检索结果-内蒙古大学图书馆

Intelligent Computing and Control for engineering and Business systems (ICCEBS)

作者： Bhumika Roy S. Deepa Nivethika G Manju M. Senthil Pandian School of Computer Science and Engineering Vellore Institute of Technology University Chennai India School of Civil Engineering Vellore Institute of Technology University Chennai India

Aim: This paper presents a three-phase approach to smart home automation using Cisco Packet Tracer. Phase 1 establishes the foundational network and integrates IoT devices, ensuring successful packet transfer. In Phase 2, the focus shifts to fire detection and prevention, utilizing smoke detectors and automated water sprinklers. Phase 3 enhances network security through firewall implementation, restricting access to specific IP addresses and blocking ICMP and PING messages. These phases collectively create a robust smart home system, combining device integration, safety measures, and network security, offering enhanced convenience and safety for residents. This work aims to use Cisco Packet Tracer and implement it to design a Smart Home for fire detection and prevention, where home appliances are connected to the router, which in turn is connected to the internet and set up a firewall that adds up network security to the system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Physical Layer Security in Untrusted Diamond Relay Networks with Imperfect Source-Relay Links

Physical Layer Security in Untrusted Diamond Relay Networks ...

引用

2022 International Symposium on Networks, computers and Communications, ISNCC 2022

作者： Qian, Shen He, Xin Zhou, Xiaobo Faculty of Engineering Kanagawa University Department of Information Systems Creation Japan Anhui Normal University School of Computer and Information China Tianjin University School of Computer Science and Technology China

ISBN: (数字)9781665485449

ISBN: (纸本)9781665485449

A two-untrusted-relay transmission scheme is proposed with lossy-forward (LF) relaying being utilized. Two untrusted relays are located between one source and one common destination and there is no direct link between the source and the destination, which referred to as diamond relaying network. Since LF relaying allows intra-link errors and always forwards the decoded information sequences from the relays to the destination, it realizes the reliable and secure transmission of decoder-and-forward based relay networks with untrusted relays. Reliable-and-secure probability is derived to evaluate the performance of the untrusted relay networks, which represents the probability that the destination can recover the original message sent from the source whereas the relays cannot. We find that as long as the contributions of the transmit power of source and relays are balanced, a certain degree of reliable-and-secure probability can be held, even without the support from a friendly jamming signal. © 2022 IEEE.

关键词： Probability

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Object Detection Based on Improved YOLOv3 4

Remote Sensing Object Detection Based on Improved YOLOv3

引用

4th IEEE International Conference on Civil Aviation Safety and information technology, ICCASIT 2022

作者： Dong, Wenlong Nie, Shiyang Wang, Yibo Dalian Maritime University School of Information Science and Technology Dalian116000 China Xiamen Institute of Technology School of Computer Science and Information Engineering Xiamen361021 China Ningxia University School of Information Engineering Yinchuan750000 China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Considering typical remote sensing object detection problems, this paper proposes an improved remote sensing object detection model based on the YOLOv3 algorithm. This model introduces the SE attention module to get richer features and uses K-means to extract the more suitable anchor for remote sensing images. The experiments are carried out under the DOTA data set, and the results show that, under the premise of unchanged detection speed, the accuracy of detection is improved, and the improved algorithm has a better effect on detecting the small and dense objects. © 2022 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Computing Resource Allocation for Hybrid Applications of Blockchain and Mobile Edge Computing 19th

Computing Resource Allocation for Hybrid Applications of B...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Fan, Yuqi Zhang, Jun Ding, Xu Jin, Zhifeng Shi, Lei School of Computer Science and Information Engineering Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Anhui Hefei230601 China Institute of Industry and Equipment Technology Hefei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783031545207

In mobile edge computing (MEC), each user chooses and then offloads the task to an edge server, whereas data security is a concern in MEC due to the lack of trust between users and edge servers. Blockchain is introduced to provide a reliable environment for MEC. In blockchain-based MEC, edge servers are used as the nodes in both MEC and blockchain. After processing the users’ tasks, the edge servers upload the results and other task-related information to the blockchain. The edge servers simultaneously execute two kind of tasks, i.e., the tasks offloaded by the users and the blockchain tasks. Therefore, the user offloading decision affects the processing latency of MEC tasks, and there is a trade-off between the resource allocation for MEC and blockchain tasks. However, most existing studies optimize the resource allocation for blockchain and MEC individually, which leads to the suboptimal performance of blockchain-based MEC. In this paper, we study the problem of user offloading decision and the computing resource allocation of edge servers for MEC and blockchain tasks, with the objective to minimize the total processing delay of MEC and blockchain tasks. We propose an algorithm for joint computing resource allocation for MEC and blockchain (JMB). Theoretical analysis proves that JMB is a 3.16-approximation algorithm. Simulation results show that JMB can effectively reduce the delay in blockchain-based MEC. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

引用

computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Waste Management: Harnessing the Power of IoT 7

Revolutionizing Waste Management: Harnessing the Power of Io...

