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检索条件"任意字段=IEEE International Conference on Intelligent Computing and Information Systems"

共 116548 条记录，以下是31-40 订阅

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A User-Friendly Ecosystem for AI FPGA-based Accelerators

A User-Friendly Ecosystem for AI FPGA-based Accelerators

引用

ieee international conference on Omni-Layer intelligent systems (ieee COINS)

作者： Leon-Vega, Luis G. Obregon-Fonseca, Erick Castro-Godinez, Jorge Univ Trieste MIGe Dept Trieste Italy Costa Rica Inst Technol Elect Engn Sch Cartago Costa Rica

ISBN: (纸本)9798350349603;9798350349597

The introduction of FPGAs in High-Performance Embedded computing and Artificial Intelligence still faces challenges regarding the difficulty of getting started. It requires hardware knowledge, familiarity with multiple tooling, libraries and frameworks and long synthesis times. To encourage the usage of FPGAs, this work proposes an ecosystem that includes a library with a set of pre-built accelerators for common Digital Signal Processing and Artificial Intelligence workloads, an engine for runtime arbitrary-precision quantisation and an agnostic API, allowing the development of FPGA-accelerated user applications while abstracting the details about the FPGA design and implementation. Our approach is based on hardware reuse, introducing software resource management of a series of pre-built IP cores, allowing low-end FPGAs to be used as hardware accelerators and multiple applications to share resources. Our work is better than managed FPGA standalone applications with Vitis HLS-based quantisation, accelerating 1.22x, thanks to our quantisation engine, which accelerates 5.12x the quantisation and 13.30x the de-quantisation, while keeping close the accelerator execution times.

关键词： Field Programmable Gate Arrays Hardware Acceleration Edge computing Cloud computing High Performance computing

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Distributed Misbehavior Detection System for Cooperative Driving Networks 8

Distributed Misbehavior Detection System for Cooperative Dri...

引用

8th ieee international conference on Fog and Edge computing (ICFEC)

作者： Abdullahi, Abdullahi Modibbo Lalouani, Wassila Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)9798350361360;9798350361353

Cooperative intelligent Transportation systems (C-ITS) are foundational evolution of vehicular networks, emphasizing the seamless coordination of Vehicle-to-Vehicle communication for exchanging vital information. In C-ITS, vehicles collaborate by distributing V2V messages throughout the network. However, the potential transmission of deceptive or inaccurate information by a malicious vehicle poses a significant risk to road safety. Detecting such malicious data promptly is crucial for taking appropriate actions. Current Misbehavior Detection systems (MBS) operate with the assumption of a centralized MBS existence, but the challenge lies in empowering vehicles to make local MBS decisions as early as possible. This approach avoids the exchange of all messages with the infrastructure, reducing excessive communication overhead. In response to this challenge, we introduce an innovative distributed misbehavior detection system designed to protect vehicular networks from potential attacks. Our proposed system harnesses the scalability benefits of Federated Learning, allowing vehicles to collaboratively learn a shared model for detecting faulty and malicious messages. Recognizing the impracticality of assuming that all vehicles possess fully labeled data in real-world settings, we propose Fisher-Discriminant Analysis and model regularization to generate an effective pseudo-labeling mechanism. The model regularization proves effective when multiple models from different vehicles produce less-confidence predictions for a given sequence of Basic Safety Messages. Our approach demonstrates efficacy in identifying a broad spectrum of faults and attacks, including rare types, by learning the inherent data distribution and essential network traffic characteristics.

关键词： Secure communication intelligent transportation systems misbehavior detection system

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Design and Implementation of intelligent Pet Feeding System 22

Design and Implementation of Intelligent Pet Feeding System

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Li, Qi Shen, Xinqi Cheng, Zhongkai Liu, Yu Jinling Inst Technol Sch Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Keeping pets can help people regulate their emotions, engage in physical activity, and cultivate friendships, all of which contribute to enhancing their overall quality of life. Based on a survey, pets are often left alone at home for an average of 8 hours, as their owners typically depart early and return late. Nonetheless, the current pet feeding systems available on the market exhibit issues like a restricted field of view, unreliable network connections, sluggish pet recognition speed, and subpar accuracy. In this paper, we introduce a more intelligent and efficient pet feeding system. This system leverages the fast and compact ResNet18 model and utilizes Jetson Nano and STM32F407ZGT6 chips to achieve pet image acquisition and species recognition functionalities. We employ the MQTT protocol to enable the uploading of environmental data and have designed a user-side webpage for convenient remote monitoring and timely checking of the pet's status. Furthermore, we implement an end-to-end interaction design, allowing users to remotely and flexibly adjust factors such as the amount of pet feeding, environmental temperature, humidity, and other related information. Lastly, we conducted real-world deployment and testing of the system in various households, achieving a remarkable pet recognition accuracy of 98.65%. This system effectively fulfills the requirements for a scientific, automated, and efficient approach to pet care in daily life.

关键词： intelligent feeding STM32F407ZGT6 Jetson Nano deep learning automated management

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Cloud-Based Perspective for intelligent Transportation and Crime Prevention: A Web Deployment Solution

Cloud-Based Perspective for Intelligent Transportation and C...

引用

12th ieee international conference on Computer, information and Telecommunication systems (CITS)

作者： Nedeljkovic, Denis Jammal, Manar York Univ Sch IT Toronto ON Canada

ISBN: (纸本)9798350359107;9798350359091

In the cutting edge domain that is the ever so expanding smart city, the need for development tools that can keep up with such an urban environment is ever so prevalent. Moreover, the rapid expansion of such metropolitan areas requires even more specialists than are currently available. This study presents a pioneering cloud-based solution for intelligent transportation and crime prevention, emphasizing the seamless integration of machine learning techniques within a web deployment framework. Utilizing data from sources like Automated Speed Enforcement, police crime statistics, and traffic monitoring programs, our approach employs advanced predictive analytics to accurately identify potential crime hotspots and optimize traffic management. A significant innovation of this research is the development of a scalable Software as a Service model, which allows for the effective predictions of traffic sensors across urban settings. The proposed system features a user-friendly graphical user interface and employs Dynamic Load Balancing to enhance computational efficiency, making it accessible to a wide range of users. By harnessing cloud computing, our solution offers a versatility for government officials, law enforcement, and researchers, promising improvements in road safety, crime prevention, and the overall quality of life.

关键词： Machine Learning Cloud computing Smart Cities Internet of Things Traffic Crime Prediction Software as a Service intelligent Transportation systems Cloud Load Balancing

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Auction Based Dynamic Resource Allocation in Edge computing

Auction Based Dynamic Resource Allocation in Edge Computing

引用

2024 ieee international conference on information Technology, Electronics and intelligent Communication systems, ICITEICS 2024

作者： Sudha, Sushree S.S. Anand, Aman Jain, Rishab Dash, Dipti Swain, Sujata Bandyopadhyay, Anjan School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9798350382693

Edge computing has gained significant attention in recent years due to its ability to provide low-latency services and handle the massive data generated by IoT devices. One of the critical challenges in edge computing is efficient resource allocation to meet the dynamic demands of various applications. Traditional resource allocation methods may not be suitable for edge environments due to their distributed and heterogeneous nature. In this paper, we propose an auction-based dynamic resource allocation mechanism for edge computing. The proposed approach leverages auctions to allocate resources dynamically based on real-time demand and availability. Through simulations and analysis, we demonstrate the effectiveness of the proposed approach in optimizing resource utilization and improving overall system performance in edge computing environments. © 2024 ieee.

关键词： Mobile edge computing

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Edge Device Selection For Industrial IoT Task Offloading In Mobile Edge computing

Edge Device Selection For Industrial IoT Task Offloading In ...

引用

ieee international conference on Omni-Layer intelligent systems (ieee COINS)

作者： Sharma, Megha Tomar, Abhinav Hazra, Abhishek Akhter, Zaid Dhangar, Daksh Singh, Rahul Kumar Aletaji Subhas Univ Technol New Delhi Dept Comp Sci & Engn New Delhi 110078 India Indian Inst Informat Technol Sri City Dept Comp Sci & Engn Sri City 517646 India

ISBN: (纸本)9798350349603;9798350349597

In the realm of Mobile Edge computing (MEC), mobile devices have the option to transfer their tasks to edge servers for processing, thereby significantly diminishing task completion duration and reducing the energy consumption of mobile devices. This offloading mechanism optimizes resource utilisation and enhances overall efficiency by leveraging the computational capabilities of nearby edge servers. This paper addresses the issue of efficient task offloading from mobile devices to edge servers in resource-constrained Industrial Internet of Things (IIoT) heterogenous networks, focusing on minimising energy consumption and heterogenous network delay. The methodology involves checking the matching feasibility using Hall's Marriage Theorem and employing a server selection algorithm for task assignment. The results show that the proposed approach minimizes energy consumption and overall time delay compared to standard algorithms. The implications include the potential for improving various QoS factors in future research.

关键词： Task Offloading Edge computing Energy Efficiency IIoT heterogenous networks Mobile Devices Edge Server Allocation

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systems of intelligent information Processing Using Fuzzy Logic 27

Systems of Intelligent Information Processing Using Fuzzy Lo...

