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检索条件"任意字段=Conference on Algorithms, Devices, and Systems for Optical Information Processing"

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Identification of Retinal Ischemia for Medical Pronouncement Through Fundus Images and Machine Learning Techniques

Identification of Retinal Ischemia for Medical Pronouncement...

引用

International conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： V. Indu Srinivas Pasupula Gill Santosh Kumar Cheekatla Swapnapriya S NagaMallik Raj T. Ravibabu Dept of CSE(AIML) BVRIT College of Engineering for Women Hyderabad Telengana Department of CSE (AI&ML) Vignana Bharathi Institute of Technology Hyderabad Telengana Dept.of Mechanical Engineering Vignan's institute of information technology (A) Visakhapatnam AP India Dept of CSE Vignan's Institute of Information Technology (A) Visakhapatnam AP India Department of ECE Miracle Educational Society Vizianagaram AP India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Retinal ischemia is caused by diabetes which affects small arteries including retinopathy. It is a condition marked by the deterioration of the retina's *** worse progression of Retinal ischemia may lead to the complete blindness. In this case, early detection of diabetes and retinal ischemia is more crucial to detect the lesions affected and provide the proper guidance and treatment to the patients suffering from diabetes. The identification of RI can be done manually and automatically. In the manual process, analysis and explanation is given by ophthalmologists and opticians. It is a time taking process and expensive. In case of automation process, early detection of RI can be done by automated systems using Artificial intelligence and deep learning over traditional methods. Most of the research work has done based on early detection of RI by taking two major facets fundus images and RI detection techniques.

关键词： Photography Biomedical optical imaging Signal processing algorithms Retina optical imaging Real-time systems Diabetes optical sensors optical signal processing Arteries

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Draft - Distilled Recurrent All-Pairs Field Transforms For optical Flow

Draft - Distilled Recurrent All-Pairs Field Transforms For O...

引用

IEEE International conference on Image processing

作者： Yanick Christian Tchenko Hicham Hadj Abdelkader Hedi Tabia IBISC Univ. Evry Université Paris-Saclay Rue Joliot Curie Gif-sur-Yvette France

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This paper addresses the challenge of utilizing learningbased algorithms for 3D scene reconstruction on resourceconstrained end-user devices. Although integrating deep learning methods into the reconstruction pipeline has demonstrated superior performance to classical techniques, the resulting large models could be impractical for resource-limited devices. We propose an efficient solution by introducing a method to compress deep learning models used in 3D reconstruction workflows. Our approach, named DRAFT, employs knowledge distillation (KD), adapted and extended for complex feature and context extraction tasks related to optical flow. New distillation components based on algebraic signpattern matrices (SPM) and inertia enhance the KD process. Empirical validation on KITTI and Sintel benchmark datasets reveals that DRAFT consistently achieves comparable or superior performance to state-of-the-art models such as RAFT, FlowID, GMFlow, and Anyflow while significantly reducing model size. This contribution enhances the feasibility of deploying learning-based 3D scene reconstruction frameworks on edge systems. It contributes to the discourse on resource-efficient deep learning methodologies, particularly for optical flow and stereo matching. Our code is available at https://***/christian-tchenko/***.

关键词： Deep learning Adaptation models Three-dimensional displays Accuracy Transforms Benchmark testing Feature extraction

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TizenRT OS Based Data Compression algorithms for IoT devices

TizenRT OS Based Data Compression Algorithms for IoT Devices

引用

International conference on Computing, Communication, and Intelligent systems (ICCCIS)

作者： Tanmoy Roy Chamak Sachi Kaushik Shiven Singhal Aditya Dadheech Prof. A. Anitha Prof. S. Jenicka Vidisha Thapa School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Samsung Research Institute Bangalore Karnataka India

The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-time operating system tailored for Internet of Things (IoT) devices, presents unique challenges in balancing compression efficiency and resource constraints. The study evaluates the performance of LZ4 alongside Miniz and LZMA in terms of compression ratios, processing overhead, and memory utilization. Experimental results demonstrate the feasibility of LZ4's adoption in TizenRT, highlighting its potential to enhance data compression and decompression operations while adhering to the stringent requirements of IoT devices. The findings contribute to the optimization of data management strategies within the TizenRT ecosystem.

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Optimization Query processing for Multi-tiered Persistent Storage 4

Optimization Query Processing for Multi-tiered Persistent St...

