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Real-time gesture control UAV with a low resource framework

Real-time gesture control UAV with a low resource framework

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2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Bhat, Manohar Mahto, Gopikishan Kesaria, Smit Femandes, Vikrant Arya, Kavi BMS College of Engineering Dept. of Electrical and Electronics Bangalore560019 India UPU Government Polytechnic Dept. of Computer Science and Engg. Durg491001 India Indian Institute of Technology Dept. of Systems and Control Mumbai400076 India Indian Institute of Technology Dept. of Computer Science and Engg. Mumbai400076 India

ISBN: (纸本)9781665433235

This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving dept. information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots. © 2021 IEEE.

关键词： Drones

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Assessing Respiratory Changes Induced by STN-DBS in a Rat Model

Assessing Respiratory Changes Induced by STN-DBS in a Rat Mo...

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International Conference on Emerging Technologies, ICET

作者： Shehla Inam Livia de Mesquita Teixeira Muhammad Waqar Muhammad Ishaq Shahzaib Iqbal Fasih Ullah Jitendran Muthuswamy School of Biological & Health Systems Engineering Arizona State University Tempe USA Dept. of Biomedical Engineering Balochistan University of Engineering & Technology Khuzdar Pakistan Dept. of Electrical Engineering National University of Computer & Emerging Sciences Islamabad Pakistan Dept. of Mechanical Engineering Balochistan University of Engineering & Technology Khuzdar Pakistan Dept. of Electrical Engineering CECOS University of IT & Emerging Sciences Peshawar Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is a technique used to treat motor symptoms of movement disorders including Parkinson's Disease (PD), a neurogenerative disorder caused by the progressive loss of striatal dopamine in the brain that leads to tremors, rigidity, and postural instability. Despite its therapeutic effects, DBS is still known to cause some non-motor symptoms including breathlessness due to unknown reasons in PD patients. In this paper, the changes in breathing rates were evaluated from peak detection for 20 trials in the time domain for the preliminary data obtained from a rat model after the application of DBS on the STN for frequencies 10Hz and 130Hz with stimulation intensities of 150 µA and 450 µA. Results revealed a significant change in breathing rates for all intensities and corresponding frequencies, confirming that the degree of disruption in breathing rate may depend on the stimulation frequency. The study may further suggest potential neuronal networks that mediate these changes.

关键词： Time-frequency analysis Statistical analysis Parkinson's disease Deep brain stimulation Motors Brain modeling Data models Rigidity Time-domain analysis Biological neural networks

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Self-Optimizing Distributed Cloud Computing with Dynamic Neural Resource Allocation and Fault-Tolerant Multi-Agent systems

Self-Optimizing Distributed Cloud Computing with Dynamic Neu...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： N. Tripura P. Divya Koushik Reddy Chaganti Kota Venkateswara Rao P. Rajyalakshmi P. Naresh Dept of MBA Vignan Institute of Technology And Science Hyderabad Dept. of Computer and Information Systems University of the Cumberlands KY USA Dept of CSE Andhra Loyola Institute of Engineering and Technology Vijayawada Dept of CSE TKR College of Engineering and Technology Hyderabad Dept of CSE Dayananda Sagar University Bangalore

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The exponential growth of distributed cloud systems necessitates an intelligent form of workload management that ensures optimum performance, scalability, and reliability. Traditional approaches based on static or heuristic resource allocation and fault-tolerance mechanisms cannot capture the dynamic nature of modern workloads and heterogeneous infrastructures. Drawing from this source of inspiration, this work develops a holistic framework integrating machine learning models that facilitate self-optimizing distributed cloud computing for dynamic workload management. Three components are significant in the proposed framework: (1) DNRA; this uses a hybrid GNN and RL architecture for real-time resource allocation, offering up to 25% improvement in resource utilization and a 30% reduction in task latency. (2) Workload-Aware Transfer Learning Scheduler (WATLS), leveraging transfer learning to predict workload dynamics across heterogeneous nodes, achieving 20% higher task throughput and 15% energy savings through proactive scheduling. (3) Multi-Agent Predictive Fault-Tolerant System (MAP-FTS), combining multi-agent systems with predictive fault detection to preemptively redistribute tasks and schedule maintenance, reducing system downtime by 50% and enhancing fault detection accuracy to 90%. The framework integrates novel methodologies towards answering the main challenges of resource allocation, workload prediction, and fault tolerance. The amount of progress achieved through cumulative contributions includes improvement in system reliability and scalability up to 5x their nodes and respective scalable energy efficiency without any degradation of performance. This work paves the pathway for an adaptive and intelligent next generation of distributed cloud systems.

关键词： Fault tolerance Cloud computing Event detection Scalability Fault tolerant systems Transfer learning Dynamic scheduling Ubiquitous computing Resource management Multi-agent systems

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Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models

Estimation of Soil Moisture with Meteorological Variables in...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Mariam Hussain Nusrat Sharmin Sumayea Binte Shafiul Dept. of Climate and Energy Systems Engineering Ewha Womans University Seoul South Korea Dept. of Computer Science and Engineering University of Science and Technology Chittagong Bangladesh Dept. of Epidemiology Bangladesh University of Health Sciences Dhaka Bangladesh

Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions.