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Karale, Ankita Rajawat, Anand Singh Goyal, S.B. Computer Engineering Sandip Foundation’s Sandip Institute of Research Center Nashik School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia

ISBN: (纸本)9781839539831

Waste management has become a pressing problem in a world that is becoming more and more urbanised. Traditional ways of dealing with trash are usually inefficient and bad, leading to pollution, health problems, and wasted resources. As an answer to these problems, the Internet of Things (IoT) has changed the way trash is handled in a big way. This abstract looks at how smart waste management based on the Internet of Things could totally change the business of garbage disposal. Through a network of sensors and devices, the Internet of Things (IoT) connects trash cans, garbage trucks, and waste processing facilities so that they can be monitored and improved in real time. A smart waste management system that is built on IoT has many benefits. With the real-time information it gives about how much trash is being made, routes for collecting trash can be made more efficient in terms of time and fuel use. This cuts down on the number of times trash needs to be picked up, reduces the amount of greenhouse gases released, and saves money. Sensors in trash cans can also find dangerous or flammable items, so they can be dealt with right away and any problems can be avoided. Also, the Internet of Things makes it possible for predictive analytics, which help waste management officials predict how much trash will be made and use their resources well. Using smart waste sorting methods like optical sensors and machine learning algorithms, recyclable and non-recyclable trash can be correctly found and sorted. This helps people recycle more and puts less pressure on landfills. Smart trash management systems that are based on the Internet of Things (IoT) get more people involved and help them understand. Mobile apps and smart devices can tell a person when their trash will be picked up, where the nearest recycling sites are, and how much trash they make. People are encouraged to act responsibly and throw away trash in ways that are good for the environment. Smart waste management base

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Exploring Reinforcement Learning in Large-Scale Data Processing 3

Exploring Reinforcement Learning in Large-Scale Data Process...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bhambu, Pawan Kumar, Ritesh Sharmila, P. Patil, Vivek D. Khurana, Shivam Vivek, V. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

Reinforcement mastering (RL) is vital for big-scale fast processing. RL utilizes the availability of large datasets and dealers skilled in this information to remedy a wide variety of obligations, from automated diagnostics and self-reliant using structures to clinical information analysis. Using exploiting the traits of the modern-day significant information era, RL allows efficient and correct decision-making in massive-scale information processing and answers complicated troubles. This paper explores the use of reinforcement learning (RL) in large-scale data processing. RL is a subfield of machine learning that focuses on decision making in dynamic environments. With the increasing demand for processing large amounts of data, traditional techniques such as rule-based systems and batch processing are becoming inadequate. This has led to the emergence of RL as a potential solution due to its ability to learn and adapt to changing environments. Through a comprehensive review of existing literature, this paper discusses the applications of RL in large-scale data processing and its potential benefits. Furthermore, it examines various challenges and their potential solutions in implementing RL for data processing tasks. The paper concludes by highlighting the future research directions in this domain and the potential impact of RL on improving data processing efficiency and accuracy. It similarly examines some of the demanding situations and possibilities related to using RL for huge-scale records processing and the capability programs of RL in the future. © 2023 IEEE.

关键词： applications challenges opportunities potential processing

来源：评论

学校读者我要写书评

暂无评论

Intelligent Street Light Management System using Solar Panels

Intelligent Street Light Management System using Solar Panel...

引用

Delhi Section Conference (DELCON), IEEE

作者： Akila Venkatraman Arutla Ruchitha Suseela Sellamuthu Dappu Shalini Pulsani Dinesh Reddy Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science & Engineering Vellore Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The conventional street lighting system is used during night time. It consumes more amount of power and there is major energy demand arises. The energy saving methods and lighting control arises to overcome the problem. The smart street light project aims to improve the energy efficiency and urban safety by using sensor- equipped street lights. It can adjust their brightness automatically based on levels of ambient light and the presence of vehicles and pedestrians. Additionally, the project incorporates the central management system implemented by using the ESP 32 module and BLYNK cloud, that allows for remote monitoring and control of street lights. The implementation of this technology can lead to significant cost saving, improves security for the cities and rise the overall quality of life for residents. Vehicle detection system is implemented to control the street lights when vehicle is detected. LED's (Light Emitting Diode) doesn't consume large amount of electricity. By the combination of both LED's and LDR's (Light Dependent Resistor) the intensity of light can be controlled and save more electricity. To make a step towards green energy, solar panels are used to power the street lights. The increased standard of living and growth of cities lead to the complex lighting system with automatic and intelligent control schemes.

关键词： Resistors Pedestrians Electricity Urban areas Lighting Safety Solar panels Security Standards Remote monitoring

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

DynMap: A Heuristic Dynamic Mapper for CGRA Multi-Task Dynamic Resource Allocation

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Yang, Yufei Xie, Chenhao Liu, Liansheng Peng, Xiyuan Harbin Institute of Technology School of Electronics and Information Engineering Heilongjiang Harbin China Beihang University School of Computer Science and Engineering Beijing China

Coarse-grained reconfigurable architecture (CGRA) has received increasing attention in both industry and academia due to its comprehensive advantages of performance, energy efficiency, and flexibility. To improve the resource utilization and handle the mixing workloads in the real-world, multiple tasks sharing the whole CGRA has became an important technical trend, and the varying resource requirements throughout their life cycles also makes run-time dynamic resource allocation necessary for higher multi-task throughput. As the key stage of dynamic resource allocation, dynamic mapping is responsible for mapping kernels within each task to the dynamically allocated CGRA resources. However, existing dynamic mapping methods have difficulty to balance the mapping time and the mapping quality, resulting in a significant gap between the actual and the optimal task throughput. To address the challenge, we propose DynMap, a heuristic dynamic mapper for CGRA multi-task dynamic resource allocation. With the support of specialized scheduling and routing schemes, DynMap heuristically references the placement tendency in the static mapping result to dramatically save the mapping time, while maintaining the high mapping quality by minimizing the possibility of resource conflicts. Experimental evaluation demonstrates DynMap not only achieves the average 1.17ms mapping time and average 98.33% of the optimal mapping quality on different CGRA architectures, but also reaches average 98.85% of the optimal task throughput expected by different CGRA multi-task dynamic resource allocation scenarios, reducing the gap between actual and optimal task throughput average 31.75× smaller than that of the current methods. © 2025 IEEE.

关键词： Mapping

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：