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Epidermal piezoresistive structure with deep learning-assisted data translation

npj Flexible Electronics 2022年第1期6卷 672-680页

作者： Changrok So Jong Uk Kim Haiwen Luan Sang Uk Park Hyochan Kim Seungyong Han Doyoung Kim Changhwan Shin Tae-il Kim Wi Hyoung Lee Yoonseok Park Keun Heo Hyoung Won Baac Jong Hwan Ko Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan University(SKKU)Suwon 16419Republic of Korea School of Chemical Engineering Sungkyunkwan University(SKKU)Suwon 16419Republic of Korea Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Ajou UniversitySuwon 16499Republic of Korea School of Electrical Engineering Korea UniversitySeoul 02841Republic of Korea Biomedical Institute for Convergence(BICS) Sungkyunkwan University(SKKU)Suwon 16419Korea Department of Organic and Nano System Engineering Konkuk UniversitySeoul 05029Republic of Korea Department of Advanced Materials Engineering for Information and Electronics Kyung Hee UniversityYongin 17104Republic of Korea School of Semiconductor and Chemical Engineering Jeonbuk National UniversityJeonju 54896Republic of Korea

Continued research on the epidermal electronic sensor aims to develop sophisticated platforms that reproduce key multimodal responses in human skin,with the ability to sense various external stimuli,such as pressure,shear,torsion,and *** development of such applications utilizes algorithmic interpretations to analyze the complex stimulus shape,magnitude,and various moduli of the epidermis,requiring multiple complex equations for the attached *** this experiment,we integrate silicon piezoresistors with a customized deep learning data process to facilitate in the precise evaluation and assessment of various stimuli without the need for such *** the ability to surpass conventional vanilla deep regression models,the customized regression and classification model is capable of predicting the magnitude of the external force,epidermal hardness and object shape with an average mean absolute percentage error and accuracy of<15 and 96.9%,*** technical ability of the deep learning-aided sensor and the consequent accurate data process provide important foundations for the future sensory electronic system.

关键词： utilize attached precise

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A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems

arXiv

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arXiv 2023年

作者： Qasemabadi, Armin Nejadhossein Mozaffari, Saeed Rezaei, Mahdi Ahmadi, Majid Alirezaee, Shahpour Electrical and Computer Engineering Department University of Windsor Windsor Canada Mechanical Automotive and Material Engineering Department University of Windsor Windsor Canada Institute for Transport Studies University of Leeds United Kingdom

Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional modules in advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle communication (V2V), vehicles can share traffic information with surrounding vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies on the vehicle's sensors to obtain the position and velocity of the leading vehicle, CACC also has access to the acceleration of multiple vehicles through V2V communication. This paper compares the type of information (position, velocity, acceleration) and the number of surrounding vehicles for driver lane change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD dataset to predict lane change intention. Results indicate a significant improvement in accuracy with an increase in the number of surrounding vehicles and the information received from them. Specifically, the proposed model can predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and CACC scenarios, respectively. © 2023, CC BY.

关键词： Adaptive cruise control

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AL-Annotator: An Active Learning-based Cervical Cell Annotation System 29

AL-Annotator: An Active Learning-based Cervical Cell Annotat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jiang, Yangbo Zhang, Shuting Wu, Jinggen Zhu, Xumei Liu, Xiao Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang Sci-Tech University College of Information Science and Engineering Hangzhou China Zhejiang University Women's Hospital School of Medicine Hangzhou China Zhejiang Institute of Mechanical and Electrical Engineering College of Continuing Education Hangzhou China Beijing Huawei Digital Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350330717

When deep learning is applied to cervical cell screening, it encounters challenges related to the inefficient labeling of data and a shortage of annotators. To mitigate these challenges, this study introduces AL-Annotator, an active learning-based cervical cell annotation system. This system consists of three key components: an interactive interface designed for efficient pathologists' review, a front-end and back-end separated cervical cell annotation system, and an active learning-based annotation method. Compared to traditional classification-based annotation methods, our annotation system achieves a 17.74% reduction in annotation quantity while improving the accuracy by 1.92%. This annotation system is crucial for developing and testing new machine learning and artificial intelligence algorithms for cervical screening, thereby facilitating advancements in diagnostic tools and methodologies. © 2023 IEEE.

