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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure

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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor wit...

IEEE SENSORS

作者： Kamalesh Tripathy Dhanranjan Kumar Karthick Thiyagarajan Mitradip Bhattacharjee Department of Electrical Engineeringand Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a flexible capacitive tactile sensor based on a polydimethylsiloxane (PDMS) elastomer, which is well-suited for a wide range of applications. The sensor incorporates an n-channel depletion-mode MOSFET to achieve an extended gate architecture. To enhance the sensing performance, TiO 2 nanoparticles are incorporated as nanofillers, resulting in an improved dielectric material ideal for highly sensitive capacitive pressure and tactile sensors. The 99PDMS-01TiO 2 nanocomposite (comprising 99 wt % PDMS and 1 wt % TiO 2 ) is utilized for promising flexible tactile sensor applications due to the synergistic effects between the PDMS matrix and TiO 2 nanoparticles, which enhance both the mechanical and sensing properties of the composite. The fabricated sensor exhibits a sensitivity of $\mathbf{30} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ within the range of 0-10.5 kPa and a sensitivity of $\mathbf{9} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ in the range of 10.5-30 kPa.

关键词： Nanoparticles MOSFET Sensitivity Nanocomposites Capacitors Tactile sensors Logic gates Robustness Sensors Titanium dioxide

Estimating Complexity for Perception-based ADAS in Unstructured Road Environments

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Estimating Complexity for Perception-based ADAS in Unstructu...

IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance Systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

Route Control of Four-rotor UAV based on Brain-computer Interface in Virtual Reality 12

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Route Control of Four-rotor UAV based on Brain-Computer Inte...

12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Niu, Lan Bin, Jianxiong Wang, Junkongshuai Zhan, Gege Jiang, Haifeng Dong, Zhiyan Zhang, Lihua Kang, Xiaoyang Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Province Yiwu City China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

This article presented an experiment to determine the effect of dynamic 3D vision-evoked modules on the performance of the system we designed to steer a four-rotor unmanned aerial vehicle (UAV). Nowadays, UAV system is widely used in various industries, and UAV control has always been a focus of research. The Brain-computer Interface (BCI) system was based on the well-known steady-state visually evoked potentials (SSVEP). The specificity of the interface was the integration of dynamic 3D paradigms in virtual reality. In order to achieve a more immersive and interactive scene, VR scenes were moving synchronously with the UAV. Users can achieve remote control of four-rotor UAV through virtual scenarios. Ten subjects participated in the experiment and operated the UAV to complete a specific track. The dynamic paradigm based on the experimenter's feedback may indeed affect their attention, we proposed an effective solution to overcome this problem. The detailed results of these experiments were reported in this paper and we discussed the possible causes of performance loss under such conditions. © 2022 IEEE.

关键词： Brain computer interface

KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization

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arXiv 2022年

作者： Wang, Jiafeng Chen, Zhaoyu Jiang, Kaixun Yang, Dingkang Hong, Lingyi Guo, Pinxue Guo, Haijing Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai200433 China

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously, adversarial examples exhibit transferability across models, enabling practical black-box attacks. However, existing methods are still incapable of achieving the desired transfer attack performance. In this work, focusing on gradient optimization and consistency, we analyse the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these challenges, we introduce Global Momentum Initialization (GI), providing global momentum knowledge to mitigate gradient elimination. Specifically, we perform gradient pre-convergence before the attack and a global search during this stage. GI seamlessly integrates with existing transfer methods, significantly improving the success rate of transfer attacks by an average of 6.4% under various advanced defense mechanisms compared to the state-of-the-art method. Ultimately, GI demonstrates strong transferability in both image and video attack domains. Particularly, when attacking advanced defense methods in the image domain, it achieves an average attack success rate of 95.4%. The code is available at https://***/Omenzychen/Global-Momentum-Initialization. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models

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arXiv 2024年

作者： Guo, Jiayi Chen, Zan Ji, Yingrui Zhang, Liyun Luo, Daqin Li, Zhigang Shen, Yiqin Glasgow College University of Electronic Science and Technology of China Chengdu China School of Engineering University of Glasgow Glasgow United Kingdom Toursun Synbio Shanghai China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Research Institute Ubtech Robotics Corp Shenzhen China Qiyuan Lab. Beijing China Department of Computer Science Johns Hopkins University MD United States

Automated Machine Learning (AutoML) can streamline the complexities of machine learning pipelines. However, existing AutoML frameworks focus on discriminative tasks leaving generative tasks unexplored. Moreover, existing AutoML frameworks often lack interpretability and user engagement during the model development and training process, due to the absence of human-centered designs. To address these challenges, we propose UniAutoML, a human-centered AutoML framework that leverages Large Language Models (LLMs) to unify the automation of both discriminative (e.g., Transformers and CNNs for classification or regression) and generative tasks (e.g., fine-tuning diffusion models or LLMs). UniAutoML enables natural language interactions between users and the framework to provide real-time feedback and progress updates, thus enhancing the interpretability, transparency, and user control, where users can seamlessly modify or refine the model training in response to evolving needs. To ensure the safety and reliability of LLM-generated content, UniAutoML incorporates a safety guard-line that filter inputs and censor outputs. We evaluated UniAutoML’s performance and usability through experiments conducted on eight diverse datasets and user studies involving 25 participants. Our results demonstrate that UniAutoML not only achieves higher performance but also improves user control and trust in the AutoML process, which makes ML more accessible and intuitive for a broader audience. The code for UniAutoML is available at: https://***/richarf22/UniAutoML. © 2024, CC BY.

关键词： Digital elevation model

Analysis of Vegetation Indices in the Cotton Crop in South Asia region using UAV Imagery

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Analysis of Vegetation Indices in the Cotton Crop in South A...

International Conference on Emerging Technologies, ICET

作者： Noor us Sabah Muhammad Usama Zuhair Zafar Muhammad Shahzad Muhammad Moazam Fraz Karsten Berns School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Aerospace and Geodesy Technical University of Munich (TUM) Munich Germany Department of Computer Science Robotics Research Lab Technische Universität Kaiserslautern (TUK) Kaiserslautern Germany

ISBN: (纸本)9781665459938

Cotton is one of the most economically significant crops in the South Asia region. Following the decline in cotton production in recent years, it is necessary to study and analyse cotton growth patterns by using modern precision agriculture techniques to enhance crop yield and quality. Multispectral imagery plays a vital role in estimating crop quantification by computing vegetation indices at different phenological stages of the crop. It facilitates in estimating plant health and impact on yield before the visible symptoms appear. In this study, four different vegetation indices maps are computed using two-band multispectral imagery acquired through Unmanned Aerial Vehicle (UAV). The UAV multispectral data is collected from two fields with different sowing dates using Sentera NDVI single sensor. The variation in the vegetation indices map generated is analysed by vegetation index thresholding and k-means clustering. By using Sentinel-2A imagery, an average value of each vegetation index for every phenological stage is validated. The correlation coefficient of each vegetation index conveys a positive progression trend and validates the accuracy of UAV data computed results. In conclusion, vegetation indices computed using multispectral UAV imagery conveys vital information regarding the progression of crop phenology and growth pattern.

关键词： Image resolution Asia Vegetation mapping Crops Autonomous aerial vehicles Market research Cotton

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS