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IEEE Conference on Decision and Control

作者： Zhiqi Tang Rita Cunha Tarek Hamel Carlos Silvestre Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal I3S-CNRS Université Côte d'Azur and Institut Universitaire de France France Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macao China

This paper proposes a novel constructive barrier feedback for reactive collision avoidance between two agents. It incorporates this feature in a formation tracking control strategy for a group of 2nd-order dynamic robots defined in three-dimensional space. Using only relative measurements between neighboring agents, we propose an elegant decentralized controller as the sum of a nominal tracking controller and the constructive barrier feedback for leader-follower formations under a directed single-spanning tree graph topology. The key ingredient is the use of divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring robots without compromising the nominal controller's performance. Compared to traditional barrier function-based optimization controllers, the proposed constructive barrier feedback avoids feasibility issues and results in more computationally efficient control algorithms with systematic equilibrium analysis. Simulations are provided to validate the effectiveness of the proposed control method.

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Social engineering attack types and prevention techniques- A survey

Social engineering attack types and prevention techniques- A...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Shruti Bhatla Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey Department of Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

The term "Cyber Security" is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim’s social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article.

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Constructive barrier feedback for collision avoidance in leader-follower formation control

arXiv

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arXiv 2023年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal Division of Decision and Control Systems KTH Royal Institute of Technology Sweden I3S-CNRS Université Côte d'Azur Institut Universitaire de France France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

关键词： Collision avoidance

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Dark Vision Assistive Application for Deaf-Blind People

Dark Vision Assistive Application for Deaf-Blind People

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Prithi Samuel Rajesh Kumar Dhanaraj Balamurugan Balusamy Narmatha C Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Associate Dean- Students Shiv Nadar University Noida India Industrial Innovation & Robotics Center Faculty of Computers and Information Technology University of Tabuk Tabuk City Kingdom of Saudi Arabia

For visually impaired, deaf-blind people, both communication and navigation are difficult activities and indoor navigation is becoming increasingly harder for them. It is even more difficult for deaf-blind people than it is for non-visually impaired people. People with visual impairments or deaf- blindness frequently rely on external support systems to make judgments, such as trained canines, humans, or specialized equipment. As a result, deaf-blind people are in desperate need of an assistive application that allows blind people to navigate and communicate freely without relying on others. The goal of the application is to create a more adaptable and cost-effective system for deaf-blind and visually impaired people. Through both outdoor and interior applications, as well as walking in even unfamiliar locations without relying on others.

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Activity With Gender Recognition Using Accelerometer and Gyroscope

Activity With Gender Recognition Using Accelerometer and Gyr...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ahmed Sharshar Ahmed Fayez Yasser Ashraf Walid Gomaa Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Egypt-Japan University of Science and Technology and Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9781665423182

ISBN: (纸本)9781665446198

Recently, the use of the inertia measurement units (IMU), especially the gyroscope and accelerometer sensors, has increased in the human activity recognition (HAR) due to the extensive use of smartwatches and smartphones. In addition to the high quality and efficiency result in by these sensors, they can capture the data of the body dynamic motion as function of time, then the stream of data is analyzed and processed to classify and predict the action being done, the gender, the health status and many other characteristics. Gender and activity recognition have been deeply studied recently, using various ways to recognize either of them through many interfaces, like voice, image, or inertia measurement motion data. All these types of classifications are crucial in many applications such as recommendation systems, speech recognition, sports tracking, security and most importantly in healthcare. In this research, we present two models (hierarchical model and joint distribution model) and compare between two datasets (MoVi and MotionSense), using only two IMU sensors on right and left hand and motion sense dataset using mobile phone, to predict gender with activity and see how every activity reflect on gender, and explore the efficiency on using autocorrelation function as a feature extractor and compare between three classifiers, Random Forest (RF), Support Vector Machine (SVM) and Convolution Neural Network (CNN).

