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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

Vehicular Communications 2025年 54卷

作者： Akshya, J. Sundarrajan, M. Amutha, S. Dhanaraj, Rajesh Kumar Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely RRT*, A*, and Dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than RRT*, A*, and Dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning. © 2025 The Author(s)

关键词： computational Geometry Convex Optimisation Delaunay Triangulations Spatial Coverage UAV Trajectory Voronoi Diagrams

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Quantum Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Cetinkaya, Oktay Marrone, Stefano Claudino, Daniel Haunschild, David Schlote, Leon Wu, Huaming Ottaviani, Carlo Liu, Xiaoyuan Machupalli, Sree Pragna Kaur, Kamalpreet Arora, Priyansh Liu, Ji Farouk, Ahmed Song, Houbing Herbert Uhlig, Steve Ramamohanarao, Kotagiri School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Dipartimento di Matematica e Fisica Università della Campania "Luigi Vanvitelli" Italy Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN United States Detecon International GmbH Munich Germany DB Cargo Berlin Germany Center for Applied Mathematics Tianjin University Tianjin China Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Fujitsu Research of America Inc. Santa ClaraCA United States Information Networking Institute Carnegie Mellon University United States Cymax Group Technologies BC Canada Microsoft Schiphol Netherlands Mathematics and Computational Research Division Argonne National Laboratory IL United States Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Baltimore United States The University of Melbourne VIC Australia

The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved otherwise with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable progress in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Deep Neural Network Based Hierarchical Control of Residential Microgrid Using LSTM

Deep Neural Network Based Hierarchical Control of Residentia...

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IEEE Region 10 International Conference TENCON

作者： Anu G Kumar M.R. Sindhu Sachin S Kumar Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Microgrid is a hot topic for the present research as their role is significant in framing reliable and efficient power system. Major sources in a typical microgrid are renewable resources like solar, wind etc. Their intermittency and uncertainty in the load demands makes the control and smooth operation of microgrid challenging. This paper presents a Long Short Term Memory (LSTM) network for hierarchical control of a residential microgrid. This multi input multi output LSTM regression architecture is used to predict the optimal real time control action for the microgrid. The performance of the LSTM model is evaluated using root mean square error (RMSE), SMAPE, MRE, MAE, RMSE and loss function. Its performance is compared with other prominent techniques also.

关键词： Microgrids Batteries Logic gates Computer architecture Training Microprocessors Mathematical model

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Modeling of a System for fECG Extraction from abdECG 17th

Modeling of a System for fECG Extraction from abdECG

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17th International Conference on Intelligent Systems Design and Applications, ISDA 2017

作者： John, Rolant Gini Deepan Chakravarthy, Ponmozhy Ramachandran, K.I. Anand, Pooja Department of Electronics and Communication Engineering Amrita University Coimbatore India Center for Computational Engineering and Networking Amrita University Coimbatore India

ISBN: (纸本)9783319763477

The objective of this paper is to move a step ahead in investigation and create a feasible, cost effective fetal ECG analysis tool for clinical practice which will be easy for usage by any non-skilled personal and provide actionable medical information such as the QRS complex of fetal ECG, fetal HR etc. In this method, a composite abdominal ECG is subjected to a pre-processing stage which involves filtering and normalization, then fed into the ‘thresholding and peak finding’ stage to detect the maternal ECG peaks. The next stage involves construction of the MLE of maternal ECG embedded in the abdominal ECG. After this, the constructed MLE which represent the maternal ECG is subtracted from the abdominal ECG to obtain fetal ECG along with a smidgen of noise. This noise which adulterates the fetal ECG is removed by filtering, done at the post processing stage. Thresholding and peak finding is done at the post processed signal to calculate the fetal HR. This paper puts forth a promising possibility of implementing the proposed algorithm in any suitable hardware model, since an average Accuracy of 76.8% and average Sensitivity of 90.7% is attained. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Electrocardiograms

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Value of ophthalmological psychophysical test and MEG in subjects at high risk for sporadic Alzheimer's disease

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ACTA OPHTHALMOLOGICA 2022年第S275期100卷

