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4th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2022

作者： Bie, Yequan Zhu, Xiaolong Tong, Yize Liao, Lingjun Ouyang, Xiaxue Ling, Xinyi He, Junhui Toe, Teoh Teik South China University of Technology School of Automation Science and Engineering Guangzhou China School of Computer Science Wuhan University Wuhan China Southwest Jiaotong University School of Electronic and Electrical Engineering Sichuan China Communication University of China School of Animation and Digital Arts Beijing China School of Mathematics Wuhan University Wuhan China Nanyang Technological University College of Business Singapore Singapore

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

Facial age estimation is one of the most important tasks in the field of face recognition and recommendation system. Since the COVID-19 pandemic, people have been required to wear masks, which can be a challenge for traditional recognition methods. In this paper, an improved convolutional neural network architecture based on MobileNet is proposed to perform age estimation. For the challenge of masked faces, an innovative mask generation method using face keypoint detection is adopted, extracting the key points of the faces in order to add synthetic masks to simulate the real situations. Then we compare the estimation results of the original images and the synthetic images. Our method is applied to the WIKI Face dataset containing more than 150,000 images, and achieves MAE of 3.79 and 6.54 on unmasked and masked faces, respectively, which demonstrates the effectiveness of the proposed model. © 2022 IEEE.

关键词： Face recognition

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UAS-Based Autonomous Visual Inspection of Airplane Surface Defects

UAS-Based Autonomous Visual Inspection of Airplane Surface D...

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NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World 2022

作者： Tappe, Mark Dose, Daniel Oelsch, Martin Karimi, Mojtaba Hösch, Leonhard Heller, Leon Knufinke, Jan Alpen, Mirco Horn, Joachim Bachmeir, Christian Helmut Schmidt University University of Federal Armed Forces Hamburg Germany Technical University of Munich Munich Germany University of Applied Sciences Würzburg-Schweinfurt Würzburg Germany Lufthansa Technik AG Hamburg Germany Media Technology The Department of Electrical and Computer Engineering The Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

ISBN: (数字)9781510649743

ISBN: (纸本)9781510649736

Today’s maintenance tasks are time-consuming and therefore cost-intensive, in particular the manual inspection of commercial airliners. The joint project"AI-Inspection Drone" aims to provide a complete process chain for the surface damage detection of airliners based on an unmanned aerial system (UAS). To achieve this goal, visual data is gathered, which can later be evaluated by artificial intelligence m odels. The process chain is explained in detail, beginning with the simulation of the hangar environment, followed by insights about the indoor navigation of the UAS. Finally, it is validated through test flights around the airplane, respecting strict security and safety requirements. The 3D-simulation based on Gazebo was expanded with hardware-in-the-loop testing functionality by utilizing a camera-based motion capture system to track the UAS’s position in real-time and feed the position data back into the simulation, to test different inspection t asks. For the deployment of the UAS in the hangar, a 3D-LiDAR based SLAM algorithm is used to provide position and orientation data in relation to the airplane. Using a 3D model, which can be gathered beforehand with LiDAR scans, the airplane’s surface area is estimated to determine the mission waypoints and the corresponding inspection views for a high-resolution camera. A path planning algorithm controls the procedure of the inspection by evaluating an efficient path based on these waypoints and enables obstacle avoidance based on LiDAR data. With the proposed autonomous aerial inspection platform, the ground time of airplanes can be reduced, thus increasing the efficiency of the airplane inspection process. © 2022 SPIE.

