed access patterns to prefetch hinted blocks in distributed multi-level storage systems. We develop a prefetching pipeline in IPODS, where an informed prefetching process is divided into a set of independent prefetching steps and separated among multiple storage levels in a distributed system. In the IPODS system, while data blocks are prefetched from hard disks to memory buffers in remote storage servers, data blocks buffered in the servers are prefetched through networks to the clients' local cache. We show that these two prefetching steps can be handled in a pipelining manner to improve I/O performance of distributed storage systems. Our IPODS technique differs from existing prefetching schemes in two ways. First, it reduces applications' I/O stalls by keeping hinted data in clients' local caches and storage servers' fast buffers (e.g., solid state disks). Second, in a prefetching pipeline, multiple informed prefetching mechanisms coordinate semi-dependently to fetch blocks (1) from low-level (slow) to high-level (fast) storage devices in servers and (2) from high-level devices in servers to the clients' local cache. The prefetching pipeline in IPODS judiciously hides network latency in distributed storage systems, thereby reducing the overall I/O access time in distributed systems. Using a wide range of real-world I/O traces, our experiments show that IPODS can noticeably improve I/O performance of distributed storage systems by 6%.

关键词： Informed prefetching Pipelining parallel storage systems distributed multi-level storage system

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Investigation of image processing techniques for glaucoma detection in human eyes 5

Investigation of image processing techniques for glaucoma de...

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5th International Conference on parallel, distributed and Grid Computing, PDGC 2018

作者： Kaushal, Shilpa Datt Sharma, Sunil Jain, Shruti Dept. of Electronics Comm. Engg. Jaypee University of Information Technology Distt. SolanHimachal Pradesh India

ISBN: (纸本)9781538660263

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection. © 2018 IEEE.

关键词： Ophthalmology

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An improved distributed storage and query for remote sensing data

An improved distributed storage and query for remote sensing...

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6th International Conference on Identification, Information and Knowledge in the Internet of Things (iiKI)

作者： Jing, Weipeng Tian, Dongxue Northeast Forestry Univ Coll Informat & Comp Engn Harbin 150040 Heilongjiang Peoples R China

With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processing methods have the problems of low efficiency and lack of scalability, so this paper uses open source big data technology to improve it. Firstly, the storage model of remote sensing image data is designed by using the distributed storage database HBase. Then, the grid index and the Hibert curve are combined to establish the index for the image data. Finally, the method of MapReduce parallel processing is used to write and query remote sensing images. The experimental results show that the method can effectively improve the data writing and query speed, and has good scalability. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： remote data distribute storage data query HBase mapreduce

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GPU-Accelerated Light-field image Super-resolution

GPU-Accelerated Light-field Image Super-resolution

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International Conference on Advanced Computing and Applications (ACOMP)

作者： Trung-Hieu Tran Mammadov, Gasim Sun, Kaicong Simon, Sven Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781538691861

Light-field imaging has become an emerging technology that brings great benefits to many fields, i.e. in photography, academia, and industry. However, these benefits come with the cost of high computation requirement that limits its applications in practice. This paper presents an accelerated solution for 4D light-field image super-resolution. The acceleration is achieved by the mean of parallel computation using graphics processing units. The selected algorithm is broken into functions which is suitable for parallel execution. Each of the functions is then transformed into GPU kernel and executed at each work-item which is associated with a pixel location in the proposed architecture. Using disparity maps extracted from input 4D light-field as an aid for super-resolution task, the proposed approach can successfully super-resolute an input 4D light-field by the factor of 4 horizontally and vertically. Two strategies, Y-RGB and RGB, are proposed to handle color images. Y-RGB is suitable for high-speed processing constraints while RGB is more preferable if output quality is the main concern. Experimental results show that the proposed approach can achieve the speed up of 203x and 71x compared to CPU implementation for Y-RGB and RGB strategy respectively. Regarding output quality, the proposed approach generates a shaper high-resolution image with more details compared to the baseline methods.