作者： Lopez-Cuenca, Ines Salobrar-Garcia, Elena Sanchez-Puebla, Lidia de Hoz, Rosa Nebreda, Alberto Garcia-Colomo, Alejandra Bruna, Ricardo Ramirez-Torano, Federico Barabash, Ana Ramon Castroviejo Institute of Ophthalmologic Research. Group UCM 920105 Health Research Institute of the Hospital Clínico San Carlos (IdISSC) Complutense University of Madrid Madrid Spain Department of Immunology Ophthalmology and ENT Faculty of Optics and Optometry Complutense University of Madrid Madrid Spain Laboratory of Cognitive and Computational Neuroscience Center for Biomedical Technology Technical University of Madrid Madrid Spain Department of Experimental Psychology Complutense University of Madrid Madrid Spain Department of Industrial Engineering & IUNE University of La Laguna Spain Department of Endocrinology and Nutrition IdISSC Madrid Spain Diabetes and Associated Metabolic Diseases Networking Biomedical Research Centre Carlos III Health Institute Madrid Spain Department of Medicine II School of Medicine Complutense University of Madrid Madrid Spain Memory Unit Department of Geriatrics Hospital Clínico San Carlos Madrid Spain Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine Madrid Spain Department of Immunology Ophthalmology and ENT School of Medicine Complutense University of Madrid Madrid Spain

Purpose: The aim of this work was to analyse in subjects at high risk of developing Alzheimer's disease (AD) the possible correlation between psychophysical tests and magnetoencephalography (MEG) in comparison with control subjects. Methods: Extensive ophthalmological analysis, MEG recording, genotyping and magnetic resonance imaging (MRI) were performed on 149 healthy subjects, of whom 109 had a family history of the disease and 40 did not. This sample was subdivided into subgroups aged 40–60 years with an increased risk of AD based on the presence of the ApoEɛ4 allele (ApoEɛ4+) and family history (FH+). 28 participants as higher risk group (ApoEɛ4+ and FH+) and 16 participants as lower risk group (FH- and ApoEɛ4-). Results: In the ApoEɛ4+ and FH+ group, a significant increase in visual acuity was observed, as well as an increase in some spatial frequencies of contrast sensitivity. In addition, in this group, some areas of the inner plexiform layer and the inner nuclear layer were thinned. Between the risk groups, when directly comparing M100 latency or temporal frequency activity associated with the visual response, no significant differences were found. However, there was an association between higher visual acuity and earlier M100 latency and increased activity in the high-risk groups. Conclusions: There seems to exist an early hyperactivation of the visual system, which would act as a compensatory mechanism, or be a manifestation of a biological malfunction that ultimately leads to toxicity. This hyperactivation represents the first evidence of the consequences of early retinal damage, associated with genetic risk, on visual processing. These changes observed by readily available and non-invasive techniques could constitute a set of biomarkers for early detection of the disease and assessment of the efficacy of possible future treatments.

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A Digital Library at Zero Cost Through a Satellite 7

A Digital Library at Zero Cost Through a Satellite

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7th IEEE International Conference on Communication and Signal Processing, ICCSP 2018

作者： Gandhiraj, R Vinutna, T Sai Bharath, Kumar B Venu Sri, Sushma K Soman, P.K. Department of ECE Amrita University Coimbatore India Amrita University Center for Excellence in Computational Engineering and Networking Coimbatore India

ISBN: (纸本)9781538635216

The Government always worked towards providing better education to all the children in the country. In this, they have always tried to arrive at cost efficient solutions to provide education to even the poorest. The current proposed methodology provides a cost effective solution for providing education through a satellite source called Outernet. This system will enable receiving of digital library content for free of cost. A cost analysis is made to compare the cost of the current education system and how Outernet makes the system cheaper. © 2018 IEEE.

关键词： Digital libraries

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Longitudinal Modeling of Depression Shifts Using Speech and Language

Longitudinal Modeling of Depression Shifts Using Speech and ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Paula Andrea Pérez-Toro Judith Dineley Agnieszka Kaczkowska Pauline Conde Yuezhou Zhang Faith Matcham Sara Siddi Josep Maria Haro Stuart Bruce Til Wykes Raquel Bailón Srinivasan Vairavan Richard J.B. Dobson Andreas Maier Elmar Nöth Juan Rafael Orozco-Arroyave Vaibhav A. Narayan Nicholas Cummins Pattern Recognition Lab. Friedrich-Alexander Universitat Erlangen Germany GITA Lab Facultad de Ingeniería Universidad de Antioquia Medellín Colombia Institute of Psychiatry Psychology and Neuroscience King’s College London London UK School of Psychology University of Sussex Falmer UK Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu CIBERSAM Barcelona Spain RADAR-CNS Patient Advisory Board King’s College London UK NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London London UK Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group Aragon Institute for Engineering Research University of Zaragoza Zaragoza Spain Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN) Spain Janssen Research and Development LLC Titusville NJ United States Institute of Health Informatics University College London London UK Davos Alzheimer’s Collaborative Thymia London UK