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4th IEEE International conference on Computer and Communication Engineering Technology, CCET 2021

作者： Noon, Nan Noon Getta, Janusz R. Xia, Tianbing University of Wollongong School of Computing Information Technology Wollongong Australia

ISBN: (纸本)9781665438902

The efficient processing of database applications by computing systems with multi-tiered persistent storage devices needs specialized algorithms to generate optimal persistent storage management plans. A correct allocation and deallocation of multi-tiered persistent storage may significantly impact the overall performance of data processing in such systems. This paper describes the new algorithms, that create persistent storage allocation plans for data processing on multi-tiered persistent storage devices. This work is based on the pipelined data processing model, and it applies a formalism of extended Petri nets to describe the data flows between the tiers of persistent storage devices. The algorithms presented in the paper perform linearization of the extended Petri nets and generate the optimal persistent storage allocation plans. The paper describes the experiments conducted to validate the generate data allocation plans for multi-tiered persistent storage devices and prove the improvements in performance compared with random data allocation plans. © 2021 IEEE.

关键词： Scheduling

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FLASHATTENTION: Fast and Memory-Efficient Exact Attention with IO-Awareness 36

FLASHATTENTION: Fast and Memory-Efficient Exact Attention wi...

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36th conference on Neural information processing systems (NeurIPS)

作者： Dao, Tri Fu, Daniel Y. Ermon, Stefano Rudra, Atri Re, Christopher Stanford Univ Dept Comp Sci Stanford CA 94305 USA SUNY Buffalo Dept Comp Sci & Engn Buffalo NY USA

ISBN: (纸本)9781713871088

Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle is making attention algorithms IO-aware-accounting for reads and writes between levels of GPU memory. We propose FLASHATTENTION, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. We analyze the IO complexity of FLASHATTENTION, showing that it requires fewer HBM accesses than standard attention, and is optimal for a range of SRAM sizes. We also extend FLASHATTENTION to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method. FLASHATTENTION trains Transformers faster than existing baselines: 15% end-to-end wall-clock speedup on BERT-large (seq. length 512) compared to the MLPerf 1.1 training speed record, 3 x speedup on GPT-2 (seq. length 1K), and 2.4 x speedup on long-range arena (seq. length 1K-4K). FLASHATTENTION and block-sparse FLASHATTENTION enable longer context in Transformers, yielding higher quality models (0.7 better perplexity on GPT-2 and 6.4 points of lift on long-document classification) and entirely new capabilities: the first Transformers to achieve better-than-chance performance on the Path-X challenge (seq. length 16K, 61.4% accuracy) and Path-256 (seq. length 64K, 63.1% accuracy).

关键词： Graphics processing unit

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A Real-Time Monitoring Computer Algorithm System for Occlusion of Photovoltaic Panels Developed by Machine Vision Technology

A Real-Time Monitoring Computer Algorithm System for Occlusi...

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Image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Xixing Hu Weiyan Zheng Fang Su Chaoyue Zhu Zhejiang Dayou Industrial Co. Ltd. Hangzhou Science and Technology Development Branch Hangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In this paper, a multi-feature detection method based on graph cut for photovoltaic panels is proposed. Combined with multi-dimensional features such as optical flow field and light intensity, an interactive feature recognition method is constructed. The combination of each feature forms a pixel feature vector and is fed into a random forest classifier. The interaction information between adjacent pixel pairs is extracted. The energy function and adaptive algorithm of object-based occlusion detection are constructed. This Paper build an undirected graph. The object is segmented by the graph cutting principle. The findings from the experiments indicate that the new technique surpasses traditional methods in precision and offers enhanced responsiveness for detecting obstructions.

关键词： Photovoltaic systems Photovoltaic cells Software algorithms Real-time systems Vectors Software Monitoring Surface treatment Random forests optical flow

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Autofocus optical imaging system based on image processing 14

Autofocus optical imaging system based on image processing

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14th International conference on information Optics and Photonics, CIOP 2023

作者： Teng, Teng Chen, Guan Xiaofei, Wang Hua, Fan Dongjie, Ju Zhaojuan, Chi Jinan250000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510671744

