检索结果-内蒙古大学图书馆

Unconventional Optical imaging ii 2020

作者： Borovytsky, Volodymyr Antonenko, Vitalii Optical Engineering Dept. National Technical University of Ukraine Igor Sikorsky KPI pr. Peremogy 37 Kyiv03 056 Ukraine

ISBN: (数字)9781510634756

ISBN: (纸本)9781510634749

The paper describes the design of an unconventional biologically inspired image sensor. It contains numerous optical channels similar to facets in a natural compound eye. Each channel has several photodetectors with pre-amplifiers and a microcontroller with a multi-channel analog-to-digital converter. The signals coming from the photodetectors in each channel are amplified, converted into a digital form and processed by a microcontroller. All channels independently perform parallel image processing and image analysis. All microcontrollers are attached to a microcontroller network. They send data through this network only if useful signals are registered. These microcontrollers can be reprogrammed to perform various image processing operations including gradient search, spatial filtration, temporal filtration, signal correlation, neural network simulation and others. This design completely differs from the traditional image sensor architecture, which includes a mega-pixel focal plane array with a sequential signal read-out and a multi-core digital signal processor. The proposed architecture can be considered as a big set of the equal channels - the "smart groups"of several pixels with read-out electronics and a digital microcontroller that can extract only the useful data and send it out. The working prototype of this image sensor has demonstrated ability to measure distribution of speed and direction of optical flow thought its field of view in the very short time. The advantages and possible applications of this sensor are also discussed. © COPYRIGHT SPIE.

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Entropy and Correlation Coefficients of Neutrosophic and Interval-Valued Neutrosophic Hypersoft Set with application of Multi-Attributive Problems

引用

Neutrosophic Sets and systems 2022年 49卷 341-356页

作者： Jafar, Muhammad Naveed Saeed, Muhammad Ghani, Tahir Department of Mathematics University of Management and Technology C-II Johar Town Lahore54000 Pakistan Department of Mathematics Lahore Garrison University DHA Phase-VI Sector C Lahore54000 Pakistan

In computational intelligence, machine learning, image processing, neural networks, medical diagnostics, and decision analysis, the ideas of correlation coefficients and entropy have practical applications. By applying hypersoft set (HSS) in neutrosophic environment provides a good model for describing and addressing uncertainties. In statistics, the correlation coefficient between two variables is crucial. Furthermore, the accuracy of the correlation assessment is dependent on data from the discourse set. The main focus of this study, is to develop entropy for (NHSS) and generalized correlation coefficient interval-valued neutrosophic hypersoft-set (IVNHSS). We proposed some theorems on entropy along with algorithms based on correlation and weighted correlation coefficients in the context of NHSs and IVNHSS. The validity and superiority are presented along with application and also comparison is made with existing approaches. © 2022, University of New Mexico. All rights reserved.

关键词： Correlation Coefficients Entropy Fuzziness Hypersoft Set Interval-valued neutrosophic hypersoft set MCDM Neutrosophic hypersoft set Neutrosophic Set Soft Set

来源：评论

学校读者我要写书评

暂无评论

High-Throughput, Resource-Efficient Multi-Dimensional parallel Architecture for Space-Borne Sea-Land Segmentation

引用

JOURNAL OF CIRCUITS systems AND COMPUTERS 2021年第2期30卷

作者： Zhang, Cunguang Jiang, Hongxun Pan, Riwei Cao, Haiheng Zhou, Mingliang Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100191 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Sea-land segmentation based on edge detection is commonly utilized in ship detection, coastline extraction, and satellite system applications due to its high accuracy and rapid speed. Pixel-level distribution statistics do not currently satisfy the requirements for high-resolution, large-scale remote sensing image processing. To address the above problem, in this paper, we propose a high-throughput hardware architecture for sea-land segmentation based on multi-dimensional parallel characteristics. The proposed architecture is well suited to wide remote sensing images. Efficient multi-dimensional block level statistics allow for relatively infrequent pixel-level memory access;a boundary block tracking process replaces the whole-image scanning process, markedly enhancing efficiency. The tracking efficiency is further improved by a convenient two-step scanning strategy that feeds back the path state in a timely manner for a large number of blocks in the same direction appearing in the algorithm. The proposed architecture was deployed on xilinx Virtex k7-410t to find that its practical processing time for a 32,768 x 1,024 remote sensing image is only about 0.4 s. The peak performance is 1.625 gbps, which is higher than other FPGA implementations of segmentation algorithms. The proposed structure is highly competitive in processing wide remote sensing images.