引用

27th international conference on Soft computing and Measurements, SCM 2024

作者： Kozlova, Liudmila P. Kozlova, Olga A. Saint Petersburg Electrotechnical University 'LETI' Faculty of Industrial Automation and Electrical Engineering St. Petersburg Russia The Bonch-Bruevich St. Petersburg State University of Communication Faculty of Information Systems and Technologies St. Petersburg Russia

ISBN: (纸本)9798350363708

One of the main units of work of any system is information. That is why methods of its processing are important for the full functioning of the entire process. The article discusses the scope of intelligent information processing algorithms, the complexity and features of such systems, and proposes a fuzzy logic method for achieving high results. © 2024 ieee.

关键词： Fuzzy logic

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Apt Detection of Ransomware - An Approach to Detect Advanced Persistent Threats Using System Call information 22

Apt Detection of Ransomware - An Approach to Detect Advanced...

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Baksi, Rudra Prasad Nalka, Vishwas Upadhyaya, Shambhu Illinois State Univ Sch Informat Technol Normal IL 61761 USA SUNY Buffalo Univ Buffalo Dept Comp Sci & Engn Buffalo NY USA

ISBN: (纸本)9798350381993;9798350382006

Ransomware of the Advanced Persistent Threat (APT) type are very sophisticated and often have a contingency plan of attack in case they are discovered while the attack is in progress. Due to the ever-changing trait of such APT-type ransomware, an intelligent and robust intrusion detection system (IDS) is the need of the hour and in this paper, we put forward machine learning (ML) and natural language processing (NLP) based intrusion detection systems. We utilize a commercial simulator to run different real-world ransomware attacks to create, for the first time, a dataset for APT-type ransomware research. Then, we develop multiple IDSes by training ML models like support vector machine (SVM), logistic regression (LR), gradient boosting (GB) decision trees, random forest (RF), naive Bayes classifier (NBC), and an NLP model called BERT, on this dataset. With our intelligent IDS, we could precisely distinguish the system calls of processes spawned by ransomware from legitimate system calls. We compare the different intrusion detection systems developed using the six aforementioned models. The IDS using the NLP BERT model achieves the best accuracy of 99.98%, and the IDS using the Naive Bayes Classifier achieves an accuracy of 98.55%. Furthermore, we discuss the tradeoffs of these models for designing an intelligent IDS. The advancement in cyber attacks, especially ransomware-based attacks, necessitates this upgrade in IDS which is essential for a strong defense.

关键词： Advanced Persistent Threats (APT) Artificial Intelligence (AI) Cybersecurity Intrusion Detection System (IDS) Machine Learning (ML) Natural Language Processing (NLP) Ransomware

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Design and Implementation of Mask Detection System Based on Improved YOLOv5s 22

Design and Implementation of Mask Detection System Based on ...

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Zhao, Changyu Yan, Zhuo Xu, Huangxin Chen, Xueliang Li, Zheng Zhong, Xinyu Liu, Cuiwei Xiao, Anyan Lv, Xingyan Shenyang Aerosp Univ Sch Artificial Intelligence Shenyang Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China

ISBN: (纸本)9798350381993;9798350382006

In this paper, we propose a lightweight mask detection algorithm and implement an intelligent vehicle system. The algorithm uses YOLOv5s as the backbone network, and at the same time incorporates the SE attention mechanism to optimize the timeliness, and is finally deployed on an intelligent vehicle system with BCM2711 as the controlplatform. Experiments prove that the algorithm proposed in this paper reduces the detection time by 30% while ensuring a higher MAP, which has certain valuefor promotion.

关键词： intelligent vehicle systems mask detection YOLOv5s SE attention mechanism

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Hybrid intelligent Reflecting Surface and Cell-free Massive MIMO-Aided Over-the-Air Computation for Digital Twin 44

Hybrid Intelligent Reflecting Surface and Cell-free Massive ...

引用

44th ieee international conference on Distributed computing systems (ICDCS)

作者： Di, Jiaying Zhang, Ni Wan, Junjie Li, Sen Wang, Kunlun East China Normal Univ Sch Commun & Elect Engn Shanghai 200241 Peoples R China Guangxi Key Lab Digital Infrastruct Nanning Peoples R China

ISBN: (纸本)9798350354720;9798350354713

This paper investigates the over-the-air computation (AirComp) problem in a hybrid intelligent reflecting surface (IRS) and cell-free massive multiple-input multiple-output (CF-mMIMO) assisted digital twin system, where multiple users offload their data to access points (APs) and central processing unit (CPU) via the IRS for data aggregation. We formulate a joint beamforming design, IRS phase shift optimization, and power allocation problem to minimize the mean squared error (MSE) of data aggregation. We solve the resultant non-convex optimization problem in three steps. First, we transform the original problem into two sub-problems. Then, we exploit a convex optimization framework to respectively determine the beamforming design, IRS phase shift optimization, and power allocation. Last, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

关键词： Over-the-air computation (AirComp) intelligent reflecting surface (IRS) cell-free massive multiple-input multipleoutput (CF-mMIMO) alternating optimization algorithm digital twin

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