关键词： active learning annotation system cervical cell classification system architecture

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Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking 7

Design and Implementation of Neuro-Fuzzy Control Approach fo...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kiruba Sandou, D. Sunad Kumara, A.N. Choudhary, Binod Kumar Gurpur, Shashikala Sarishma Natrayan, L. Sivaramkumar, M. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Pondicherry605107 India BGS Institute of Technology Department of Computer Science and Engineering Adichunchanagiri University Karnataka Mandya571448 India ARKA Jain University Department of Electrical and Electronics Engineering Jharkhand Jamshedpur832108 India Maharashtra Pune411014 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamilnadu Chennai602105 India Karpagam Academy of Higher Education Department of EEE Tamiinadu Coimbatore India

ISBN: (纸本)9798350340600

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications. © 2023 IEEE.

关键词： Controllers

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Intelligent Automated Interference for the Protection of Road Safety 1

Intelligent Automated Interference for the Protection of Roa...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Pant, Gargi Bahuguna, Rajesh Pandey, Shweta Gehlot, Anita Yadav, Satya Prakash Pachauri, Rupendra Kumar Uttaranchal University Law College Dehradun Dehradun India Uttaranchal University Lcd Division of Research &innovation Dehradun India Uttaranchal University Uit Division of Research &innovation Dehradun India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Noida India University of Petroleum and Energy Studies School of Engineering Electrical Cluster Dehradun India

ISBN: (纸本)9798350338027

Development in Industry 4.0 is ongoing and as a result, the inculcation of technology has enhanced that use of state-of-the-art technology in different spheres of society. The road safety is an essential component of a life longingprocess in modern era. This makes it important to use technology for making the road transport smooth and efficient. The safety in road transporthas also included the use of technology for providing an efficient model and smooth functioning. The paper reviews different models of road safety with the use of Al including ANN (Artificial Neural Network), Fuzzy Logic model (FLM), Vehicular Ad-hoc Network (VANET) and the use of CNN (Convolutional Neural Network). Additionally, the paper reviews the models of IoT including an app in smartphone which canshow the cracks in the road so that accidents can be prevented, use of mobile application to find parking with the use of infrared sensors and Raspberry Pi model to get the real time traffic condition's data. Lastly, the paper after discussing the challenges of the different models in road safety, provide suggestions to refine and enhance the present road safety models using AI and IoT. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Quantum algorithms for sampling log-concave distributions and estimating normalizing constants 22

Quantum algorithms for sampling log-concave distributions an...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Andrew M. Childs Tongyang Li Jin-Peng Liu Chunhao Wang Ruizhe Zhang Joint Center for Quantum Information and Computer Science Department of Computer Science and Institute for Advanced Computer Studies University of Maryland Center on Frontiers of Computing Studies and School of Computer Science Peking University Simons Institute and Department of Mathematics UC Berkeley Department of Computer Science and Engineering Pennsylvania State University Department of Computer Science The University of Texas at Austin

ISBN: (纸本)9781713871088

Given a convex function f : ℝd → ℝ, the problem of sampling from a distribution ∝ e-f(x) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants $\int_{\mathbb{R}^d}e^{-f(x)}\mathrm{d} x$. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number κ and dimension d) of analogous classical algorithms that use gradient (first-order) queries, even though the quantum algorithms use only evaluation (zeroth-order) queries. For estimating normalizing constants, these algorithms also achieve quadratic speedup in the multiplicative error e. Second, we develop quantum Metropolis-adjusted Langevin algorithms with query complexity Õ(κ1/2d) and Õ(κ1/2d3/2/ε) for log-concave sampling and normalizing constant estimation, respectively, achieving polynomial speedups in κ, d, ε over the best known classical algorithms by exploiting quantum analogs of the Monte Carlo method and quantum walks. We also prove a 1/ε1-o(1) quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in ε.

