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检索条件"丛书名=Lecture Notes in Networks and Systems"

共 9905 条记录，以下是2421-2430 订阅

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A Computer Vision-Based Eye-Tracking System Toward an Eye-Controlled Powered Wheelchair 7th

A Computer Vision-Based Eye-Tracking System Toward an Eye-Co...

引用

7th International Conference on Green Technology and Sustainable Development

作者： Vi-Do Tran Cong-Trung Nguyen Ho Chi Minh City Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9783031761966;9783031761973

This paper presents the design of an advanced wheelchair system for patients with Amyotrophic Lateral Sclerosis (ALS), which controlled by eye movements in a natural environment. The system comprises an electric wheelchair, a vision system, and a primary control system. The control system captures the user's eye image via a camera, then employs deep learning and an attention mechanism to determine the direction of eye movement. A lightweight eye-movement recognition model (YOLOv5-small) is integrated into an embedded AI controller. The experimental results demonstrate a 93.5% accuracy in recognizing eye-movement direction, and a maximum wheelchair movement speed of 0.5 m/s.

关键词： Eye-tracking deep learning powered wheelchair

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SDN Enabled L2 Switch Implementation and Its Performance Evaluation Through P4 Programming 1

引用

20th International Conference on Computing and Information Technology (IC2IT2)

作者： Pudasaini, Sakar Dawadi, Babu R. Ghimire, Roshani Pau, Giovanni Sapkota, Binod Tribuvan Univ Inst Engn Pulchowk Campus Kathmandu Nepal Tribhuvan Univ Adv Coll Engn & Management Kathmandu Nepal Kore Univ Enna Fac Engn & Architecture Enna Italy

ISBN: (数字)9783031585616

ISBN: (纸本)9783031585609;9783031585616

The concept of SDN (Software-Defined Networking) originally came with the purpose of disaggregating control plane and data plane. However, current scope of SDN has surpassed the original goal. It sets out to introduce programmability in the control plane for better visibility and manageability of networking devices. The idea has been recently extended to program the data plane. In the present scenario P4 (Programming Protocol-Independent Packet Processors) stands as the de-facto language to program the data plane. In this study, we implemented an L2 (Layer 2) switch and evaluated it performance using P4 programming language. The performance of the switch was evaluated in terms of throughput (Mbps) and latency (ms). The throughput was found to be nominal as the switch was implemented in software and the latency for first packet was also high since the switch's table was empty. However, once the table was filled, subsequent packets suffered very low latency.

关键词： SDN Programmable Data Plane P4 Data Plane Control Plane Network Programmability

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A Feasibility Study of a Leader-Follower Multi-robot Formation for TDLAS Assisted Methane Detection in Open Spaces 6th

A Feasibility Study of a Leader-Follower Multi-robot Formati...

引用

6th Iberian Robotics Conference (Robot)

作者： Monroy, Javier Ojeda, Pepe Gonzalez-Jimenez, Javier Univ Malaga Inst Mech Engn & Cyber Phys Syst IMECH UMA Machine Percept & Intelligent Robot Grp MAPIR UMA Malaga Spain

ISBN: (纸本)9783031586750;9783031586767

This work deals with the problem of detecting and localizing methane emission sources in open spaces with a mobile robot equipped with a remote gas detector (TDLAS). To reduce the long inspection time of traditional approaches which use the ground as the natural reflector, in this work, we analyze the feasibility of a leader-follower formation, where one robot, the leader, carries the remote gas detector that scans horizontally, parallel to the ground, and a second robot, the follower, that acts as an artificial reflector. We present a visual tracking mechanism for the relative pose estimation of both mobile platforms to extend the measurement range up to 10 m. Results in a 70 m(2) experimental area demonstrate that this approach is effective for a fast location of methane gas sources.

关键词： mobile robots relative pose estimation remote gas sensing gas distribution mapping gas source localization

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A Comparative Study and Adaptability of Mobility Models for UAV networks 1

引用

5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Indu Singh, Rishipal Guru Jambheshwar Univ Sci & Technol Hisar Haryana India

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

UAVs (Unmanned Aerial Vehicles) connected together autonomously create FANET (flying ad hoc network) gaining popularity. FANET is a subclass of MANET (mobile ad hoc network) and VANET (Vehicular ad hoc network) but different with respect to high mobility and aerodynamic limitations. High mobility leads to problems as connectivity loss, communication loss, sudden topological changes and energy consumption. Therefore, mobility models for FANETs gain attention. Traditional mobility models developed for MANETs have been adopted for FANETs. UAVs are highly mobile and change speed, direction and location rapidly;therefore, new models need to be developed. In this paper, a summarized analysis of traditional, as well as new developed mobility models for flying networks, has been presented on the basis of their category, random and fixed variables, high node mobility, adaptability for FANETs, smooth curve and collision avoidance. Some future insights to further enhance and new research related to mobility models for FANETs have been provided.

