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UAV Smart Navigation: Combining Delaunay Triangulation and the Bat Algorithm for Enhanced Efficiency 1

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Jothi, Akshya Sundarrajan, M. Gayana, R. Kadry, Seifedine SRM Institute of Science and Technology Chennai India K. Ramakrishnan College of Engineering Tiruchirappalli India Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

In the realm of unmanned aerial vehicles (UAVs), path planning is crucial. The objective is to create a safe, practical, and optimum flight route for UAVs to fly across unfamiliar terrain while avoiding obstacles. Because of their high computational complexity and inability to handle changing surroundings, traditional route planning algorithms confront hurdles in real-world applications. As a result, academics have created many sophisticated ways to handle UAV route planning challenges. This research provides a unique technique for intelligent UAV route planning. The program first divides the research region using the Delaunay Triangulation method. After obtaining the partitions, the coverage points are located, and the issue is framed as the Traveling Salesperson Problem (TSP). The Bat Optimization Algorithm (BOA) is used to find an optimum route for the TSP. The suggested technique seeks to find the best route for UAVs in dynamic situations. The suggested approach was verified by comparing it to other existing algorithms. The findings demonstrate that the suggested method can construct an optimum route that meets all requirements while avoiding obstacles. Moreover, the method may swiftly identify a new route as the environment changes. The study findings show that the suggested approach beats the other techniques in total computing time, saving roughly 0.03 s. Moreover, the suggested technique reduces the UAV’s route by around 0.02 cm, substantially improving existing algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion planning

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Deep Generative Models for Bayesian Inference on High-Rate Sensor data: Applications in Automotive Radar and Medical Imaging

arXiv

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arXiv 2025年

作者： Stevens, Tristan S.W. Overdevest, Jeroen Nolan, Oisín van Nierop, Wessel L. van Sloun, Ruud J.G. Eldar, Yonina C. Department of Electrical Engineering University of Technology Eindhoven Netherlands Signal Processing Algorithms NXP Semiconductors Netherlands Faculty of Mathematics and Computer Science Weizmann institute of Science Israel

Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging. © The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://***/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. © 2025, CC BY-SA.

关键词： Inverse problems

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Smart Building Indoor Temperature Prediction Using the IoT and Machine Learning 2

Smart Building Indoor Temperature Prediction Using the IoT a...

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2nd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Vincent, Robin Rohit Sarkar, Swagata Irence, Shalom Prakash, R.B.R. Senthil Rama, R. Suganthi, D. School of Computer Science & Engineering and Information Science Computer Science & Engineering Presidency University Bengaluru560064 India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai 44 India EEE Karunya Kits / CBE India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India DMI College of Engineering EEE Chennai India Saveetha College of Liberal Arts and Sciences Department of Computer Science Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350397635

Due to fast expansion and improved lifestyle, the need for useable energy has skyrocketed in recent decades, notably in the construction industry. Several factors, such as local climate, building makeup, and energy consumption habits, affect a structure's efficiency in using energy. In this paper, we aim for forecasting the interior temperature of smart buildings by combining IoT with popular machine Learning algorithms. This prediction model has been developed using online learning techniques to increase its adaptability to novel inputs. This paper details a methodology for creating an analytical model that may be utilized for smart building interior temperature forecasting by merging the IoT with some popular machine learning methods. This prediction model was built with an online-learning strategy, so it may be used for a dataset with which the developers are unfamiliar. To ensure the precision of the method, this article uses Machine Learning to test it using real-world sensor data. In order to verify the methodology, the study conducts experiments using Machine Learning on collected, real-world sensor data. The study then proposes incorporating the following method into an Edge Computing based IoT architecture in order to make the building operational in an energy-efficient manner. The learning framework was validated with data collected from an Internet of Things network installed in a home over the course of a summertime. The learnt model's efficacy has been evaluated, and the model's predictability of interior temperature changes has been quantified. The paper demonstrates the viability of the proposed Learning framework for thermal models based on the Internet of Things and provides a pragmatic answer to the problem of supplying accurate thermal models for use in the temperature controls of smart buildings of the future. © 2023 IEEE.

关键词： Energy utilization

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Using Long Short-Term Memory Units for Time Series Forecasting 2

Using Long Short-Term Memory Units for Time Series Forecasti...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Yadav, Dhyan Sahoo, Laxman Mandal, Sanjeev Kumar Ravivarman, G. Vijayaraghavan, P. Prasad, B. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350308846

This paper specializes in using long short-time period memory (LSTM) networks for time collection forecasting. LSTM is a form of artificial recurrent neural network (RNN) that is especially properly-applicable to analyze from enjoy over long sequences of statistics. Not like conventional recurrent networks, LSTM networks can technique input sequences in each instruction, while also allowing for the protection of lengthy-time period dependencies and rapid convergence. It's far shown that these characteristics of LSTM networks allow them to be used efficaciously for forecasting time series consisting of inventory performance and electricity tendencies. Strategies inclusive of feeding the LSTM network data in a time step layout, the use of peephole connections, stacking a couple of layers and early stopping are discussed. Results are then presented and mentioned, demonstrating that LSTM networks can research complex lengthy-time period dependencies in time series facts and correctly forecast destiny values. © 2023 IEEE.

