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Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs

Transactions on Emerging Telecommunications Technologies 2025年第6期36卷

作者： Aadam Quraishi Rakeshnag Dasari Sushilkumar Dangiya Sateesh Kumar Nallamala Krishna Kanth Kondapaka Swaroop Reddy Gayam Isa Bayhan Uguloy Berdieva Rubal Jeet M.D. Research Cognitive and AI laboratory Interventional treatment institute Houston Texas USA Department of CSE Acharya Nagarjuna University Nagarjuna Nagar Guntur India Department of Electronics and Communication Engineering GTU – School of Engineering and Technology Ahmedabad India Independent Researcher USA Independent Researcher California USA Independent Researcher and Senior Software Engineer at TJMax USA Assistant Professor of Tourist Guiding Dept Bolu Abant Izzet Baysal University Golkoy Campus Bolu Turkey The Department of Tax and Taxation Tashkent State University of Economics Tashkent Uzbekistan Chandigarh Group of Colleges Jhanjeri Chandigarh Engineering College Department of Computer Science and Engineering Punjab India

Determining the weights of evaluation metrics is one of the key factors influencing the cybersecurity and anomaly detection of intelligent vehicular ad hoc networks (VANETs). To address the limitations of traditional weighting methods, which often overlook the impact of changes in metric attribute states on evaluation weights, this paper proposes a dynamic weight allocation–based network security and anomaly detection model. The model begins by decomposing and analyzing the security and anomaly detection objectives of VANETs, constructing a comprehensive evaluation metric system. The network security assessment model for VANETs presented in this research overcomes the drawbacks of conventional static models by utilizing a dynamic weight allocation technique. Based on current network conditions, a state variable weight method was created that dynamically computes security values by combining incentive and penalty mechanisms. A ranking-based weighting algorithm is employed to analyze the correlation between security and anomaly detection metrics. Subsequently, the proposed dynamic weight allocation algorithm calculates the dynamic weights of individual metrics within the system, enabling a robust assessment of network security and anomaly detection for intelligent VANETs. The evaluation results provide security level classifications and identify anomalies effectively. Experimental results demonstrate that the model significantly enhances the rationality and accuracy of intelligent VANET evaluations, contributing to improved cybersecurity and anomaly detection.

关键词： anomaly detection dynamic weight allocation intelligent vehicle network security VANETs

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Electroweak, QCD and flavour physics studies with ATLAS data from Run 2 of the LHC

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Physics Reports 2025年 1116卷 57-126页

作者： Aad, G. Aakvaag, E. Abbott, B. Abdelhameed, S. Abeling, K. Abicht, N.J. Abidi, S.H. Aboelela, M. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B.S. Ackermann, A. Adam Bourdarios, C. Adamczyk, L. Addepalli, S.V. Addison, M.J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Akiyama, D. Akolkar, N.N. Aktas, S. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Alegria, Z.L. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H.M.J. Ali, S. Alibocus, S.W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B.M.M. Allen, J.F. Allendes Flores, C.A. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alsolami, Z.M.K. Alvarez Estevez, M. Alvarez Fernandez, A. Alves Cardoso, M. Alviggi, M.G. Aly, M. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C.G. Amidei, D. Amirie, K.J. Amor Dos Santos, S.P. Amos, K.R. An, S. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparo, M.A. Aperio Bella, L. Appelt, C. Apyan, A. Arbiol Val, S.J. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Ashby Pickering, R.A. Assamagan, K. Astalos, R. Astrand, K.S.V. Atashi, S. Atkin, R.J. Atkinson, M. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Auriol, A.D. Austrup, V.A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Baer, T.M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. Baines, J.T. Baines, L. Baker, O.K. Bakos, E. Bakshi Gupta, D. Balakrishnan, V. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. CPPM Aix-Marseille Université CNRS IN2P3 Marseille France Department for Physics and Technology University of Bergen Bergen Norway Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman OK United States New York University Abu Dhabi Abu Dhabi United Arab Emirates II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton NY United States Physics Department Southern Methodist University Dallas TX United States Faculté des sciences Université Mohammed V Rabat Morocco Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel Department of Physics New York University New York NY United States INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King's College London London United Kingdom Kirchhoff-Institut für Physik Ruprecht-Karls-Universität Heidelberg Heidelberg Germany LAPP Université Savoie Mont Blanc CNRS IN2P3 Annecy France AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Brandeis University Waltham MA United States School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb IL United States Department of Physics Istanbul University Istanbul Turkey Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz CA United States Enrico Fermi Institute University of Chicago Chicago IL United States Institut de Física d'Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain INFN Sezione di Pavia Italy Dipartimento di Fisica Uni

