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arXiv 2024年

作者： Saraogi, Divita Aditya, J. Venkata Bhalerao, Varun Bala, Suman Balasubramanian, Arvind Mate, Sujay Chattopadhyay, Tanmoy Gupta, Soumya Prasad, Vipul Waratkar, Gaurav Navaneeth, P.K. Gopalakrishnan, Rahul Bhattacharya, Dipankar Dewangan, Gulab Vadawale, Santosh Department of Physics Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India Science and Technology Institute Universities Space Research Association HuntsvilleAL35805 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Maharashtra Mumbai400005 India Kavli Institute of Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Homi Bhabha National Institute Anushakti Nagar Maharashtra Mumbai400094 India Ashoka University Department of Physics Haryana Sonepat131029 India Inter University Centre for Astronomy and Astrophysics Maharashtra Pune411007 India Physical Research Laboratory Gujarat Ahmedabad380009 India

The Cadmium Zinc Telluride Imager (CZTI) aboard AstroSat has good sensitivity to Gamma Ray Bursts (GRBs), with close to 600 detections including about 50 discoveries undetected by other missions. However, CZTI was not designed to be a GRB monitor and lacks localisation capabilities. We introduce a new method of localising GRBs using ‘shadows’ cast on the CZTI detector plane due to absorption and scattering by satellite components and instruments. Comparing the observed distribution of counts on the detector plane with simulated distributions with the AstroSat Mass Model, we can localise GRBs in the sky. Our localisation uncertainty is defined by a two-component model, with a narrow Gaussian component that has close to 50% probability of containing the source, and the remaining spread over a broader Gaussian component with an 11.3 times higher σ. The width (σ) of the Gaussian components scales inversely with source counts. We test this model by applying the method to GRBs with known positions and find good agreement between the model and observations. This new ability expands the utility of CZTI in the study of GRBs and other rapid high-energy transients. Copyright © 2024, The Authors. All rights reserved.

关键词： Cadmium telluride

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Ab Initio Study of (100) Diamond Surface Spins

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Physical Review Applied 2023年第1期20卷 014040-014040页

作者： Jyh-Pin Chou Péter Udvarhelyi Nathalie P. de Leon Adam Gali Department of Physics National Changhua University of Education No. 1 Jin-De Road Changhua City Changhua Taiwan Institute for Solid State Physics and Optics Wigner Research Centre for Physics Budapest POB 49 H-1525 Hungary Department of Atomic Physics Institute of Physics Budapest University of Technology and Economics Műegyetem rakpart 3. Budapest H-1111 Hungary Department of Electrical and Computer Engineering Princeton University Engineering Quadrangle Olden Street Princeton New Jersey 08544 United States

Unpaired electronic spins at diamond surfaces are ubiquitous and can lead to excess magnetic noise. They have been observed in several studies to date, but their exact chemical nature is still unknown. We propose a simple model to explain the existence and chemical stability of surface spins associated with the sp3 dangling bond on the (100) diamond surface using density-functional theory. We find that the (111) facet, which is naturally generated at a step edge of (100) crystalline diamond surface, can sterically protect a spinful defect. Our study reveals a mechanism for annihilation of these surface spins upon annealing, consistent with recent experimental results. We also demonstrate that the Fermi-contact term in the hyperfine coupling is not negligible between the surface spins and the surrounding nuclear spins, and thus ab initio simulation can be used to devise a sensing protocol where the surface spins act as reporter spins to sense nuclear spins on the surface.

关键词： Magnetic coupling Quantum information with solid state qubits Surface states Nitrogen vacancy centers in diamond Surfaces Spin Density functional calculations

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Exploring the Dynamical Nature and Radio Halo Emission of Abell 384: A Combined Radio, X-ray and Optical Study

arXiv

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arXiv 2024年

作者： Chatterjee, Swarna Pillay, Denisha Datta, Abhirup Raja, Ramij Knowles, Kenda Rahaman, Majidul Sikhosana, S.P. Department of Astronomy Astrophysics and Space Engineering Indian Institute of Technology Indore M.P. Indore India Centre for Radio Astronomy Techniques and Technologies Department of Physics and Electronics Rhodes University Makhanda6139 South Africa Astrophysics Research Centre University of KwaZulu-Natal South Africa South African Radio Astronomy Observatory Liesbeek House Settlers Way Mowbray Cape Town7705 South Africa Institute of Astronomy National Tsing Hua University Hsinchu300044 Taiwan School of Mathematics Statistics and Computer Science University of KwaZulu-Natal South Africa