关键词： Space-borne processing remote sensing image sea-land segmentation multi-dimension parallel architecture

来源：评论

学校读者我要写书评

暂无评论

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

引用

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling algorithms for Decision Making;employing Natural Language processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

关键词：

来源：评论

学校读者我要写书评

暂无评论

image Distillation Based Screening for x-ray Crystallography Diffraction images

Image Distillation Based Screening for X-ray Crystallography...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Lingyao Chen Kang xu xiaoying Zheng Yongxin Zhu Yi Jing Shanghai Information Technology Research Center Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

The modern synchrotron radiation facilities are producing massive diffraction images, which present a severe problem for data processing due to the high dimensionality of imaging data. Feature recognition and selection based deep learning methods have been developed to analyze data automatically. One crucial step is to use AI to screen out the diffraction images without Bragg spots. This paper proposes a feature distillation based approach for screening. It helps to reduce over 40% raw data volume and greatly alleviates the post processing workload faced by scientists.

关键词： Heating systems Deep learning Diffraction imaging x-ray diffraction Data processing Crystallography

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

引用

International Conference on VLSI Design

作者： Ayushparth Sharma Kusum Lata The LNM Institute of Information Technology Jaipur India

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. This article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. The proposed platform has been implemented on xilinx Spartan-6 FPGA board and co-simulated with xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10× higher energy-efficiency compared to serial counterpart and reduced execution period by 70 ×. Clinical validation shows that parallel architecture introduces 5-7 % error in nodule characteristic determination in comparison to serial one.

关键词： Memory management Lung cancer Very large scale integration Streaming media Energy efficiency Real-time systems parallel architectures

来源：评论

学校读者我要写书评

暂无评论

Predictive Local Sharpening and Digital Holography

Predictive Local Sharpening and Digital Holography

引用

Conference on Unconventional imaging and Adaptive Optics

作者： Sulaiman, Sennan Gibson, Steve Spencer, Mark F. Northrop Grumman Space Syst Lasers & Sensors Subs Redondo Beach CA 90278 USA Univ Calif Los Angeles Mech & Aerosp Engn Los Angeles CA 90095 USA Air Force Res Lab Directed Energy Directorate Kirtland AFB NM USA

ISBN: (纸本)9781510638235

Digital holography and image sharpening have been used increasingly in recent years for wavefront sensing and imaging. Compared to conventional imaging and wavefront sensing techniques, digital holography and image sharpening require significantly fewer and simpler optical components to retrieve the complex field (i.e., both the amplitude and phase) and produce a focused image from an estimate of the phase aberrations present in the imaging system. A drawback for digital holography in real-time applications, such as wavefront sensing for high energy laser systems and high-speed imaging for target tracking systems, is the fact that digital holography and image sharpening are computationally intensive, requiring iterative virtual wavefront propagation to optimize sharpness criteria. Recently, it was shown that minimum variance wavefront prediction can be integrated with digital holography and image sharpening to reduce significantly the large number of costly sharpening iterations required to achieve near-optimal wavefront correction. This paper demonstrates further gains in computational efficiency with a new subspace sharpening method in conjunction with predictive dynamic digital holography for real-time applications. The method sharpens local regions of interest in an image plane by parallel independent wavefront correction on reduced-dimension subspaces of the complex field in a pupil plane. Results in this paper from wave-optics simulations show that the new subspace method produces results comparable to that from conventional global and local sharpening, and that subspace wavefront estimation and sharpening coupled with wavefront prediction achieves order-of-magnitude increases in processing speed.

关键词： Digital holography wavefront sensing image reconstruction prediction system identification

来源：评论

学校读者我要写书评

暂无评论

Accelerating image Encryption with AES Using GPU: A Quantitative Analysis 18th

Accelerating Image Encryption with AES Using GPU: A Quantita...