关键词： Spatial resolution Graphics processing units Imaging Estimation Computer architecture Three-dimensional displays

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A Memory Efficient parallel Particle-Based Volume Rendering for Large-Scale distributed Unstructured Volume Datasets in HPC Environments 18th

A Memory Efficient Parallel Particle-Based Volume Rendering ...

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18th Annual Asia Simulation Conference (AsiaSim)

作者： Yamaoka, Yoshiaki Hayashi, Kengo Sakamoto, Naohisa Nonaka, Jorji Kobe Univ Kobe Hyogo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9789811328534;9789811328527

In recent years, the size and complexity of the datasets generated by the large-scale numerical simulations using modern HPC (High Performance Computing) systems have continuously increasing. These generated datasets can possess different formats, types, and attributes. In this work, we have focused on the large-scale distributed unstructured volume datasets, which are still applied on numerical simulations in a variety of scientific and engineering fields. Although volume rendering is one of the most popular techniques for analyzing and exploring a given volume data, in the case of unstructured volume data, the time-consuming visibility sorting becomes problematic as the data size increases. Focusing on an effective volume rendering of large-scale distributed unstructured volume datasets generated in HPC environments, we opted for using the well-known PBVR (Particle-based Volume Rendering) method. Although PBVR does not require any visibility sorting during the rendering process, the CPU-based approach has a notorious image quality and memory consumption tradeoff. This is because that the entire set of the intermediate rendering primitives (particles) was required to be stored a priori to the rendering processing. In order to minimize the high pressure on the memory consumption, we propose a fully parallel PBVR approach, which eliminates the necessity for storing these intermediate rendering primitives, as required by the existing approaches. In the proposed method, each set of the rendering primitives is directly converted to a partial image by the processes, and then they are gathered and merged by the utilized parallel image composition library (234Compositor). We evaluated the memory cost and processing time by using a real CFD simulation result, and we could verify the effectiveness of our proposed method compared to the already existing parallel PBVR method.

关键词： Particle-based Volume Rendering Unstructured volume dataset 234 image composition In-situ visualization

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Fast Nonlocal Diffusion by Stacking Local Filters for image Denoising

Fast Nonlocal Diffusion by Stacking Local Filters for Image ...

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2019国际计算机前沿大会

作者： Peng Qiao Weichu Sun Yong Dou Rongchun Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology University of South China

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing highdefinition images. Based on the analysis of the distribution of the distance distrs between the similar patches and their reference patches from a semantic aspect, the large distrss was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion(TNLRD)with local filters. For image with 4096 × 4096 resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore,the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM.

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Real-time fusion of visible and thermal infrared images in surveillance applications on SoC hardware. 2

Real-time fusion of visible and thermal infrared images in s...

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Conference on Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies ii

作者： Moren, Konrad Perschke, Thomas Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (数字)9781510621886

ISBN: (纸本)9781510621886

Fusion of thermal infrared and visual images is an important technique for real-time surveillance applications. Since image fusion is used in many real-time night vision applications such as target detection, recognition and tracking, it is important to understand the processing requirements and provide computationally efficient methods. In this paper, we present a real-time image fusion system designed for night vision supervisory and monitoring purposes. The system is equipped with two image sensors: TV and IR (thermal infrared). We implement a processing pipeline on the NVIDIA Tegra TX2. The TX2 platform is equipped with a many-core NVIDIA GPU and multi-core ARM CPU. Additionally, we present the system architecture as well as the design process of the efficient, real-time multi-spectral signal-processing algorithm. The algorithm is based on the second-generation wavelets also called lifting scheme. We show also a novel parallelization approach to perform the calculations in place, so no auxiliary memory is needed. This enables a fast parallel and pipelined processing flow. We achieve a considerable speedup compared to an optimized CPU implementation. The experimental results show that the fusion system can realize real-time image fusion processing for dual channels images at the rate of 30 frames per second for the Full-HD images.

关键词： image Fusion DWT GPGPU Multicore CPU SoC

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