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The Impact of Transient and Stable Patterns of Functional Connectivity in Emotion Recognition

The Impact of Transient and Stable Patterns of Functional Co...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Yinghao Huang Xucheng Liu Ye Li Chio-In Ieong Yong Hu Feng Wan Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau China Shenzhen Institute of Advanced Technology Chinese Academy Sciences Shenzhen China Guangdong Institute of Intelligence Science and Technology Zhuhai China Department of Orthopaedics and Traumatology The University of Hong Kong Hong Kong China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The achievement of emotional functions relies on the interactions of various functional systems of the human brain. Numerous studies tried to explore the mechanism of the emotions based on the functional connectivity. However, the contribution of the transient and stable patterns of brain communication in brain emotions was still unclear. The recently proposed activation network framework assumed the activity of functional connectivity (AFC) and background of functional connectivity (BFC) to respectively represent transient and stable patterns of functional connectivity. In this paper, we employed the activation network framework to SEED-IV dataset to achieve the emotion recognition and evaluate the performance of the transient and stable patterns in emotional activities. The top 100 critical connections of each subject were extracted by a data-driven feature selection strategy. The critical connections across all subjects of both AFC and BFC suggested the importance of Gamma band in emotion recognition. Especially, the AFC and BFC showed the different communication modes during the emotions. Finally, the subject-independent classification was employed on each subject’s critical connections to achieve the emotion recognition. The BFC showed the best classification accuracy of 78.71% ± 1.73% (mean ± std). The findings demonstrated that human emotions were mostly influenced by the consistent brain communication patterns. The findings of this investigation offer a new insight on the studies of human emotion.

关键词： Emotion recognition Accuracy Virtual environments Feature extraction Electroencephalography Transient analysis Computational intelligence

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Node Mobility and Encounter Rate Metrics to Enhance Stability in MANET

Node Mobility and Encounter Rate Metrics to Enhance Stabilit...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Gnanajeyaraman Rajaram Archana Vishveswari R S R. Deepa A. Packialatha Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Varuvan Vadivelan Institute of Technology Dharmapuri Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay.

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Integrated Sensing and Communication With mmWave Massive MIMO: A Compressed Sampling Perspective

Integrated Sensing and Communication With mmWave Massive MIM...

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作者： Gao, Zhen Wan, Ziwei Zheng, Dezhi Tan, Shufeng Masouros, Christos Ng, Derrick Wing Kwan Chen, Sheng Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology School of Information and Electronics Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework. © 2002-2012 IEEE.

关键词： Compressed sensing

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Enhancing Video Captioning: A Bayesian Normalized Attention-based Multi-Dimensional Graph Network with Moss Growth Optimization

Enhancing Video Captioning: A Bayesian Normalized Attention-...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Khallikkunaisa H. S. Niranjana Murthy Shirish Kulkarni Syed Mohd Uzair Iqbal Umang Pancha Natrayan L Department of Computer Science and Engineering HKBK College of Engineering Bangalore Karnataka India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bangalore Karnataka India Symbiosis Law School Symbiosis Centre for Advanced Legal Studies and Research Symbiosis International (Deemed University) Pune India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Post Limda Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

The explosive increase of video data requires the creation of advanced automated video captioning systems that can produce effective textual descriptions. Current methods, such as traditional deep learning (DL) models, are plagued with drawbacks such as high computational expense, overfitting, and inefficacy in contextual modeling, resulting in poor caption generation. For resolution of these difficulties, this contribution introduces the Bayesian Normalized Attention-based Multi-Dimensional Graph Network with Moss Growth Optimization (BN-AMGN-MGO) model, engineered to improve video captioning automatization on the MSVD set. The proposed framework starts off with pre-processing, in which unnecessary frames are removed via template matching to get only the representative frames. The feature encoding process utilizes a Bayesian Normalized Neural Network (BNNN) for dynamically manipulating probabilistic weight distributions to counter overfitting and increase robustness. During the decoder process, a Hierarchical Attention-based Multi-Dimensional Edge Graph Neural Network (HAM-GNN) combines temporal context modeling, graph neural networks, and hierarchical attention mechanisms to create structured textual descriptions. Classification stage employs a temporal attention mechanism to project dialogue act labels into attention-weighted representations, utilizing speaker dependencies for better accuracy. To further optimize performance, MGO is employed to refine HAM-GNN’s hyperparameters through wind direction determination, spore dispersal search, dual propagation search, and cryptobiosis mechanisms, ensuring effective exploration and exploitation of the search space. The proposed BN-AMGN-MGO model achieves the lowest MSE (0.021), highest PSNR (34.89), and SSIM (0.905), ensuring high-quality caption generation. Additionally, it outperforms existing models with an accuracy of 94.5% while maintaining the shortest computational time (5.6 sec), making it a robust and efficient

关键词： Attention mechanisms Accuracy Computational modeling Streaming media Graph neural networks Encoding Bayes methods Computational efficiency Context modeling Overfitting

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