Speech analysis can provide a potential non-invasive and objective means of assessing and monitoring an individual’s mental health. Most studies to date have focused on cross-sectional analysis and have not explored the benefits of speech analysis as a longitudinal monitoring tool that can assist in the management of chronic conditions such as major depressive disorder (MDD). Objectively monitoring for shifts in depression symptom severity levels over time presents a notable challenge, which we address through an automated approach using longitudinal English and Spanish speech samples collected from a clinical population. We employ time–frequency representations and linguistic embeddings to enhance the early recognition of alterations in depression levels in individuals with MDD. We investigate the suitability of using siamese-based training for modeling these changes, intending to enable personalized and adaptive interventions.

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Robust waveform design and analysis of active radar for the detection of target 2

Robust waveform design and analysis of active radar for the ...

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2nd International Conference on Micro-Electronics and Telecommunication engineering, ICMETE 2018

作者： Muthukumar, S.P. Gandhiraj, R. Sundaram, G. Shanmuga Department of Remote sensing and wireless sensor networks Center for Excellence in Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781538669181

This paper illustrates how, the evaluation of robust properties for transmitted waveform by worst case detection analysis and performance for target detection in the existence of clutter and noise. Here, Stepped FM waveform is considered for waveform design and analysis. Its design and analysis can be achieved through simulink blocks and these blocks contains various numerical parameters through which the waveform can be generated. Evaluation of the robustness of the waveform can be determined using Waveform Co-variance Matrices(WCM) and Interference Covariance Matrix(ICM) over a uncertainty region. Derivation of this , will technically intimate the property of robustness for the generated waveform structure. These techniques involves certain parameters such as SINR(Signal to Noise Ratio), probability of detection and transmitted energy to address the optimal evaluation of receiver operating characteristics by representing the curves. The waveform's robustivity can be observed through these curves. Based on this, the Constant false alarm rate(CFAR) can be determined, which is the application of sequential target detection and estimation. © 2018 IEEE.

关键词： Signal to noise ratio

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Deep Learning Framework and Visualization for Malware Classification

Deep Learning Framework and Visualization for Malware Classi...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： S. Akarsh K. Simran Prabaharan Poornachandran Vijay Krishna Menon K.P. Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Center for Cyber Security Systems and Networks Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kerala India

In this paper we propose a deep learning framework for classification of malware. There has been an enormous increase in the volume of malware generated lately which represents a genuine security danger to organizations and people. So as to battle the expansion of malwares, new strategies are needed to quickly identify and classify malware. Malimg dataset, a publicly available benchmark data set was used for the experimentation. The architecture used in this work is a hybrid cost-sensitive network of one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network which obtained an accuracy of 94.4%, an increase in performance compared to work done by [1] which got 84.9%. Hyper parameter tuning is done on deep learning architecture to set the parameters. A learning rate of 0.01 was taken for all experiments. Train-test split of 70-30% was done during experimentation. This facilitates to find how well the models perform on imbalanced data sets. Usual methods like disassembly, decompiling, de-obfuscation or execution of the binary need not be done in this proposed method. The source code and the trained models are made publicly available for further research.

关键词： Malware Feature extraction Deep learning Computer architecture Logic gates Convolution

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Modified Vgg Deep Learning Architecture For Covid-19 Classification Using Bio-Medical Images

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IOP Conference Series: Materials Science and engineering 2021年第1期1084卷

作者： R ANAND V SOWMYA VIJAYKRISHNAMENON E.A. GOPALAKRISHNAN K.P. SOMAN Research Scholar Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India and Assistant Professor Research Member in Sona SIPRO Department of ECE Sona College of Technology Salem Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

The world encountered a deadly disease by the beginning of 2020, known as the coronavirus disease (COVID-19). Among the different screening techniques available for COVID-19, chest radiography is an efficient method for disease detection. Whereas other disease detection techniques are time consuming, radiography requires less time to identify abnormalities caused by the disease in the lungs. In this study, one of the standard deep learning architectures, VGGNet, is modified for classifying chest X-ray images under four categories. The planned model uses images of four classes, namely COVID, bacterial, normal, and viral images. The performance matrices of the planned model are compared with five deep learning architectures, namely VGGNet, AlexNET, GoogLeNET, Inception-v4, and DenseNet-201.

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