关键词： Support vector machines Accelerometers Speech recognition Predictive models Activity recognition Feature extraction Gyroscopes

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Attention state of children during Schulte tables task

Attention state of children during Schulte tables task

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Grubov, Vadim Maksimenko, Vladimir Kurkin, Semen Khramova, Marina Aleksandrova, Natalya Hramov, Alexander Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Lab Russia Saratov State University Faculty of Computer Science Saratov Russia

ISBN: (纸本)9781728187631

We have analyzed EEG signals of children during cognitive load of specific type in order to estimate level of attention during this task. EEG signals were recorded in accordance with proposed design of experiment. For obtained EEG data we have analyzed behavioral characteristics as well as EEG-related characteristics. We have found that behavioural characteristic changes through the task, so may the attention level. We have also found that cognitive task completion is accompanied by appearance of spatial structure-specific distribution of wavelet energy across cortex, which partially changes during completion of the task as well. We believe that changes in attention level are tied to changes in energy distribution, so we suggest this distribution to be used as the marker for attention state estimation. © 2020 IEEE.

关键词： Electroencephalography

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EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm 8

EMG Signals Classification on Human Activity Recognition usi...

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8th NAFOSTED Conference on Information and Computer science, NICS 2021

作者： Nurhanim, Ku Elamvazuthi, I. Izhar, L.I. Capi, Genci Su, Steven Research Group Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Malaysia Assistive Robotics Laboratory Department of Mechanical Engineering Faculty of Science and Engineering HOSEI University Tokyo184-8584 Japan School of Biomedical Engineering University of Technology Sydney Australia

ISBN: (纸本)9781665410014

In Human activity recognition (HAR) research, it is a common practice to use wearable sensors to acquire the signals for human daily activities. In this study, an experimental data from electromyography (EMG) wireless sensors is analysed for six different activities recognition. This paper aims to compare EMG signals of left and right of upper leg muscles by using Random Forest (RF) Machine Learning Classifier. The HAR processing comprises of data filtering and segmentation, data feature extraction, feature selection of the data, and classification. Model evaluation of holdout method is implemented for classification assessment. The performance of all human daily activities is evaluated according to the comparison of precision and recall for each activity. The results show combined muscles obtained the highest precision and recall on running activity with 89.2% and 88.3%. The highest overall accuracy of classification was 82.08% on the bicep femoris left and right (BF-Left Right). © 2021 IEEE.

关键词： Decision trees

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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3D semantic mapping from arthroscopy using out-of-distribution pose and depth and in-distribution segmentation training

arXiv

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arXiv 2021年

作者： Jonmohamadi, Yaqub Ali, Shahnewaz Liu, Fengbei Roberts, Jonathan Crawford, Ross Carneiro, Gustavo Pandey, Ajay K. School of Electrical Engineering and Robotics Science and Engineering Faculty Queensland University of Technology United Kingdom Australian Institute for Machine Learning School of Computer Science University of Adelaide

Minimally invasive surgery (MIS) has many documented advantages, but the surgeon's limited visual contact with the scene can be problematic. Hence, systems that can help surgeons navigate, such as a method that can produce a 3D semantic map, can compensate for the limitation above. In theory, we can borrow 3D semantic mapping techniques developed for robotics, but this requires finding solutions to the following challenges in MIS: 1) semantic segmentation, 2) depth estimation, and 3) pose estimation. In this paper, we propose the first 3D semantic mapping system from knee arthroscopy that solves the three challenges above. Using out-of-distribution non-human datasets, where pose could be labeled, we jointly train depth+pose estimators using self-supervised and supervised losses. Using an in-distribution human knee dataset, we train a fully-supervised semantic segmentation system to label arthroscopic image pixels into femur, ACL, and meniscus. Taking testing images from human knees, we combine the results from these two systems to automatically create 3D semantic maps of the human knee. The result of this work opens the pathway to the generation of intra-operative 3D semantic mapping, registration with pre-operative data, and robotic-assisted arthroscopy. Copyright © 2021, The Authors. All rights reserved.

关键词： Endoscopy

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

arXiv

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arXiv 2024年

作者： Chauhan, Nidhika Kaur, Navneet Saini, Kamaljit Singh Verma, Sahil Alabdulatif, Abdulatif Khurma, Ruba Abu Garcia-Arenas, Maribel Castillo, Pedro A. University Institute of Computing Chandigarh University Punjab 143001 India Department of Computer Science and Engineering Chandigarh University Punjab 143001 India Department of Computer Science and Engineering Uttaranchal University Premnagar Uttarakhand Dehradun248007 India Department of Computer Science College of Computer Qassim University Buraydah52571 Saudi Arabia MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature. © 2024, CC BY.

关键词： Cloud computing

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