关键词： UAVs FANET Ad hoc network Mobility models Stochastic Adaptability

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Patterns in Human Activity Recognition Through Machine Learning Analysis Towards 6G Applications

Patterns in Human Activity Recognition Through Machine Learn...

引用

Intelligent systems Conference

作者： Mashudi, Nurul Amirah Ahmad, Norulhusna Izhar, Mohd Azri Mohd Kaidi, Hazilah Md Mohamed, Norliza Noor, Norliza Mohd Univ Teknol Malaysia Fac Artificial Intelligence Kuala Lumpur Malaysia

ISBN: (纸本)9783031664274;9783031664281

Human activity recognition uses smartphone sensors to identify individual activities. The ability of current sensor-based human activity detection, such as smartphone-based activity recognition, to reliably identify physical activities has declined. Sixth-generation (6G) systems aim to integrate communication and sensing seamlessly. Human activity recognition is a sensing task with many applications in smart homes, emergency systems, and games. It can replace dedicated sensors by monitoring communication signals. Human activity recognition is crucial in several domains and practical applications. Thus, there is a high demand for a support system that can offer information on a user's present activity by concealing the complex activity recognition. This paper proposed several machine learning methods, such as decision trees, random forests, logistic regression, and K-nearest neighbours, to classify human activity recognition. The proposed methods evaluate the performance of human activity recognition using the WISDM dataset based on the acceleration and angular velocity of a smartphone and smartwatch. The proposed methods accurately identified 18 activities, including walking, jogging, stairs, sitting, standing, typing, brushing teeth, eating soup, eating chips, eating pasta, drinking from a cup, eating sandwich, kicking a soccer ball, playing catch tennis ball, dribbling basketball, writing, clapping, and folding clothes. A comparative analysis was conducted for each activity to determine the precision, recall, and F1 scores. The experimental results showed that K-NN achieved better accuracy at 76.48%, compared to a decision tree, random forest, and logistic regression. A comparative analysis is also performed with state-of-the-art methods.

关键词： Human activity recognition Machine learning Smartphone Smartwatch Accelerometer 6G

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Survival Signature for Reliability Quantification of Large systems and networks 1

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19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Coolen, Frank P. A. Coolen-Maturi, Tahani Univ Durham Durham DH1 3LE England

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

The survival signature is a useful tool for quantification of reliability of large systems and networks with relatively few types of components. This paper provides an introductory overview of the survival signature, with emphasis on recent developments and challenges to enable its use for practical applications. Topics discussed include different survival signatures for specific scenarios, the level of detail in reliability modelling, and computational aspects. In the literature, system reliability quantification is mostly focused on binary state systems with typically only few components in relatively straightforward configurations and with single functions. Real-world systems, on the other hand, often have multiple levels of functioning and consist of many components in a variety of configurations while they may need to perform multiple functions, leading to substantial challenges for reliability quantification. In the twelve years since its introduction, the survival signature has gained much attention in the literature, and progress has been made on the challenges indicated above. However, many challenges remain, including some theoretical questions about the very nature of system reliability in real-world situations.

关键词： Large systems Survival signature System reliability networks

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VOTUM: Secure and Transparent E-Voting System 9th

VOTUM: Secure and Transparent E-Voting System

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Egocheaga, Joaquin Angulo, William Salas, Cesar Peruvian Univ Appl Sci Lima Peru

ISBN: (纸本)9789819745807;9789819745814

Elections are an essential part of citizens' rights, and they are also conducted in universities and colleges to ensure transparent selection of ideal authorities while preventing identity fraud and information loss among voters. It is worth noting that Internet voting has gained significant attention in recent years, with many organizations worldwide planning to experiment with and implement it. To address these challenges, we propose VOTUM, a free fraud e-voting system that incorporates two authentication methods: facial recognition and one-time password (OTP). Additionally, the system employs two cryptographic algorithms to encrypt voters' information throughout the voting process and generates a unique code to verify the successful casting of votes. VOTUM's design is creative, flexible, colorful, and animated, aiming to encourage students and professors to fulfill their civic duty by participating in elections. Through interviews conducted with 31 students and university professors, we achieved a 90% trust level and a 15% margin of error to assess satisfaction with transparency, trust, and user experience within the VOTUM system. The results indicated a satisfaction level of over 90%, showing the significant contribution of this research in enhancing trust and transparency in the voting processes of universities and colleges.