Multiwavelength studies of galaxy clusters are crucial for understanding the complex interconnection of the thermal and non-thermal constituents of these massive structures and uncovering the physical processes involved in their formation and evolution. Here, we report a multiwavelength assessment of the galaxy cluster A384, which was previously reported to host a radio halo with a 660 kpc size at MeerKAT 1.28 GHz. The halo is slightly offset from the cluster centre. Our uGMRT observation reveals that the halo extends up to 690 kpc at 407 MHz with a nonuniform spectral index α4071284MHzMHz distribution varying from flat (-0.5) to steep (-1.3) values. In addition, we use legacy GMRT 608 MHz, XMM-Newton X-ray, and the Dark Energy Survey optical observations to obtain an extensive understanding of the dynamical nature of the galaxy cluster. The X-ray surface brightness concentration parameter (CSB = 0.16) and centroid shift (w = 0.057) reveal an ongoing dynamical disturbance in the cluster. This is also supported by the elongated 2-D optical galaxy density distribution map of the cluster. The obtained centre shift between optical and X-ray peaks and the asymmetry parameter from optical analysis further supports the dynamical disturbance in the cluster. The radio and X-ray surface brightness follows a sub-linear correlation. Our observations suggest that the cluster is currently in a merging state where particle re-acceleration in the turbulent ICM resulted in the radio halo emission. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark energy

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An Intelligent Age of Information Based Self-Energized Uav-Assisted Wireless Communication System

SSRN

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SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Muthuchidambaranathan, P. Ranchagodage Don, Sanjaya Arunapriya National Institute of Technology Tiruchirappalli India Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli620015 India Senior Member Technical Staff Baseband Algorithm Development Team Saankhya Labs Private Limited Bengaluru560001 India Centro de Investigaçä em Tecnologias - Autónoma TechLab Universidade Autónoma de Lisboa Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka10500 Sri Lanka

Internet-of-things (IoT) is an enabling technology in the fourth generation industrial revolution. An unified performance metric named age of information (AoI) is introduced to quantify the freshness of data and its applications. Thus, this paper study the combination of AoI and full-duplex enabled unmanned aerial vehicle (FD-UAV) as a promising solution to improve latency and spectrum efficiency of a wireless powered sensor network. A sensor node (SN), located ina transport infrastructure, harvests energy from radio frequency signals transmitted by the FD-UAV. This uses to transmit real time sensor observations to the data sink via FD-UAV. The SN generates an update after replenished the battery and transmits by using the harvested energy. A closed-form expression for average AoI (AAoI) is derived as a function of time allocated for energy harvesting (EH). The optimal time allocation for EH that maximizes the freshness of data update is identified. Deep learning algorithms such as Long-Short Term Memory (LSTM) is used to predict the AAoI. Simulation results demonstrate the usefulness performance bounds in-terms of freshness of data updates. © 2022, The Authors. All rights reserved.

关键词： Sensor nodes

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parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark

arXiv

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arXiv 2022年

作者： Afshar, Parnian Mohammadi, Arash Plataniotis, Konstantinos N. Farahani, Keyvan Kirby, Justin Oikonomou, Anastasia Asif, Amir Wee, Leonard Dekker, Andre Wu, Xin Haque, Mohammad Ariful Hossain, Shahruk Hasan, Md. Kamrul Kamal, Uday Hsu, Winston Lin, Jhih-Yuan Rahman, M. Sohel Ibtehaz, Nabil Foisol, Sh.M. Amir Lam, Kin-Man Guang, Zhong Zhang, Runze Channappayya, Sumohana S. Gupta, Shashank Dev, Chander The Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada The Department of Electrical and Computer Engineering University of Toronto Toronto Canada RockvilleMD United States The Frederick National Laboratory for Cancer Research FrederickMD United States The Department of Medical Imaging Sunnybrook Health Sciences Centre University of Toronto Toronto Canada The Electrical and Computer Engineering Department Concordia University Montreal Canada The Department of Radiation Oncology MAASTRO Clinic Maastricht Netherlands Maastricht University Maastricht Netherlands The Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh The Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan The Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong India

Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability, and is also time-consuming, considering the fact that only experts are capable of providing annotations. Automatic and semi-automatic tumor segmentation methods have recently shown promising results. However, as different researchers have validated their algorithms using various datasets and performance metrics, reliably evaluating these methods is still an open challenge. The goal of the Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark created through 2018 IEEE Video and Image Processing (VIP) Cup competition, is to provide a unique dataset and pre-defined metrics, so that different researchers can develop and evaluate their methods in a unified fashion. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. At the registration stage, there were 129 members clustered into 28 teams from 10 countries, out of which 9 teams made it to the final stage and 6 teams successfully completed all the required tasks. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique. Methods developed by the three finalists show promising results in tumor segmentation, however, more effort should be put into reducing the false positive rate. This competition manuscript presents an overview of the VIP-Cup challenge, along with the proposed algorithms and results. © 2022, CC BY.