引用

Joint Conferences on 18th International Conference on Intelligent systems Design and applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Saxena, Aryan Agrawal, Vatsal Chakrabarty, Rajdeepa Singh, Shubhjeet Banu, J. Saira Vellore Institute of Technology Vellore India

ISBN: (纸本)9783030166595

From military imaging to sharing private pictures, confidentiality, integrity and authentication of images play an important role in the Internet of modern world. AES is currently one of the most famous symmetric cryptographic algorithms. Performing encryption/decryption of high-resolution images with AES is highly computation-intensive and time-consuming due to their large sizes and the underlying complexity of AES algorithm. Earlier increasing cryptographic complexity meant an increase in the processing time for encryption as well as decryption. Now, with the rise of the powerful GPUs containing thousands of high-performance and efficient cores and with the evolvement of GPU computing, the processing time has been reduced to a fraction of the time it used to take earlier. This paper presents a parallel implementation of AES using NVIDIA CUDA and OpenCV to encrypt images rapidly. We achieved an average speed up of four times on GPU as compared to CPU-only. © 2020, Springer Nature Switzerland AG.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Improved search space shrinking for medical image retrieval using capsule architecture and decision fusion

引用

ExPERT systems WITH applications 2021年 171卷 114543-114543页

作者： Bhattacharya, Jhilik Bhatia, Tarunpreet Pannu, Husanbir Singh Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India

Medical diagnosis is a challenging procedure that involves issues such as data imbalance, insufficient labels, obscure images, redundancy and lack of effective model training directions to shrink the semantic gap between human knowledge and computer algorithms. Due to privacy norms, sometimes medical images are difficult to access and therefore, retrieval of identical existing images from an existing repository is quite useful. This paper proposes a search space to narrow down the identical images in an archive by using (i) Capsule Networks, followed by a (ii) decision fusion with Wavelet-Discrete Cosine Transform (W-DCT) and Radon Barcodes (RBC). Empirical case study has been applied on IRMA (image Retrieval in Medical applications) dataset, imageCLEFMed-2009, containing 14,410 x-ray images, but the proposed method is generic, reproducible and scalable. Subjective and quantitative performance has been compared with the state-of-art and it has been found superior to yield accuracy of 92.83% and IRMA error of 124.25 for 193 class-code category. Thus, the proof-of-concept helps to improves diagnosis efficiency for automatic image retrieval and annotation by clustering similar images from the underlying repository.

关键词： Content-based image retrieval Machine learning image processing image retrieval

来源：评论

学校读者我要写书评

暂无评论

Enhanced GPU-Based Anti-Noise Hybrid Edge Detection Method

引用

COMPUTER systems SCIENCE AND ENGINEERING 2020年第1期35卷 21-37页

作者： Abed, Sa'ed Ali, Mohammed H. Al-Shayeji, Mohammad Kuwait Univ Coll Engn & Petr Dept Comp Engn POB 5969 Safat Kuwait

Today, there is a growing demand for computer vision and image processing in different areas and applications such as military surveillance, and biological and medical imaging. Edge detection is a vital image processing technique used as a pre-processing step in many computer vision algorithms. However, the presence of noise makes the edge detection taskmore challenging;therefore, an image restoration technique is needed to tackle this obstacle by presenting an adaptive solution. As the complexity of processing is rising due to recent high-definition technologies, the expanse of data attained by the image is increasing dramatically. Thus, increased processing power is needed to speed up the completion of certain tasks. In this paper,we present a parallel implementation of hybrid algorithm-comprised edge detection and image restoration along with other processes using Computed Unified Device Architecture (CUDA) platform, exploiting a Single Instruction Multiple Thread (SIMT) execution model on a Graphical processing Unit (GPU). The performance of the proposed method is tested and evaluated using well-known images from various applications. We evaluated the computation time in both parallel implementation on the GPU, and sequential execution in the Central processing Unit (CPU) natively and using Hyper-Threading (HT) implementations. The gained speedup for the naive approach of the proposed edge detection using GPU under global memory direct access is up to 37 times faster, while the speedup of the native CPU implementation when using shared memory approach is up to 25 times and 1.5 times over HT implementation.

关键词： Edge Detection GPU image processing Noise parallel processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：