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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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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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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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Fabrication of Paper Microfluidics POCT Device for the Colorimetric Assay of Alkaline Phosphatase 15

Fabrication of Paper Microfluidics POCT Device for the Color...

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15th IEEE India Council International Conference, INDICON 2018

作者： Resmi, P.E. Stanley, John Kumar, Sachin Soman, K.P. Ramachandran, T. Satheesh Babu, T.G. Amrita Vishwa Vidyapeetham Amrita Biosensor Research Lab Amrita School of Engineering Coimbatore India Amrita Vishwa Vidyapeetham Center for Excellence in Computational Engineering and Networking Amrita School of Engineering Coimbatore India Department of Sciences Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781538682357

Alkaline phosphatase (ALP) serves as an important biomarker for the diagnosis of various diseases including liver malfunction, bone diseases, breast and prostate cancers, and diabetes. This paper discusses the fabrication and testing of a paper-based Point-of-care testing (POCT) device for the colorimetric detection of alkaline phosphatase (ALP). The device was fabricated using a commercial wax printer followed by reagent casting on the patterned paper. In the presence of ALP concentration of 30 - 500 U/L, the reagent paranitrophenylphosphate (PNPP) changes to yellow color with increasing intensity. The different color intensities observed for different ALP concentrations were captured and a color band was developed for the visual interpretation. The RGB and HSV of the captured color intensities were processed using MATLAB to obtain the corresponding ALP concentration. © 2018 IEEE.

关键词： Fabrication

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A Digital Library at Zero Cost Through a Satellite

A Digital Library at Zero Cost Through a Satellite

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International Conference on Communications and Signal Processing

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

关键词： Tuners Receivers Broadband antennas Satellite broadcasting Libraries Education Government

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Fabrication of Paper Microfluidics POCT Device for the Colorimetric Assay of Alkaline Phosphatase

Fabrication of Paper Microfluidics POCT Device for the Color...

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IEEE India Conference (INDICON)

作者： Resmi P.E. John Stanley Sachin Kumar K.P. Soman Ramachandran T. Satheesh Babu T.G. Amrita Biosensor Research Lab Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Center for Excellence in Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Sciences Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781538682364

关键词： Paper-based POCT devices Para-nitrophenyl phosphate Alkaline phosphatase RGB and HSV values Alkaline Phosphatase fabrication Color COLOR STRENGTH Prostate Cancer Microfluidics HSV Simplexvirus

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Variational mode feature-based hyperspectral image classification

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Advances in Intelligent Systems and Computing 2016年 380卷 365-373页

作者： Nechikkat, Nikitha Sowmya, V. Soman, K.P. Center for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham CoimbatoreTamil Nadu India

ISBN: (纸本)9788132225225

Hyperspectral image analysis is considered as a promising technology in the field of remote sensing over the past decade. There are various processing and analysis techniques developed that interpret and extract the maximum information from high-dimensional hyperspectral datasets. The processing techniques significantly improve the performance of standard algorithms. This paper uses variational mode decomposition (VMD) as the processing algorithm for hyperspectral data scenarios followed by classification based on sparse representation. Variational Mode Decomposition decomposes the experimental data set into few different modes of separate spectral bands, which are unknown. These modes are given as raw input to the classifier for performance analysis. Orthogonal matching pursuit (OMP), the sparsity-based algorithm is used for classification. The proposed work is experimented on the standard dataset, namely Indian pines collected by the airborne visible/infrared imaging spectrometer (AVIRIS). The classification accuracy obtained on the hyperspectral data before and after applying Variational Mode Decomposition was analyzed. The experimental result shows that the proposed work leads to an improvement in the overall accuracy from 84.82 to 89.78%, average accuracy from 85.03 to 89.53% while using 40% data pixels for training. © Springer India 2016.

关键词： Variational mode decomposition

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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AMRITA_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders 9

AMRITA_CEN@SemEval-2015: Paraphrase Detection for Twitter us...

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Sundaram, Mahalakshmi Shanmuga Madasamy, Anand Kumar Padannayil, Soman Kotti Center for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781941643402

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Semantic Similarity in Twitter. Our paraphrase detection system makes use of phrase-structure parse tree embeddings that are then provided as input to a conventional supervised classification model. We achieve an F1 score of 0.45 on paraphrase identification and a Pearson correlation of 0.303 on computing semantic similarity. © 2015 Association for computational Linguistics

关键词： Semantics

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A computational framework for Tamil document classification using Random Kitchen Sink

A computational framework for Tamil document classification ...

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International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015

作者： Sanjanasri, J.P. Anand Kumar, M. Center for Excellence in Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781479987917

Along the prompt growth in World Wide Web, the availability and accessibility of regional language contents such as e-books, web pages, e-mails, and digital repositories has grown exponentially. As a result, the automatic document classification has become the hotspot for fetching information among the millions of web documents. The idea of classifying the text, forms the baseline for many NLP applications such as information extraction, query response, information summarization, etc. The main objective of this paper is to develop an computational framework for supervised Tamil document classification task. This paper highlights the performance of Random Kitchen Sink, a randomization algorithm, in Grand Unified Regularized Least Squares (GURLS), a Machine Learning Library, is proven to be comparably better than the conventional kernel based classifier in terms of accuracy. Henceforth, we claim that Random Kitchen Sink can be an effective alternative to the kernels for a classifier. © 2015 IEEE.

关键词： Support vector machines

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