检索结果-内蒙古大学图书馆

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 7216-7227页

作者： Le Gal, Bertrand Univ Rennes Inria Lab IRISA UMR CNRS 6074 Rennes France

Low-density parity-check (LDPC) codes are error correction codes (ECC) with near Shannon correction performances limit boosting the reliability of digital communication systems using them. Their efficiency goes hand in hand with their high computational complexity resulting in a computational bottleneck in physical layer processing. Solutions based on multicore and many-core architectures have been proposed to support the development of software-defined radio and virtualized radio access networks (vRANs). Many studies focused on the efficient parallelization of LDPC decoding algorithms. In this study, we propose an efficient SIMD parallelization strategy for DVB-S2(x) LDPC codes. It achieves throughputs from 7 Gbps to 12 Gbps on an INTEL Xeon Gold target when 10 layered decoding iterations are executed. Simultaneously, the latencies are lower than 400 mu s. These performances are equivalent to FPGA-based solutions and overclass CPU and GPU related works by factors up to 5x .

关键词： Codes Decoding Kernel Iterative decoding Multicore processing Single instruction multiple data Computer architecture Throughput Standards Software LDPC decoding SIMD multicore DVB-S2 DVB-S2x high-throughput low-latency parallelization strategy

来源：评论

学校读者我要写书评

暂无评论

Calculating porosity and permeability from synthetic micro-CT scan images based on a hybrid artificial intelligence

引用

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第11期101卷 6591-6612页

作者： Mohyeddini, Amir Rasaei, Mohammad Reza Univ Tehran Inst Petr Engn Coll Engn Sch Chem Engn Tehran Iran

Nowadays, lattice Boltzmann is one of the standard and exact methods of simulation in micro-CT images of rock. However, it has a high weakness in run time. Therefore, the effort in this article is to reach a comprehensive substitute method for permeability calculation with less run time than the lattice Boltzmann method. The other purposes are the automation of processing.operations, preparation of images, and in the end, the calculation of porosity. The best way to achieve these outcomes is to use hybrid artificial intelligence. In this research work, comprehensive model architecture has been used to design a hybrid artificial intelligence to be able to calculate permeability and porosity in complex images. A thousand images were randomly generated with high complexity, which makes the model comprehensive and extensible, and image processing.was applied. After that, the lattice Boltzmann method as the direct simulation was selected. Finally, the convolutional neural network and multilayer perceptron based on a new and comprehensive model were evaluated for the first time;the mean squared error resulting from the evaluation of training data is 0.01, and the test data is 0.03. Expert systems have been used as a subset of artificial intelligence for automated image processing.and porosity calculation. In this way, problems related to the direct implementation of classical algorithms for image processing. models, and patterns related to machine learning and needing an expert were solved to an acceptable extent, and an error of less than 5% was achieved.

关键词： digital rock hybrid artificial intelligence image processing micro-CT scan permeability porosity

来源：评论

学校读者我要写书评

暂无评论

Algorithm for improving the quality of mixed noisy images 5

Algorithm for improving the quality of mixed noisy images

引用

5th International Conference on Applied Physics, Information Technologies and Engineering 2023, APITECH 2023

作者： Mamatov, N.S. Jalelova, M.M. Samijonov, A.N. Samijonov, B.N. Department of Digital Technology and Artificial Intelligence Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Kori Niyoziy Street 39 Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Amir Temur Avenue 108 Tashkent100084 Uzbekistan Sejong University South Korea Neungdong-ro 209 Gwangjin-gu Seoul Korea Republic of

digital images are one of the effective means of receiving, transmitting, exchanging and expressing information. However, the digital image is not always of the expected quality, that is, as a result of the influence of various factors, noises appear in the image. This significantly reduces the accuracy and quality of the data in the image. Segmentation and recognition of objects from a low-quality image creates many additional problems. For example, it requires image quality enhancement based on powerful pre-processing.algorithms. One such problem is to eliminate multiple noises in the image, and it is one of the pressing issues of digital image processing. This paper is devoted to solving the problem of image reconstruction by eliminating the mixed condition of Gaussian, salt-pepper and Poisson noises, which are common in digital images, in which one filter that optimally reduces each type of noise is selected and all combinations of them are applied to the mixed noise image to maximize the images. The idea of restoration has been put forward. The quality of the reconstructed images was evaluated by the NIQE criterion, which is one of the no-reference quality indicators, and a rule for automating the image processing.process was proposed based on its values. © Published under licence by IOP Publishing Ltd.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