关键词： Antennas

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"I Used To Carry A Wallet, Now I Just Need To Carry My Phone": Understanding Current Banking Practices and Challenges Among Older Adults in China

arXiv

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arXiv 2022年

作者： Jin, Xiaofu Fan, Mingming The Hong Kong University of Science and Technology China Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Managing finances is crucial for older adults who are retired and may rely on savings to ensure their lives’ quality. As digital banking platforms (e.g., mobile apps, electronic payment) gradually replace physical ones, it is critical to understand how they adapt to digital banking and the potential frictions they experience. We conducted semi-structured interviews with 16 older adults in China, where the aging population is the largest and digital banking grows fast. We also interviewed bank employees to gain complementary perspectives of these help givers. Our findings show that older adults used both physical and digital platforms as an ecosystem based on perceived pros and cons. Perceived usefulness, self-confidence, and social influence were key motivators for learning digital banking. They experienced app-related (e.g., insufficient error-recovery support) and user-related challenges (e.g., trust, security and privacy concerns, low perceived self-efficacy) and developed coping strategies. We discuss design considerations to improve their banking experiences. © 2022, CC BY-NC-ND.

关键词： Electronic money

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A FIWARE-based IoT Framework for Smart Water Distribution Management 12

A FIWARE-based IoT Framework for Smart Water Distribution Ma...

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Panagiotakopoulos, Theodor Vlachos, Dimitrios P. Bakalakos, Thanasis V. Kanavos, Andreas Kameas, Achilles Hellenic Open University School of Technology and Science Patras Greece Business School University of Nicosia Nicosia Cyprus University of Patras Department of Electrical and Computer Engineering Patras Greece Department of Digital Media and Communication Ionian University Kefalonia Greece

ISBN: (纸本)9781665400329

A key axis of design and implementation of sustainable water management solutions is based on the application of IoT, which enable the deployment of monitoring systems across the water distribution network, facilitate data acquisition in an automated manner and provide a rich set of data. Distributed software architectures are acknowledged as vital to realize smart water management systems, especially in large-scale and complex deployments with heterogeneous interacting entities. Current smart water systems mainly use commercial IoT platforms with only a few researches rooting for open source software solutions. However, both approaches have limitations as the closed APIs of commercial IoT platforms have been accused of hindering open market evolution, while open source solutions lack interoperability and data integration/sharing mechanisms. This paper presents an IoT framework based on FIWARE that aims to realize a highly flexible standards-based open source software solution for the development of smart water systems. We designed an architecture consisting of various FIWARE software components and two dashboard applications. We have also constructed a digital model of a part of a water distribution network and used a simulation dataset to showcase the framework's functionality and data visualization aspects. © 2021 IEEE.

关键词： Open source software

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A Review on the Application of Radiomics and Deep Learning for Disease Identification in Musculoskeletal Radiography

A Review on the Application of Radiomics and Deep Learning f...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Shyamali Das Rama Devi C M. Gomathy Nayagam D Suganthi Fidha Thachamkode K. Manoj Senthil Department of Artificial intelligence and Machine learning CMR institute of technology Bengaluru Electrical and Electronics Engineering St.Joseph's college of Engineering Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Department of Oral Medicine and Radiology KMCT Dental college Kozhikode Kerala Electronics and Communication Engineering Kongu Engineering College Erode

Musculoskeletal disorders cause most disability worldwide. Musculoskeletal (MSK) issues are diagnosed, predicted, and treated utilizing imaging methods, magnetic resonance imaging (MRI), and ultrasound, and their precise interpretation by specialized radiologists. Radiological scanning aids metabolic health, aging, and diabetes assessment. Proposed research shows how machine learning, specifically deep learning, may be used to analyze MRI data quickly and reliably, addressing a crucial clinical need in MSK radiology. As primary care radiography services grow, so does interest in how AI, and especially deep learning, might help radiologists and primary care physicians make accurate diagnoses and enhance patient treatment. Chronic pain impairs mobility, dexterity, and function. Diagnosis requires bone X-rays. Deep learning algorithms are increasingly used in musculoskeletal radiology, with promising outcomes. The proposed method uses a calibrated ensemble of deep learners to musculoskeletal radiograph recognition. In the suggested model uses three deep neural networks, which are commonly utilized in deep learning-based solutions for this problem domain. Publicly available dataset to demonstrates the suggested strategy outperforms three individual models and a standard ensemble learner. This study strongly supports the use of a calibrated ensemble approach for identifying abnormalities in musculoskeletal X-rays, where the greatest radiologist accuracy is between 0.95 and 0.98 in Cohen's kappa statistic.

