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5th International conference on Contemporary Computing and Informatics, IC3I 2022

作者： Jain, Amit Kumar Ross, Dhyana Sharon Babu, M. Kishore Dharamvir Uike, Dipesh Gangodkar, Durgaprasad Delhi University Department of Computer Science Delhi India Tamil Nadu Chennai India Koneru Lakshmaih Education Foundation KL Business School Andhra Pradesh Guntur India The Oxford College of Engineering Department of MCA Bangalore India Dr. Ambedkar Institute of Management Studies and Research Maharashtra Nagpur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Combining cloud computing and Internet of Things concept is the emerging trend for online management efficiently and the sensor data processing. Pervasive healthcare sector or using the Internet of Things paradigm and the study offers the platform the disc based on cloud computing for the administration of information healthcare department. The development of clear and organized healthcare monitoring systems is made possible by the Cloud IoT convergence of the Cloud and IoT. Different types of healthcare applications use various IoT based and cloud-based application for transmission of data and to connect with each other that enables the provision of clinical healthcare solutions effectively. The research study proposes a novel hybrid system that uses IoT smart appliance using metaheuristic swarm intelligence technique for storing data into the cloud then to balance the load with real-time providers at low-cost that receives data from the sensor devices of hospital and stored in cloud. The computer resources are revolutionizing algorithms for individualized inference and actions in health management and the data is protected using blockchain technology. © 2022 IEEE.

关键词： Internet of things

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Overview of Underwater Object Detection Based on Image processing Techniques

Overview of Underwater Object Detection Based on Image Proce...

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Pattern Recognition and Artificial Intelligence (PRAI), International conference on

作者： Muhammad Aamir Jinxiong Gao Yonghui Zhang Hao Tang Uzair Aslam Bhatti College of Computer Science Huanggang Normal University Huanggang China School of Information and Communication Engineering Hainan University Haikou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

underwater object detection and recognition are critical technologies in marine science and engineering, enabling advancements in environmental monitoring, resource exploration, and autonomous underwater vehicles. The rapid progression of computing power, underwater robotics, and artificial intelligence has driven substantial academic and industrial interest in this domain. This paper presents a comprehensive overview of the current methodologies in image-based underwater object recognition, where optical images or videos captured under various lighting conditions are processed to detect and identify underwater objects. The discussion includes an analysis of the prevailing algorithms, a review of the contemporary research landscape, and an exploration of emerging trends. Additionally, this paper offers insights into the anticipated technological advancements that will shape the future of underwater object recognition.

关键词： Image recognition Accuracy Shape Object detection Solids Real-time systems Pattern recognition Object recognition Artificial intelligence Videos

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A Bioinspired Algorithm for Improving the Effectiveness of Knowledge processing

A Bioinspired Algorithm for Improving the Effectiveness of K...

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International conference on Intelligent and Fuzzy systems, INFUS 2020

作者： Kursitys, Ilona Kravchenko, Yury Kuliev, Elmar Natskevich, Alexandr Southern Federal University Rostov-on-Don Russia

ISBN: (纸本)9783030511555

The paper deals with an approach to improve the effectiveness of knowledge processing in terms of large dimensions. The authors suggest the model of classification of the information resources to be used as a preprocessing stage for their further integration. The amount of information produced, transferred and processed by people and various technical devices rapidly grow every year, so the problem of improving the knowledge processing efficiency is very important these days. Ontological structures as used in this work to represent knowledge of the information systems, because they allow us to consider the semantics of the processed knowledge. The authors propose performing the classification using two components of semantic similarity between the objects of the ontologies. They are equivalent and horizontal semantic similarity components. To solve the classification task, we use bioinspired algorithms since they are proven to be effective in terms of solving the optimization problems of large dimensions. One of the suggested bioinspired algorithms developed for solving the mentioned tasks is the bacteria optimization algorithm. The paper describes the algorithm and provides the results of its work. The experiments show that the bacteria algorithm gives effective results with polynomial time complexity. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Bacteria

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Design of Factory Environmental Monitoring System based on ESP32

Design of Factory Environmental Monitoring System based on E...