In-Memory Sensing and Logic processing.in Negative Capacitance Phototransistors

引用

ADVANCED FUNCTIONAL MATERIALS 2025年

作者： Liu, Jie Han, Wushuang Hong, Enliu Deng, Ming Li, Ziqing Wu, Limin Fang, Xiaosheng Fudan Univ Dept Mat Sci Shanghai 200438 Peoples R China Fudan Univ State Key Lab Mol Engn Polymers Shanghai 200438 Peoples R China Fudan Univ Inst Optoelect Shanghai Frontiers Sci Res Base Intelligent Optoel Shanghai 200438 Peoples R China Inner Mongolia Univ Coll Chem & Chem Engn Hohhot 010021 Peoples R China

Nowadays, miniaturization, low power consumption and multi-scenario applications are urgent requirements for the development of the next generation of vision architecture. Eliminating the interface of image sensing, memory and digital processing.units and folding the entire signal chain into one device has become a promising strategy but remains challenging. Here, a 2D fully ferroelectric-gated negative capacitance (NC) phototransistor is demonstrated to enable the integration of in-memory sensing and logic processing. Attributed to the combined action of ferroelectric NC effect and strong photogating effect, the prototype tungsten disulfide (WS2) NC phototransistor exhibits a small subthreshold swing (SS) of 41.7 mV dec(-1) and high photodetectivity of 2.3 x 10(13) Jones. The quick switching of conductance states illustrates that such a device is suitable for ultralow-power nonvolatile memory with high program/erase ratio (>10(4)), long retention time (>10(4) s), stable cyclic endurance (>300 cycles) and ultralow programming energy (1.41 pJ/bit) and erasing energy (0.945 pJ/bit). The work demonstrates ferroelectric-optoelectronic engineering in 2D material to integrate sensing, memory, and logic all-in-one device, providing a promising implementation of vision system with low power consumption, low latency, and low system complexity.

关键词： 2D materials ferroelectric materials in-memory sensing and logic processing negative capacitance phototransistors vision system

来源：评论

学校读者我要写书评

暂无评论

Hardware Implementation of image processing.Morphological and Convolution Operations as SoC on FPGAs 24

Hardware Implementation of Image Processing Morphological an...

引用

7th International Conference on digital Medicine and image processing. DMIP 2024

作者： Moussa, Alfred M. Groves, Richard Rafla, Nader Electrical and Computer Engineering Department Boise State University BoiseID United States

ISBN: (纸本)9798400709586

Efficient image processing.architectures are consistently in demand across a multitude of applications, particularly those customized for resource-constrained systems-on-chip (SoC). The increasing need for high-performance image processing.in various sectors has driven the development of specialized architectures. However, deploying such architectures on platforms with limited resources, such as SoCs, poses significant challenges. Furthermore, the implementation of complex algorithms to handle large datasets using software solutions often leads to slower response times, prompting exploration into hardware implementations. Field-Programmable Gate Arrays (FPGAs) are becoming popular for hardware implementations because of their attributes: low latency, connectivity, parallel computing capabilities, and flexibility. Consequently, the utilization of FPGA-based implementations has resulted in faster and more efficient performance of unique architectures tailored to specific requirements. This paper presents a novel hardware/software co-design approach to implement erosion, dilation, and neighborhood image processing.operations on the FPGA development board, "Zedboard". In this approach, the FPGA is programmed by connecting it to a PC via USB, facilitating the transfer of an image pixel by pixel. The pixels are temporarily stored in on-chip DDR and accessed through DMA (Direct Memory Access) until they are requested by an interrupt signal from the image processing.IP, at which point they are moved to line buffers for faster processing. Once processed, the image is transmitted back to the PC via UART, facilitating pixel-by-pixel transfer for verification, where it is compared with a reference image generated using Python. This comparison confirms a 99.22% match between the processed image and the reference image, with the discrepancy occurring at the image's edges due to initial padding. Additionally, the time required to process the entire image was measured and displayed