A summary of precision measurements sensitive to electroweak, QCD and quark-flavour effects performed by the ATLAS Collaboration at the Large Hadron Collider is reported. The measurements are predominantly performed on proton–proton (pp) collision data recorded at a centre-of-mass energy of 13 TeV taken from 2015 to 2018, with an integrated luminosity of up to 140 fb⁻¹, with some results based on pp and Pb+Pb data recorded at lower nucleon centre-of-mass energies. The results cover a wide range of topics, from strong production of particles at low energies and the spectroscopy of hadrons to perturbative QCD with hadronic jets and electroweak and strong production of single and multiple vector bosons. They provide precise measurements of fundamental constants and stringent tests of the Standard Model with unprecedented precision and in energy ranges never explored before. They are also used to explore the proton structure and to perform model-independent searches for new physics. © 2024 CERN for the benefit of the ATLAS Collaboration

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Gravity Spy: Lessons Learned and a Path Forward

arXiv

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arXiv 2023年

作者： Zevin, Michael Jackson, Corey B. Doctor, Zoheyr Wu, Yunan Østerlund, Carsten Johnson, L. Clifton Berry, Christopher P.L. Crowston, Kevin Coughlin, Scott B. Kalogera, Vicky Banagiri, Sharan Davis, Derek Glanzer, Jane Hao, Renzhi Katsaggelos, Aggelos K. Patane, Oli Sanchez, Jennifer Smith, Joshua Soni, Siddharth Trouille, Laura Walker, Marissa Aerith, Irina Domainko, Wilfried Baranowski, Victor-Georges Niklasch, Gerhard Téglás, Barbara Zooniverse The Adler Planetarium 1300 South DuSable Lake Shore Drive ChicagoIL60605 United States Kavli Institute for Cosmological Physics The University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute The University of Chicago 933 East 56th Street ChicagoIL60637 United States Information School University of Wisconsin–Madison 600 N Park Street MadisonWI53706 United States Northwestern University 1800 Sherman Ave EvanstonIL60201 United States Department of Electrical and Computer Engineering Northwestern University 2145 Sheridan Road EvanstonIL60208 United States School of Information Studies Syracuse University 343 Hinds Hall SyracuseNY13210 United States SUPA School of Physics and Astronomy University of Glasgow Kelvin Building University Ave Glasgow8QQ G12 United Kingdom Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States LIGO Laboratory California Institute of Technology 1200 East California Boulavard PasadenaCA91125 United States Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton RougeLA70803 United States LIGO Hanford Observatory 127124 N Route 10 Hanford WA99354 United States The Nicholas and Lee Begovich Center for Gravitational-Wave Physics and Astronomy California State University Fullerton 800 N. State College Blvd FullertonCA92831 United States LIGO Laboratory Massachusetts Institute of Technology 185 Albany Street CambridgeMA02139 United States Department of Physics Computer Science and Engineering Christopher Newport University One Avenue of the Arts Newport NewsVA23606 United States Cedar Falls IA United States Hermagor Austria Sorbonne Paris Nord University 99 Avenue Jean Baptiste Clément Villetaneuse93430 France ConSol Software GmbH St.-Cajetan-Strasse 43 Munich81669 Germany Budapest Hungary Gravity Spy Moderator

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches. © 2023, CC BY.

关键词： Machine learning

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Generating Approximate Ground states of Molecules Using Quantum Machine Learning

arXiv

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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Modeling cloud workflows using computation structure model 33

Modeling cloud workflows using computation structure model

引用

33rd International Conference on computers and Their Applications, CATA 2018

作者： Alenizi, Abdullah Elfouly, Raafat Amamra, Abdelfattah Ammar, Reda Computer Science and Engineering Department University of Connecticut United States Mathematics and Computer Science Department Rhode Island College Canada Computer Science Department California State Polytechnic University United States

ISBN: (纸本)9781943436101

Computation Structure Model (CSM) has been used widely in computer science to get a better understanding of expected execution time and power consumption of resources during design time. In this paper, we try to utilize CSM capabilities and use it in cloud computing so that we have a better understanding of needed resources. Our methodology is based on transforming/mapping workflow applications into CSM model. copyright ISCA, CATA 2018

关键词： Cloud computing

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Deep learning for automated image classification of seismic damage to built infrastructure 11

Deep learning for automated image classification of seismic ...

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11th National Conference on Earthquake engineering 2018: Integrating science, engineering, and Policy, NCEE 2018

作者： Patterson, B. Leone, G. Pantoja, M. Behrouzi, A. Dept. of Computer Science & Software Engineering California Polytechnic State University - San Luis Obispo San Luis ObispoCA93407 United States Dept. of Architectural Engineering California Polytechnic State University - San Luis Obispo San Luis ObispoCA93407 United States

ISBN: (纸本)9781510873254

The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. The proposed solution is to automate post-earthquake reconnaissance image tagging activities by training a computer algorithm to classify each occurrence of damage per building material and structural member type. The approach is based on deep learning (DL), a subset of machine learning loosely based on the operation of a biologic neural system, which aims to learn and extract accurate representations from large data sets. DL algorithms are data driven;improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, DL has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. The authors implemented a DL algorithm to automatically identify multiple damage types and associated structural members in a single image by adapting a pre-trained deep residual network. The algorithm was tested as follows: (i) binning building images as damage-no damage (88% accuracy), (ii) drawing a bounding box around damage in buildings (85% accuracy) and short/captive reinforced concrete columns with shear damage (77% accuracy). The lower accuracy of correctly identifying a target region in an image (test ii) compared to simple binning (test i) is anticipated since it is a more complex problem and there is a more limited number of expertly tagged training images (200 count) for shear damage-short column condition being studied. The research team expects algorithm accuracy will improve as with training on additional images tagged for certain damage-structure pairs by a diverse set of experts. Copyright © 2018 Earthquake engineering Research Institute. All rights reserved.