关键词： computerized tomography

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Joint Visibility Region and Channel Estimation for Extremely Large-Scale MIMO Systems

arXiv

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arXiv 2023年

作者： Tang, Anzheng Wang, Jun-Bo Pan, Yijin Zhang, Wence Zhang, Xiaodan Chen, Yijian Yu, Hongkang de Lamare, Rodrigo C. The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Computer Science and Communication Engineering the Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The School of Management Shenzhen Institute of Information Technology Shenzhen518172 China The Wireless Product Research and Development Institute ZTE Corporation Shenzhen518057 China The Centre for Telecommunications Studies Pontifical Catholic University of Rio de Janeiro Rio de Janeiro22451-900 Brazil The Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Visibility

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Frequency-splitting SWIPT with joint signal detection and nonlinear distortion compensation

Frequency-splitting SWIPT with joint signal detection and no...

引用

2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Rajaram, Akashkumar Dinis, Rui Guerreiro, Joao Jayakody, Dushantha Nalin K. Beko, Marko NOVA School of Science and Technology NOVA University Lisbon Caparica Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka Sri Lanka Instituto de Telecomunicações Instituto Superior t'Ecnico Universidade de Lisboa Lisbon Portugal COPELABS Universidade Lusófona de Humanidades e Tecnologias Lisbon Portugal

ISBN: (纸本)9781665412629

Frequency splitting based simultaneous wireless information and power transmission (FS-SWIPT) is a novel SWIPT technique that can be used with orthogonal frequency division multiplexing (OFDM) or with other frequency division multiplexing techniques. In this work, we implement FS-SWIPT with OFDM signals and adopt separate subcarriers for data and energy carrying symbols. The main advantage of FS-SWIPT is that the data symbols do not suffer energy loss. However, the amplification of high power symbols can lead to nonlinear distortion and considerable performance degradation. To tackle the nonlinear distortion effects, we propose a joint signal detection and distortion compensation scheme. This compensation technique takes advantage of the known energy symbols and improves the performance comparatively with the case where energy symbols are not transmitted. We validate the analytical results of the nonlinear distortion effects and analyse the improvements in error rate performance of FS-SWIPT by using Monte Carlo simulations. © 2021 IEEE.

关键词： Intelligent systems

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parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data

Journal of Geophysical Research: Machine Learning and Comput...

引用

Journal of Geophysical research: Machine Learning and Computation 2025年第2期2卷

作者： Sandeepan Dhoundiyal Moni Shankar Dey Shashikant Singh Pattathal V. Arun Guneshwar Thangjam Alok Porwal Centre of Studies in Resources Engineering Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Group Indian Institute of Information Technology Sri City Sri City India School Earth & Planetary Sciences National Institute of Science Education and Research Bhubhaneshwar India

This paper addresses some of the challenges in automating mineral mapping from CRISM hyperspectral data using two ML algorithms, namely, Random Forest (RF) and Gradient Boosted Trees (GBTree) algorithms. An interpretable framework using tree-ensemble classification and SHapely Additive exPlanations (SHAP) is implemented to interpret the model decisions. SHAP explanations quantify the influence of diagnostic absorption features, demonstrating that classifiers rely on physically significant spectral features, rather than artifacts. Novel metrics: “Physically Significant Precision,” “Physically Significant Recall,” and “Physically Significant F-measure” quantify the classifier's expected performance on unseen data. RF outperforms GBTree, and thus is used to develop a novel framework for mineral mapping from CRISM data, demonstrated on five CRISM datacubes. This amalgamation of Random Forest and SHAP addresses limitations associated with existing CRISM classification methods, offering stability during training, reduced manual intervention, and interpretability while achieving a Kappa of 0.91 over the CRISM Machine Learning Toolkit's mineral data set with 470,000 labeled spectra. The effectiveness of the Random Forest and gradient-boosted tree classifiers for identifying minerals from hyperspectral data is evaluated A novel metric, based on SHapely Additive exPlanations, to estimate a trained classifier's expected performance on unseen data is introduced A novel explainable framework for mapping minerals from CRISM hyperspectral data is introduced This paper describes a method for automating mineral detection from CRISM hyperspectral data, comparing Random Forest and Gradient Boosted Trees algorithms. It presents a method using tree-ensemble algorithms for classification and SHapely Additive exPlanations (SHAP) for interpretability. SHAP explanations show classifiers prioritize physically significant spectral features over artifacts. Novel metrics: “Physically Significan

关键词： hyperspectral remote sensing CRISM mineral mapping Mars Explainbale AI

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