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Frontiers Technology of information and Computer (ICFTIC), IEEE International conference on

作者： Wenze Song Xuezhen Wang Tianqi Wang Zhixuan Li Zening Zhang Lishan Yang School of Physics Science and Information Engineering Liaocheng University Liaocheng China

With the rapid population of embedded devices, Industrial Internet of Things (IIoT) has provided a promising opportunity to enhance intelligent industrial systems by leveraging the growing ubiquity of wireless sensor and optical devices, which is crucial to ensure the reliable operation of the traditional industrial system and benefit the manufacturing process. In this paper, an efficient factory environmental monitoring system is designed for production data acquisition, which is based on ESP32-S3 and network database, enabling autonomous data collection, analysis and visualization via graph signal processing method, which reduces transmission volume and conserves network bandwidth. The experimental results show that the designed monitoring system with low cost, convenient operation and strong safety, which provides a practical solution to collect and analyze the data obtained by the industrial wireless sector.

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Lightweight Small Target Detection Algorithm Based on YOLOv5 2

Lightweight Small Target Detection Algorithm Based on YOLOv5

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2nd International conference on Applied Statistics, Computational Mathematics, and Software Engineering, ASCMSE 2023

作者： Song, Nan Wang, Ying Xin, Zhiyi Xin, Huili Liu, Tianxu Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475004 China Software Engineering Intelligent Information Processing Innovation Base-Subject Innovation Base of Henan Higher Universities Henan University Kaifeng475004 China Henan Provincial Communications and Transportation Dispatch and Command Center Zhengzhou450000 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTGYOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further lightweighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhenced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy. © 2023 SPIE.

关键词： Signal detection

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The list algorithms accuracy characteristics improvement based on Krohn's algorithm and its modifications

The list algorithms accuracy characteristics improvement bas...

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Intelligent information Technology and Mathematical Modeling 2021, IITMM 2021

作者： Kobak, V.G. Zolotykh, O.A. Zolotykh, I.A. Poliev, A.V. Don State Technical University Department of Computer and Computer-based Systems Software 1 Gagarina Square Rostov-on-Don344000 Russia Don State Technical University Department of Advanced Mathematics 1 Gagarina Square Rostov-on-Don344000 Russia International Management RSUE Department of Information Systems and Applied Informatics Department 69 B. Sadovaya Street Rostov-on-Don34400 Russia

The research of algorithms for uniform loading of devices for homogeneous information processing systems is a very important science-intensive task. An experimental approach was chosen for the research. This is primarily due to the fact that the analytical solution of the distribution problem gives solutions that are far from reality, since it is unable to take into account many factors that affect the computing machine during its operation. The aim of this research is to improve the accuracy characteristics of the list algorithms through the use of heuristic algorithms, such as Krohn's algorithm and its modifications. This made it possible to obtain a more even distribution of tasks among executive devices, which can be networked workstations, processors or processor cores. The work uses list algorithms, such as the Critical Path algorithm and Pashkeev's algorithm, as well as heuristic algorithms - Krohn's algorithm and its modifications. The main idea of the research is to obtain the best suboptimal solution by improving the quality of the resulting distribution. In this case, with the help of the list algorithms, the initial distribution is formed, and its refinement is carried out through the application of the Krohn's algorithm and its modifications. In fact, in the work, a number of symbiotic algorithms are examined and analyzed. For this, many computational experiments were carried out and a large amount of output data were collected, on the basis of which conclusions were drawn about the effectiveness of the solution obtained for each symbiotic group and for all groups as a whole. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Heuristic algorithms

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Design-Space Exploration of Systolic Array for Edge Inferencing Applications 23

Design-Space Exploration of Systolic Array for Edge Inferenc...

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Proceedings of the Third International conference on AI-ML systems

作者： Prashanth H C Yash Dharmesh Mogal Madhav Rao International Institute of Information Technology Bangalore IN International Institute of information Technology Bangalore IN

ISBN: (纸本)9798400716492

Systolic array (SA) architectures are suitable hardware accelerators to run artificial intelligence (AI) algorithms due to their scalable and pipelined structure. Inferencing on edge devices enables decision-making capability in a small, power-efficient form factor suitable for many IoT-based applications. Convolutional neural networks (CNNs), which form a major part of AI algorithms, are generally memory and compute-intensive algorithms. Optimizing resource-constrained SA-based edge-computing system architecture is crucial. The primary challenge is to find an optimal SA configuration with sufficient on-chip and off-chip memory, processing elements, and optimal memory-bandwidth interface for achieving minimum latency and stall cycles while maximizing compute utilization and mapping efficiency. This work characterizes the popular ResNet50 CNN model on different SA architecture configurations that are structured using a realistic designer tool SCALE-SIM. The design-space exploration of SA was carried out to run the Resnet50 model by configuring i) array sizes (processing elements matrix), ii) SRAM memory sizes available to the computing elements, iii) varying interface bandwidth of memory to the computing elements. The compute latency in clock cycles, SA utilization, Mapping efficiency, number of Stall cycles, and SRAM and DRAM bandwidth requirements were extracted using cycle accurate runs. All three types of data-flow models, including output stationary (OS), weight stationary (WS), and input stationary (IS), were investigated in this work. Unlike previous research that relied on benchmarking various mathematical models, this study focuses on characterizing SA configurations using the ResNet50 model. It presents comprehensive results for all the investigated configurations and establishes the most efficient SA architecture for running CNN architectures. Moreover, it provides valuable insights for designing resource-constrained edge-inferencing systems.