关键词： Image enhancement

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Video Demo: Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video

Video Demo: Unsupervised Learning of Depth and Ego-Motion fr...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Alisha Sharma Jonathan Ventura Laboratories for Computational Physics & Fluid Dynamics Naval Research Laboratory Washington D.C USA Department of Computer Science & Software Engineering California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9781728156040

ISBN: (纸本)9781728156057

In this demonstration, we will present a video showing depth predictions for street-level 360° panoramic footage generated using our unsupervised learning model. Panoramic depth estimation is important for a range of applications in- cluding virtual reality, 3D modeling, and autonomous robotic navigation. We have developed a convolutional neural network (CNN) model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. In contrast with previous works, we focus on cylindrical panoramic projection. Unlike spherical or cube map projection, cylindrical projection is fully compatible with traditional CNN layers while still supporting a continuous 360° horizontal field of view. We find that this increased field of view improves the ego-motion prediction accuracy for street-level video input. This abstract motivates our work in unsupervised structure-from-motion estimation, describes the video demonstration, outlines our implementation, and summarizes our study conclusions.

关键词： Solid modeling Unsupervised learning Three-dimensional displays Virtual reality Predictive models Laboratories Computational modeling

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Growth optimization and device integration of narrow-bandgap graphene nanoribbons

arXiv

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arXiv 2022年

作者： Barin, Gabriela Borin Sun, Qiang Di Giovannantonio, Marco Du, Cheng-Zhuo Wang, Xiao-Ye Llinas, Juan Pablo Mutlu, Zafer Lin, Yuxuan Wilhelm, Jan Overbeck, Jan Daniels, Colin Lamparski, Michael Sahabudeen, Hafeesudeen Perrin, Mickael L. Urgel, José I. Mishra, Shantanu Kinikar, Amogh Widmer, Roland Stolz, Samuel Bommert, Max Pignedoli, Carlo Feng, Xinliang Calame, Michel Müllen, Klaus Narita, Akimitsu Meunier, Vincent Bokor, Jeffrey Fasel, Roman Ruffieux, Pascal Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf 8600 Switzerland State Key Laboratory of Elemento-Organic Chemistry College of Chemistry Nankai University Tianjin 300071 China Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Institute of Theoretical Physics University of Regensburg RegensburgD-93053 Germany Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Center for Advancing Electronics Dresden Department of Chemistry and Food Chemistry TU Dresden Dresden01062 Germany Max Planck Institute for Polymer Research Mainz55128 Germany Department of Chemistry Johannes Gutenberg-Universität Mainz Mainz55128 Germany Organic and Carbon Nanomaterials Unit Okinawa Institute of Science and Technology Graduate University 1919-1 Tancha Onna-son Okinawa904-0495 Japan Department of Chemistry Biochemistry and Pharmaceutical Sciences University of Bern Bern3012 Switzerland

The electronic, optical and magnetic properties of graphene nanoribbons (GNRs) can be engineered by controlling their edge structure and width with atomic precision through bottom-up fabrication based on molecular precursors. This approach offers a unique platform for all-carbon electronic devices but requires careful optimization of the growth conditions to match structural requirements for successful device integration, with GNR length being the most critical parameter. In this work, we study the growth, characterization, and device integration of 5-atom wide armchair GNRs (5-AGNRs), which are expected to have an optimal band gap as active material in switching devices. 5-AGNRs are obtained via on-surface synthesis under ultrahigh vacuum conditions from Br- and I-substituted precursors. We show that the use of I-substituted precursors and the optimization of the initial precursor coverage quintupled the average 5-AGNR length. This significant length increase allowed us to integrate 5-AGNRs into devices and to realize the first field-effect transistor based on narrow bandgap AGNRs that shows switching behavior at room temperature. Our study highlights that optimized growth protocols can successfully bridge between the sub-nanometer scale, where atomic precision is needed to control the electronic properties, and the scale of tens of nanometers relevant for successful device integration of GNRs. Copyright © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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Unsupervised Learning of Depth and Ego-Motion From Cylindrical Panoramic Video

Unsupervised Learning of Depth and Ego-Motion From Cylindric...

引用

IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

ISBN: (数字)9781728156040

ISBN: (纸本)9781728156057

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3d modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.

关键词： Solid modeling Distortion Three-dimensional displays Data models Convolution Unsupervised learning Training

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