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A Comprehensive Evaluation of Secured Electronic Voting System Design based on Face Biometric Authentication Policy

A Comprehensive Evaluation of Secured Electronic Voting Syst...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Pandarinath Potluri R Jayakarthik Shivam Agarwal Shobana S Venkata Padmavathi S Aarthi R Cse Department Swarnandhra College of Engineering and Technology Sitarampuram Narsapuram Dept of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Department of Information Technology GL Bajaj Institute of Technology and Management Gr. Noida Department of IT R.M.K Engineering College Chennai Electrical and Electronics Engineering GST GITAM Deemed to be University Telangana India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In the field of electronic voting systems, ensuring secure and reliable authentication mechanisms is paramount to safeguarding the integrity and fairness of the democratic process. Face biometric authentication has emerged as a promising approach, integrating advanced deep learning algorithms for accurate and efficient verification of voters based on their facial features. This paper presents a comprehensive overview of the system architecture and design considerations for integrating face biometric authentication into electronic voting systems. It describes the hardware and software components essential for system functionality, discusses the selection of appropriate technologies for face recognition and encryption, and elaborates on the design of user interfaces, database structures, and ballot generation systems. Furthermore, the paper delves into the implementation of face detection and recognition algorithms, emphasizing the importance of anti-spoofing measures and multi-factor authentication mechanisms to enhance security. It also addresses voter registration processes, ballot generation, the voting process, and vote counting and verification procedures within the context of face biometric authentication. Additionally, the paper highlights the significance of robust security measures, including encryption techniques, access controls, and regular security audits, to mitigate cyber threats and ensure the integrity of the electoral process. By integrating face biometric authentication into electronic voting systems, this paper aims to contribute to the development of secure, transparent, and inclusive electoral systems, fostering trust and confidence in democratic governance, with an accuracy rate of 96%.

关键词： Electronic voting systems Accuracy Face recognition Software algorithms Systems architecture User interfaces Biometric authentication Software Encryption Reliability

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Signal Processing with Machine Learning for Context Awareness in 5G Communication technology

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Mohandas, R. Lira, James Luis Alberto Nunez Gonzales, Walter Edgar Gomez Obaidi, Riyadh A. L. Ibraheem, Ibraheem Kasim Cotrina-Aliaga, Juan Carlos Shafi, Jana Pranesh, K.A. Alaric, J. Sam Department of ECE Balaji Institute of Technology & Science Warangal India University Nacional Mayor de San Marcos Peru Universidad Privada San Juan Bautista Peru Department of Accounting Al-Mustaqbal University College Hillah Babil Iraq Department of Computer Engineering Techniques Al-Rasheed University College Baghdad Iraq University Cesar Vallejo Peru Department of Computer Science College of Arts and Science Prince Sattam Bin Abdul Aziz University Wadi Ad-Dawasir11991 Saudi Arabia Department of Electrical and Electronics Engineering Study World College of Engineering Tamilnadu Coimbatore India Department of Electrical and Computer Engineering College of Engineering and Technology Wollega University Nekemte Ethiopia

To meet users' expectations for speed and reliability, 5th Generation (5G) networks and other forms of mobile communication of the future will need to be highly efficient, flexible, and nimble. Because of the expected density and complexity of 5G networks, sophisticated network control across all layers is essential. In this context, self-organizing network (SON) is among the essential solutions for managing the next generation of mobile communication networks. Self-optimization, self-configuration, and self-healing (SH) are typical SON functions. This research creates a framework for analyzing SH by exploring the impact of recovery measures taken in precarious stages of health. For this reason, our suggested architecture takes into account both detection and compensating operations. The system is broken down into some faulty states and the "fuzzy c-means"(FCM) approach is used to conduct the classifying. In the compensation process, the network is characterized as the Markov decision model (MDM), and the linear programming (LP) technique is implemented to find the most effective strategy for reaching a goal. Numerical findings acquired from a variety of situations with varying objectives show that the suggested method with optimized operations in the compensation stage exceeds the approach with randomly chosen actions. © 2023 R. Mohandas et al.