关键词： E-voting Facial recognition One-time password Security voting Amazon Rekognition Secure e-voting Transparency voting Face recognition voting

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Human Pose Recognition Using Deep Learning 9th

Human Pose Recognition Using Deep Learning

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Javaid, Sameena Ubaid, Muhammad Talha Bahria Univ Dept Comp Sci Sch Engn & Appl Sci Karachi Campus Karachi Sindh Pakistan Univ Cent Punjab Fac Informat Technol & Comp Sci Lahore Pakistan

ISBN: (纸本)9789819735556;9789819735563

The recognizing human body posture is an enduring subject in the computer vision-based deep learning field that has highlighted numerous challenges in the past. Human activity analysis is useful in numerous fields, including security, video games, physical treatment, and so on. Exercises or yoga postures are one of the difficulties in estimating human poses. The current study designates a hybrid deep learning methodology for properly recognizing various yoga poses. A publicly available dataset of 27 individuals captured in an in-house premises, serene of 19 females and 8 males, performing each of the ten yoga poses is used. Yoga poses, such as "garland pose", "happy baby pose", "head to knee pose", "lunge pose", "mountain pose", "raise arm pose", "seated forward bend", "staff pose", "plank pose", and "standing forward bend" are used. A hybrid deep learning model based on convolutional neural network (CNN) and long short-term memory (LSTM) is giving some promising results for yoga pose recognition on real-time videos. Using CNN extracting features from key points of each frame obtained from OpenPose and LSTM provides temporal predictions. The system achieves a test accuracy of 97.38% after aggregating predictions over 45 frames, indicating its robustness in dealing with temporal variations. Real-time testing with 100 people offers an accuracy of 97.22%, demonstrating the model's practical application in scenarios such as yoga training and health monitoring.

关键词： Human pose detection Long short-term memory Yoga pose estimation

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Solving a Dynamic Scheduling Problem for a Manufacturing System with Reinforcement Learning

Solving a Dynamic Scheduling Problem for a Manufacturing Sys...

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Intelligent systems Conference (IntelliSys)

作者： Heik, David Bahrpeyma, Fouad Reichelt, Dirk Univ Appl Sci Dresden Fac Informat Math D-01069 Dresden Germany

ISBN: (纸本)9783031477232;9783031477249

A recent trend in the manufacturing community for increased customization capabilities and shorter production cycles has necessitated an increasing the level of flexibility and, consequently, more complexity in the development of control mechanisms. The increased complexity as well as the presence of uncertainty in such problems has been persuading researchers to attempt intelligent controllers who can also adapt to changes in such dynamic environments. One common problem in manufacturing systems is production scheduling, specifically when optimization on the job assignment should be carried out under uncertainty. In recent years, reinforcement learning has become increasingly popular for developing intelligent systems due to its ability to handle uncertainty. In this paper, we will develop an intelligent controller for our IIoT Test Bed at the HTW Dresden for production scheduling with uncertain operation times. In this study, we will employ three state-of-the-art reinforcement learning methods including DDQN, PPO and RecurrentPPO to perform dynamic production scheduling. The results show that RL was able to converge to optimal solutions that are superior to traditional heuristic methods for the complex problem of scheduling under uncertainty.

关键词： Reinforcement Learning Proximal Policy Optimization Smart Production systems Manufacturing Double Deep Q-Network

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Camera- and Sensor Data-Based Process Observation in Liquid Steel Production 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Thumfart, Maria Roessler, Roman Gruber, Christine Wachlmayr, Johann K1 MET GmbH A-4020 Linz Austria Voestalpine GmbH A-4020 Linz Austria

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

The Ruhrstahl-Heraeus (RH) degassing plant is of vital importance for ultra-clean steel production, due to its effective degassing and homogenizing, but its vacuum-based nature and the high temperatures of liquid steel renders the process rather inaccessible to direct observations. We developed a camera-based image analysis solution, which in combination with enhanced sensor data evaluation (e.g., chamber pressure, offgas composition or O-2-injection) determines the status of the process and detects irregularities and malfunctions of the plant. The proposed research helps to deeper understand the RH process in steelmaking and provides the basis for a future on-line decision support system for operation.

关键词： image analysis data analysis secondary metallurgy

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