关键词： Complex Dependencies Destiny Research Values

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture 9

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Saranya, V.S. Mangali, Saikiran Srinija, K. Medabalimi, Galeiah Devi, Meena Venkata Ramana, N.R. Lakkshmanan, Ajanthaa School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603203 India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Mlrit Department: Data science Dundigal India Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Chinna Amiram Andhra Pradesh Bhimavaram India Kcg college of technology Department of electrical and electronics engineering Chennai600097 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350377972

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Subject-Independent EEG-Based Auditory Attention Decoding with WGAN and Pearson Correlation Coefficient

Enhancing Subject-Independent EEG-Based Auditory Attention D...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Pahuja, Saurav Ivucic, Gabriel Putze, Felix Cai, Siqi Li, Haizhou Schultz, Tanja University of Bremen Germany The Department of Electrical and Computer Engineering National University of Singapore Singapore University of Bremen Germany Csl and Hosts Mll The Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Kriston Ai Xiamen China

ISBN: (纸本)9798350337020

Electroencephalography (EEG) related research faces a significant challenge of subject independence due to the variation in brain signals and responses among individuals. While deep learning models hold promise in addressing this challenge, their effectiveness depends on large datasets for training and generalization across participants. To overcome this limitation, we propose a solution to the above limitation by increasing the size and quality of training data for subject-independent auditory attention decoding (AAD) using EEG with deep learning. Specifically, our method employs a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic data, with Pearson correlation filtering the most realistic samples. We evaluated this method on a publicly available dataset of selective auditory attention experiments and showed superior performance in subject-independent AAD performance. The mixed training set, consisting of both real and artificial data generated by the WGAN+Pearson Correlation Coefficient, demonstrated approximately 4% improvement in AAD accuracy for a 1-second window. These results demonstrate that deep learning remains a viable approach to overcoming data scarcity in subject-independent AAD tasks based on EEG. Moreover, the proposed method has the potential to improve the generalization and reliability of EEG classification tasks. © 2023 IEEE.

关键词： Auditory attention decoding data scarcity EEG Generative adversarial network Pearson Correlation Coefficient

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LEARN: AN INVEX LOSS FOR OUTLIER OBLIVIOUS ROBUST ONLINE OPTIMIZATION

arXiv

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arXiv 2024年

作者： Barik, Adarsh Krishna, Anand Tan, Vincent Y.F. Institute of Data Science National University of Singapore 117602 Singapore Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

We study a robust online convex optimization framework, where an adversary can introduce outliers by corrupting loss functions in an arbitrary number of rounds k, unknown to the learner. Our focus is on a novel setting allowing unbounded domains and large gradients for the losses without relying on a Lipschitz assumption. We introduce the Log Exponential Adjusted Robust and iNvex (LEARN) loss, a non-convex (invex) robust loss function to mitigate the effects of outliers and develop a robust variant of the online gradient descent algorithm by leveraging the LEARN loss. We establish tight regret guarantees (up to constants), in a dynamic setting, with respect to the uncorrupted rounds and conduct experiments to validate our theory. Furthermore, we present a unified analysis framework for developing online optimization algorithms for non-convex (invex) losses, utilizing it to provide regret bounds with respect to the LEARN loss, which may be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistics

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Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and Novel Controller Framework to Meet Real-World Innovations

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Syambabu, V. Kalaiselvi, K. Mahil, S. Derisha Hemalatha, S. Alekhya, Badi Kanagavalli, N. Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Aiml Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors. © 2024 IEEE.

关键词： Cameras

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Pairwise alignment improves graph domain adaptation 24

Pairwise alignment improves graph domain adaptation

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Proceedings of the 41st International Conference on Machine Learning

作者： Shikun Liu Deyu Zou Han Zhao Pan Li Department of Electrical and Computer Engineering Georgia Institute of Technology Georgia School of Data Science University of Science and Technology of China Hefei China Department of Computer Science University of Illinois Urbana- Champaign Champaign

Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This work delves into Graph Domain Adaptation (GDA) to address the unique complexities of distribution shifts over graph data, where interconnected data points experience shifts in features, labels, and in particular, connecting patterns. We propose a novel, theoretically principled method, Pairwise Alignment (Pair-Align) to counter graph structure shift by mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align uses edge weights to recalibrate the influence among neighboring nodes to handle CSS and adjusts the classification loss with label weights to handle LS. Our method demonstrates superior performance in real-world applications, including node classification with region shift in social networks, and the pileup mitigation task in particle colliding experiments. For the first application, we also curate the largest dataset by far for GDA studies. Our method shows strong performance in synthetic and other existing benchmark datasets.

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