关键词： Numerical methods

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A tailored,electronic textile conformable suit for large-scale spatiotemporal physiological sensing in vivo

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npj Flexible Electronics 2020年第1期4卷 45-57页

作者： Irmandy Wicaksono Carson ITucker Tao Sun Cesar A.Guerrero Clare Liu Wesley M.Woo Eric J.Pence Canan Dagdeviren Media Lab Massachusetts Institute of TechnologyCambridge 02139 MAUSA Mechanical Engineering Massachusetts Institute of TechnologyCambridge 02139 MAUSA Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridge 02142 MAUSA Architecture and Planning Massachusetts Institute of TechnologyCambridge 02139 MAUSA

The rapid advancement of electronic devices and fabrication technologies has further promoted the field of wearables and smart ***,most of the current efforts in textile electronics focus on a single modality and cover a small ***,we have developed a tailored,electronic textile conformable suit(E-TeCS)to perform large-scale,multimodal physiological(temperature,heart rate,and respiration)sensing in *** platform can be customized for various forms,sizes and functions using standard,accessible and high-throughput textile manufacturing and garment patterning *** to a compression shirt,the soft and stretchable nature of the tailored E-TeCS allows intimate contact between electronics and the skin with a pressure value of around~25 mmHg,allowing for physical comfort and improved precision of sensor readings on *** E-TeCS can detect skin temperature with an accuracy of 0.1℃ and a precision of 0.01℃,as well as heart rate and respiration with a precision of 0.0012 m/s^(2) through mechano-acoustic inertial *** knit textile electronics can be stretched up to 30%under 1000 cycles of stretching without significant degradation in mechanical and electrical *** and theoretical investigations are conducted for each sensor modality along with performing the robustness of sensor-interconnects,washability,and breathability of the *** results suggest that our E-TeCS can simultaneously and wirelessly monitor 30 skin temperature nodes across the human body over an area of 1500 cm2,during seismocardiac events and respiration,as well as physical activity through inertial dynamics.

关键词： stretching sizes electronics

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FRAS: Federated Reinforcement Learning Empowered Adaptive Point Cloud Video Streaming

arXiv

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arXiv 2022年

作者： Gao, Yu Zhou, Pengyuan Liu, Zhi Han, Bo Hui, Pan The School of Cyber Science and Technology University of Science and Technology of China Hefei China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Department of Computer Science George Mason University United States Computational Media and Arts Hong Kong University of Science and Technology Guangzhou China

Point cloud video transmission is challenging due to high encoding/decoding complexity, high video bitrate, and low latency requirement. Consequently, conventional adaptive streaming methodologies often find themselves unsatisfactory to meet the requirements in threefold: 1) current algorithms reuse existing quality of experience (QoE) definitions while overlooking the unique features of point cloud video thus failing to provide optimal user experience, 2) most deep learning approaches require long-span data collections to learn sufficiently varied network conditions and result in long training periods and capacity occupation, 3) cloud training approaches pose privacy risks caused by leakage of user reported service usage and networking conditions. To overcome the limitations, we present FRAS, the first federated reinforcement learning framework, to the best of our knowledge, for adaptive point cloud video streaming. We define a new QoE model which takes the unique features of point cloud video into account. Each client uses reinforcement learning (RL) to train video quality selection with the objective of optimizing the user’s QoE under multiple constraints. Then, a federated learning framework is integrated with the RL algorithm to enhance training performance with privacy preservation. Extensive simulations using real point cloud videos and network traces reveal the superiority of the proposed scheme over baseline schemes. We also implement a prototype that demonstrates the performance of FRAS via real-world tests. © 2022, CC BY-NC-SA.

关键词： Reinforcement learning

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： S. Sivakumar Mali Ram Yadav A. Bharathi D Akila Dineshkumar P V. Banupriya Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Chennai India Department of Physical Education Lovely Professional University Punjab India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Pallavaram India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy.

关键词： Degradation Adaptation models Scalability Reinforcement learning Solids Real-time systems Batteries

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