关键词： AI Accelerators CNN Design Space Exploration Systolic Array

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Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models

Edge-Cloud Collaborative Motion Planning for Autonomous Driv...

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International conference on Communication Technology (ICCT)

作者： Jiao Chen Suyan Dai Fangfang Chen Zuohong Lv Jianhua Tang Laiquan Han School of Intelligent Engineering South China University of Technology China School of Information Engineering Guangzhou College of Commerce China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Integrating large language models (LLMs) into autonomous driving enhances personalization and adaptability in open-world scenarios. However, traditional edge computing paradigm still faces significant challenges in processing complex driving data, particularly regarding real-time performance and system efficiency. To address these challenges, this study introduces EC-Drive, a novel edge-cloud collaborative autonomous driving system with data drift detection capabilities. EC-Drive utilizes drift detection algorithms to selectively upload critical data, including new obstacles and traffic pattern changes, to the cloud for processing by GPT-4, while routine data is efficiently managed by smaller models on edge devices. This approach not only reduces inference latency but also improves system efficiency by optimizing communication resource usage. Experimental validation confirms the system’s robust processing capabilities and practical applicability in real-world driving conditions, demonstrating the effectiveness of this edge-cloud collaboration framework.

关键词： Large language models Image edge detection System performance Collaboration Traffic control Predictive models Real-time systems Planning Autonomous vehicles Edge computing

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Adaptive exposure control for image-based visual-servo systems using local gradient information

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JOURNAL OF THE optical SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION 2020年第1期37卷 56-62页

作者： Ding, Zhushun Chen, Xin Jiang, Zhe Tan, Cheng Natl Univ Def Technol Dept Aerospace & Engn Changsha 410073 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China

We present anew method for automatic adjustment of camera exposure time for visual-servo systems. The proposed method can improve the robustness of image processing in a high-dynamic-range environment. In this paper, we evaluate an appropriate exposure time by computing the local gradient information of a target area, allowing a camera to capture images without losing target features under artificially adjusted illumination conditions. To validate the advantage of the proposed method, an evaluation is made using an off-the-shelf visual-servo system with a machine vision camera. The experimental results demonstrate the effectiveness of our method, which improves the performance of the target recognition algorithm. (C) 2019 optical Society of America

关键词： Image metrics Image processing Image processing algorithms Image quality Liquid crystals Machine vision

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Design and application of a high-protection micro outdoor cabinet for edge servers based on diagnostic system algorithms 2

Design and application of a high-protection micro outdoor ca...

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2nd International conference on Frontiers of Applied Optics and Computer Engineering, AOCE 2025

作者： Gao, Mingliang Ding, Zhifu Fu, Changzhao Gao, Shan Wang, Yongguang Wu, Jianguo Sun, Yi Edge Computing Business Unit Inspur Electronic Information Industry Co. Ltd. Shandong Jinan250013 China CRRC Changchun Railway Vehicles Co. Ltd. China

ISBN: (纸本)9781510689404

The expansion of edge computing has led to a shift in server deployment strategies, with an increasing preference for server hardware in outdoor environments. This shift is primarily aimed at reducing the latency of data processing and improving the utilization of network bandwidth. Despite the obvious potential benefits, outdoor environments present numerous challenges that can detract from the stability and reliability of server operations. Factors such as temperature changes, humidity levels, and exposure to natural elements can greatly affect server performance. In light of these challenges, this article explores the evolution of complex diagnostic systems for edge servers. On the basis of high-protection micro-outdoor cabinets, the research focuses on the design of complex real-time server monitoring and fault prediction algorithms. These algorithms are essential for early identification of potential problems, enabling preventative maintenance actions and reducing downtime. The overall goal of this study is to enhance the operational consistency of outdoor servers and ensure their efficient functionality under harsh conditions. In addition, by reducing the need for frequent and expensive maintenance, the proposed system aims to reduce the overall operating costs associated with managing servers. © The Authors.

关键词： Edge computing

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