关键词： Radio frequency Support vector machines Training Biological system modeling Soil moisture Urban areas Weather forecasting

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Enhancing Autism Disease Classification Using a Hybrid GA-KNN Approach for Feature Selection

Enhancing Autism Disease Classification Using a Hybrid GA-KN...

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International Conference on Information and Communication systems (ICICS)

作者： Maria Yousef Laith Al Shehab Doaa Abdel Ghani Hadeel Alazzam Mohammad Ghatasheh Department of Computer Science Middel East University (MEU) Amman Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan Department of Intelligence Systems Al-Balqa Applied University Al-Salt Jordan Dept. of Computer Information Systems Middel East University (MEU) Amman Jordan

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

These days, autism spectrum disorder (ASD) is considered to be one of the most common health problems. A person with ASD is said to have a neurodegenerative disorder that affects their ability to engage and communicate. Using screening tests to identify autism symptoms is highly costly and time-consuming. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism symptoms early on. Early detection of autism symptoms is now feasible due to advancements in AI and ML. The main contribution of this study is to address the high dimensionality problem by proposing a new hybrid feature selection approach using the genetic algorithm (GA) with K-nearest neighbor (KNN). This hybrid approach employs the GA-KNN to identify the ideal feature subset that enhances the performance of autism classification. The model uses three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) to compare their impact on classification performance and accuracy based on the proposed approach based on three ASD datasets for adults, adolescents, and children. The experimental results show that: 1) the hybrid GA-KNN approach used in the feature selection step can reduce the total number of features from 21 to 9 and increase the classifiers accuracy by 9% to 17%; 2) out of all the tested classifiers, the CNN classifier performed the best in classification, yielding the highest accuracy of 97.5%.

关键词： Support vector machines Autism Accuracy Nearest neighbor methods Feature extraction Classification algorithms Random forests

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Difference rewards policy gradients 20

Difference rewards policy gradients

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20th International Conference on Autonomous Agents and Multiagent systems, AAMAS 2021

作者： Castellini, Jacopo Devlin, Sam Oliehoek, Frans A. Savani, Rahul Dept. of Computer Science University of Liverpool Liverpool United Kingdom Microsoft Research Cambridge Cambridge United Kingdom Dept. of Intelligent Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9781713832621

Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent's contribution to the overall performance, which is crucial for learning good policies. We propose a novel algorithm called *** that explicitly tackles this by combining difference rewards with policy gradients to allow for learning decentralized policies when the reward function is known. By differencing the reward function directly, *** avoids difficulties associated with learning the Q-function as done by Counterfactual Multiagent Policy Gradients (COMA), a state-of-the-art difference rewards method. For applications where the reward function is unknown, we show the effectiveness of a version of *** that learns a reward network that is used to estimate the difference rewards. © 2021 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Reinforcement learning

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SLIPT in Joint Dimming Multi-LED OWC systems with Rate Splitting Multiple Access

SLIPT in Joint Dimming Multi-LED OWC Systems with Rate Split...

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IEEE International Conference on Communications (ICC)

作者： Sepideh Javadi Sajad Faramarzi Farshad Zeinali Hosein Zarini Mohammad Robat Mili Panagiotis D. Diamantoulakis Eduard Jorswieck George K. Karagiannidis Pasargad Institute for Advanced Innovative Solutions (PIAIS) Tehran Iran Dept. of Electrical Engineering Iran University of Science and Technology Tehran Iran Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Institute for Communications Technology Technische Universitat Braunschweig Braunschweig Germany Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) scheme that lowers the consumed power of a SLIPT-enabled OWC system by controlling the number of active LEDs. We further enhance the data rate of this system by utilizing rate splitting multiple access (RSMA). More specifically, we formulate a data rate maximization problem to optimize the beamforming design, LED selection and RSMA rate adaptation that guarantees the power budget of the OWC transmitter, as well as the quality-of-service (QoS) and an energy harvesting level for users. We propose a dynamic resource allocation solution based on proximal policy optimization (PPO) reinforcement learning. In simulations, the optimal dimming level is determined to initiate a trade-off between the data rate and power consumption. It is also verified that RSMA significantly improves the data rate.

关键词： Wireless communication Energy consumption Quality of service Reinforcement learning Light emitting diodes Dynamic scheduling Real-time systems

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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th International Conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

Distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. Processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential Processing Techniques. This paper proposes a Multi-Core Parallel Processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

An Investigation of Running Load Comparisons of ARCore on Na...

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Vocational Education and Electrical Engineering, International Conference on

作者： Komang Candra Brata Nobuo Funabiki Sritrusta Sukaridhoto Evianita Dewi Fajrianti Mustika Mentari Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Informatics Engineering Universitas Brawijaya Malang Indonesia Dept. of Informatic and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Information Technology State Polytechnic of Malang Malang Indonesia

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.

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