关键词： System-on-chip

来源：评论

学校读者我要写书评

暂无评论

Recent Progress in Low Light image Enhancement algorithms Based on Deep Learning 16

Recent Progress in Low Light Image Enhancement Algorithms Ba...

引用

16th International Conference on digital image processing. ICDIP 2024

作者： Wang, Wencheng Yin, Boxin Li, Lun Wu, Xiaojin Li, Lei Liu, Hongtao He, Weikai Gao, Yuan Lan, Xiaowei Weifang University College of Machinery and Automation Weifang261061 China Qufu Normal University College of Engineering Rizhao276827 China School of Astronautics Shandong Jiaotong University China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

In low light environments, the collected images have characteristics such as low contrast, low signal-to-noise ratio, and loss of details, leading to an overall decline in image quality. Low light image enhancement aims to restore images with complete details and has gradually become a research hotspot in computer image processing. With the large-scale increase in data volume in recent years, deep learning based methods have gradually become mainstream. This article provides a detailed classification and analysis of low light image enhancement methods based on deep learning, sorts out various networks used, introduces the basic principles and steps of various algorithms, and introduces the existing low light image enhancement datasets and evaluation methods. Finally, a summary of the content was made, pointing out the difficulties in current research and providing prospects for future research directions. © 2024 SPIE.

关键词： image enhancement Deep learning image processing Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient On-Orbit Remote Sensing imagery processing.via Satellite Edge Computing Resource Scheduling Optimization

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Jiang, Qiangqiang Zheng, Lujie Zhou, Yu Liu, Hao Kong, Qinglei Zhang, Yamin Chen, Bo Harbin Inst Technol Sch Aerosp Shenzhen 518055 Peoples R China Xian Inst Surveying & Mapping Xian 710054 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Wuhan Digital Engn Inst Wuhan 430074 Peoples R China

With the enormous scale of remote sensing imagery generation, on-orbit computing has become a crucial paradigm to enable near-real-time processing. Due to the limited onboard resources and on-orbit power supply, satellite edge computing (SEC) is developed for satellite-ground collaboration, aiding on-orbit computation. However, the intermittent satellite-to-ground transmission link poses an efficiency challenge when collaborating SEC resources. Therefore, this article proposes a satellite edge computing resource scheduling technique for on-orbit remote sensing imagery processing.( SECORS ). First, we design a remote sensing mission-specific SEC architecture, which involves an offline-online satellite working mode. Subsequently, a computational resource scheduling model ( SEC - RSM ) is established, including the directed acyclic graph (DAG) model and mathematical problem formulation. Next, to obtain effective scheduling solutions, we develop an end-to-end algorithm leveraging the multiagent proximal policy optimization and heuristic rule of the earliest finish time ( SEC - MPH ). Finally, we build a simulation SEC platform to carry out experiments and implement several methods as the comparison including multiobjective evolutionary algorithms, deep reinforcement learning approaches, and the scheme without optimization (baseline). Simulation results show that SECORS achieves 68.87% and 66.60% reductions in time and energy for on-orbit computation. Moreover, our method improves the energy efficiency ratio (EER) by three times and achieves high processing.capacity with 548 pixels per unit of power (W) and time (ms).