Autofocus optical imaging systems are widely used in industrial inspection, medical diagnosis, drone photography, machine vision and other fields. Image-based autofocus algorithms typically consist of two key steps: image sharpness evaluation and search strategy. In this study, we propose an advanced image-based autofocus algorithm specifically designed for industrial image measurement. In order to improve the accuracy of the clarity assessment, we improve the existing method by introducing Gaussian filtering and threshold controlled Laplace operators. This improvement effectively reduces the effects of noise and light intensity changes, improving the reliability of clarity measurements. In addition, we propose a novel distance adjustment strategy combining coarse and fine tuning as part of the search strategy. This strategy reduces the interference of local peaks, allowing the algorithm to accurately identify the best image focus. The proposed image-based autofocus algorithm has several advantages, e.g. high focusing accuracy, high repeatability, and stability under light intensity changes2. These advantages make it ideal for industrial image measurement applications. To verify the effectiveness of our method, we build an experiment setup in a lab using ADLINK PCI-9114-DG board and a custom-designed lens. The results show that the sensitivity and performance of the autofocus system meet certain requirements. In summary, the image-based autofocus algorithm that we developed enables accurate and reliable focusing. It overcomes some challenges such as noise and light intensity changes to ensures optimal image focus, and improves overall image quality. The successful implementation of our autofocus system can benefit industrial inspection, medical diagnostics and many other applications. © 2023 SPIE.

关键词： Diagnosis

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DMDA: A Computational Resource Allocation Approach for IoT devices in Fog Computing

DMDA: A Computational Resource Allocation Approach for IoT D...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International conference on

作者： Luthfan Hadi Pramono Shan-Hsiang Shen Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Fog Computing enables the efficient offloading of computational tasks from IoT devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT devices based on parameters such as data type, computational load, priority, and responsiveness. devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments.

关键词： Performance evaluation Smart cities Heuristic algorithms Graphics processing units Parallel processing Dynamic scheduling Real-time systems Resource management Internet of Things Edge computing

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Cloud-Edge Collaborative Human Activity Recognition System Design and Implementation 15

Cloud-Edge Collaborative Human Activity Recognition System D...

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15th IEEE International conference on Wireless Communications and Signal processing, WCSP 2023

作者： Zhang, Xichen Zhu, Chen Shi, Zhiguo Zhejiang University Polytechnic Institute Hangzhou310015 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

ISBN: (纸本)9798350324662

This paper proposes an edge computing architecture for Human Activity Recognition (HAR) based on Raspberry Pi and HaaS100, aimed at enhancing real-time performance and system robustness. The proposed approach employs Edge-End inference within the communication range of edge nodes, while relying on Cloud Edge inference otherwise, thereby establishing a comprehensive Cloud Edge End system that leverages the capabilities of a mobile communication 5G private network Wi-Fi. To achieve accurate activity recognition, three algorithms, namely Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), and Residual Network-based CNN (ResNet-based CNN), are implemented and their performances are rigorously compared using Actitracker datasets. The experimental results demonstrate that the ResNet-based CNN exhibits superior performance, achieving an impressive F1-score of 0.977. Additionally, to address the challenge of model size and resource-constrained edge devices, model compression is employed through channel-wise data collection, effectively reducing the parameter size from 220,000 to 160,000, with a negligible 0.5% decrease in accuracy. Finally, the compressed model is deployed on edge devices, and retraining is conducted using self-collected datasets to ensure practicality and adaptability in real-world scenarios. The proposed HAR edge computing architecture showcases significant advancements in real-time performance, system robustness, and resource optimization, making it a promising solution for activity recognition in edge computing environments. © 2023 IEEE.

关键词： Actitracker Cloud-Edge Collaborative CNN Depth Separable Convolution Transfer Learning

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Research on Smart Photovoltaic Power Station Control System Based on DCS Architecture

Research on Smart Photovoltaic Power Station Control System ...

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Computer Communication, Networks and information Science (CCNIS), International conference on

作者： Lei Zhan State R&D Center of Control System and Information Security Technologies for Energy Industry Chn Energy Zhishen Control Technology Co. Ltd. Beijing Engineering Research Center of Power Station Automation Beijing China

ISBN: (数字)9798331507046

ISBN: (纸本)9798331507053

Aiming at the operation and maintenance management and control issues of large-scale photovoltaic power stations, a smart photovoltaic power station control system based on DCS architecture was studied. In terms of system architecture design, the design idea based on the “cloud-edge-end” architecture is adopted, and some core equipment and data storage devices in the site are placed in the cloud to achieve centralized management and storage of data. At the same time, some computationally intensive tasks are processed in the cloud to reduce the computing pressure on edge devices. Applying this system to the control of photovoltaic power plants can achieve precise control of operation and maintenance data, and enable the identification and accurate processing of different faults. Comparing the fault identification algorithm used in this system with other algorithms, the results prove that the fault identification accuracy of this algorithm can reach 100℅, creating a large amount of economic benefits for the stable operation of photovoltaic power plants.

关键词： Photovoltaic systems Fault diagnosis Economics Accuracy systems operation systems architecture Computer architecture Control systems Maintenance Monitoring

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