关键词： Processor scheduling Satellites Scheduling Remote sensing Optimization Edge computing Computational modeling Job shop scheduling Collaboration Sensors Multiagent deep reinforcement learning (MADRL) on-orbit computation remote sensing imagery processing resource scheduling model satellite edge computing (SEC)

来源：评论

学校读者我要写书评

暂无评论

On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition

引用

PHYSICAL CHEMISTRY CHEMICAL PHYSICS 2024年第15期26卷 11263-11270页

作者： Sokolov, I. Tufts Univ Dept Mech Engn Medford MA 02155 USA Tufts Univ Dept Biomed Engn Medford MA 02155 USA Tufts Univ Dept Phys Medford MA 02155 USA

Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physicochemical properties of sample surfaces, a process known as multidimensional imaging. While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task. However, the relatively slow speed of AFM imaging poses a challenge in applying deep learning methods broadly used in image recognition. This prospective is focused on ML recognition/classification when using a relatively small number of AFM images, aka small database. We discuss ML methods other than popular deep-learning neural networks. The described approach has already been successfully used to analyze and classify the surfaces of biological cells. It can be applied to recognize medical images, specific material processing. in forensic studies, even to identify the authenticity of arts. A general template for ML analysis specific to AFM is suggested, with a specific example of the identification of cell phenotype. Special attention is given to the analysis of the statistical significance of the obtained results, an important feature that is often overlooked in papers dealing with machine learning. A simple method for finding statistical significance is also described. Steps of combining atomic force microscopy (AFM or SPM) with machine learning.

关键词： Physicochemical properties

来源：评论

学校读者我要写书评

暂无评论

Semiblind Compressed Sensing: A Bidirectional-Driven Method for Spatiotemporal Fusion of Remote Sensing images

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 19048-19066页

作者： Liu, Peng Wang, Lizhe Chen, Jia Cui, Yongchuan Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100094 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

Spatiotemporal remote sensing imaging is one of the most important ways to continuously monitor the Earth. Due to some technical limitations, it is still not easy to obtain images with high-temporal-high-spatial resolution. In this article, we propose a new spatiotemporal remote sensing image fusion method with semiblind deep compressed sensing (SDCS). The reconstruction by SDCS includes two stages: compressed sensing observation and deep post processing. In the stage of CS observation, we design a sensing matrix to connect two spatiotemporal sequences. It can make sure that both the RIP condition of CS and the correspondence of spatiotemporal features are satisfied at the same time, and then CS observation provides a good initial estimates. In the stage of deep postprocessing. it is data-driven, and we designed a deep CNN architecture with multivariate activation function. The second stage not only smoothes out the noise but also reduces the errors from unprecise sampling matrix and compensates for the image differences caused by different imaging conditions. The proposed method is tested on two Landsat and MODIS datasets. Some of state-of-the-art algorithms are comprehensively compared with the proposed SDCS. The experiment results and ablation analysis confirm the better performances of the proposed method when compared with others.

关键词： Remote sensing Spatiotemporal phenomena Spatial resolution Compressed sensing image fusion Sensors Deep learning Imaging Degradation image reconstruction data-driven image fusion model-driven

来源：评论

学校读者我要写书评

暂无评论

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and processing

引用

ADVANCED SCIENCE 2024年第43期11卷 e2405902页

作者： Hou, Kunqi Chen, Shuai John, Rohit Abraham He, Qiang Zhou, Zhongliang Mathews, Nripan Lew, Wen Siang Leong, Wei Lin Nanyang Technol Univ Sch Phys & Math Sci 21 Nanyang Link Singapore 637371 Singapore Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Swiss Fed Inst Technol Dept Chem & Appl Biosci Lab Inorgan Chem CH-8093 Zurich Switzerland Nanyang Technol Univ Sch Mat Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing.challenges;however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing.from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing. An 'in-electrolyte computing' platform is demonstrated using spatially modulated ionic doping in all-solid-state organic electrochemical transistors. Over 400-fold modulation is achieved using this approach, enabling precise differentiation of 12 tactile signals with a single polymeric channel. This approach significantly simplifies circuit design and reduces the use of electrical components. image

关键词： in-electrolyte computing ion modulation organic electrochemical transistor self-multiplexer platform solid-state tactile sensors

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：