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Traffic Optimization Algorithms in Optical Networks For Real Time Traffic Analysis

Optik 2022年

作者： Suresh Kumar. K Swati Vijay Shinde S. Venkata Lakshmi R.S. Sabeenian Koppisetti Durga Bhavani K.V.S.R. Murthy Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Dept. of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of ECE Sona College of Technology Salem Department of Management Studies Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India EEE Department & Dean (R&D) Aditya Group of Engineering Colleges

The rapid growth of metropolitan areas poses significant traffic control issues. Advanced telecommunication structures, such as the IoT (Internet of Things) technology, including wireless connections, produce enormous amounts of diverse traffic information. Conventional networking control approaches for tracking and information analysis confront certain obstacles and problems in specific systems, like reliability and efficient actual-time analysis of massive datasets. Furthermore, due to variables like gadget portability and connection diversity, the overall structure of internet activity, particularly in wireless networks, exhibits extremely complicated activity. The article aims to create a method for collecting high-quality, comprehensive information like transportation flowing strength, travel routes, and mean vehicle velocity. Simultaneously, information is gathered throughout the whole operational zone of junctions and neighboring highway segments under the city video security sensor perspective. More profound learning methods will be used within the communication domain for Networking Transport Management and Analytics (NTMA) activities such as traffic categorization and forecasting. The YOLOv3 artificial routing topology, as well as a SORT-accessible monitor, are used in our approach. Utilizing extended masking branches and improving its geometry of anchoring, overall essential efficiency in YOLO has been enhanced. We employed a technique of viewpoint conversion using parameters from actual pictures to global dimensions for computing overall vehicular velocities.

关键词： Networking Traffic Management and Analytics (NTMA) real-time surveillance traffic management and database processing

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Mass supply from Io to Jupiter's magnetosphere

arXiv

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arXiv 2024年

作者： Roth, L. Blöcker, A. de Kleer, K. Goldstein, D. Lellouch, E. Saur, J. Schmidt, C. Strobel, D.F. Tao, C. Tsuchiya, F. Dols, V. Huybrighs, H. Mura, A. Szalay, J.R. Badman, S.V. de Pater, I. Dott, A.-C. Kagitani, M. Klaiber, L. Koga, R. McEwen, A. Milby, Z. Retherford, K.D. Schlegel, S. Thomas, N. Tseng, W.L. Vorburger, A. KTH Royal Institute of Technology Space and Plasma Physics Stockholm Sweden Department of Earth and Environmental Sciences Ludwig Maximilian University of Munich Munich Germany Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA91125 United States Dept. Aerospace Engineering and Engineering Mechanics The University of Texas at Austin AustinTX United States Observatoire de Paris Meudon France Institute of Geophysics and Meteorology University of Cologne Cologne Germany Center for Space Physics Boston University BostonMA United States Departments of Earth & Planetary Science and Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States National Institute of Information and Communications Technology Koganei Japan Graduate School of Science Tohoku University Sendai Japan Institute for Space Astrophysics and Planetology National Institute for Astrophysics Italy School of Cosmic Physics DIAS Dunsink Observatory Dublin Institute for Advanced Studies Dublin15 Ireland Space and Planetary Science Center Khalifa University Abu Dhabi United Arab Emirates Department of Mathematics Khalifa University Abu Dhabi United Arab Emirates Roma Italy Department of Astrophysical Sciences Princeton University PrincetonNJ United States Department of Physics Lancaster University LancasterLA1 4YB United Kingdom Department of Astronomy Department of Earth & Planetary Science University of California BerkeleyCA94720 United States Physics Institute University of Bern Bern3012 Switzerland School of Data Science Nagoya University Aichi Nagoya464-8601 Japan Department of Planetary Sciences University of Arizona TucsonAZ United States Southwest Research Institute San AntonioTX United States University of Texas at San Antonio San AntonioTX United States Department of Earth Sciences National Taiwan Normal University Taiwan

Since the Voyager mission flybys in 1979, we have known the moon Io to be both volcanically active and the main source of plasma in the vast magnetosphere of Jupiter. Material lost from Io forms neutral clouds, the Io plasma torus and ultimately the extended plasma sheet. This material is supplied from Io’s upper atmosphere and atmospheric loss is likely driven by plasma-interaction effects with possible contributions from thermal escape and photochemistry-driven escape. Direct volcanic escape is negligible. The supply of material to maintain the plasma torus has been estimated from various methods at roughly one ton per second. Most of the time the magnetospheric plasma environment of Io is stable on timescales from days to months. Similarly, Io’s atmosphere was found to have a stable average density on the dayside, although it exhibits lateral (longitudinal and latitudinal) and temporal (both diurnal and seasonal) variations. There is potential positive feedback in the Io torus supply: collisions of torus plasma with atmospheric neutrals are probably a significant loss process, which increases with torus density. The stability of the torus environment may be maintained by limiting mechanisms of either torus supply from Io or the loss from the torus by centrifugal interchange in the middle magnetosphere. Various observations suggest that occasionally (roughly 1 to 2 detections per decade) the plasma torus undergoes major transient changes over a period of several weeks, apparently overcoming possible stabilizing mechanisms. Such events (as well as more frequent minor changes) are commonly explained by some kind of change in volcanic activity that triggers a chain of reactions which modify the plasma torus state via a net change in supply of new mass. However, it remains unknown what kind of volcanic event (if any) can trigger events in torus and magnetosphere, whether Io’s atmosphere undergoes a general change before or during such events, and what processes could

关键词： Magnetosphere

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Toward finding latent cities with non-negative matrix factorization

Toward finding latent cities with non-negative matrix factor...

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2018 Joint ACM IUI Workshops, ACMIUI-WS 2018

作者： Graells-Garrido, Eduardo Caro, DIego Parra, Denis Data Science Institute Universidad Del Desarrollo Telefonica RandD Santiago Chile Dept. of Computer Science Faculty of Engineering Pontificia Universidad Catolica Santiago Chile

In the last decade, digital footprints have been used to cluster population activity into functional areas of cities. However, a key aspect has been overlooked: we experience our cities not only by performing activities at specific destinations, but also by moving from one place to another. In this paper, we propose to analyze and cluster the city based on how people move through it. Particularly, we introduce Mobilicities, automatically generated travel patterns inferred from mobile phone network data using NMF, a matrix factorization model. We evaluate our method in a large city and we find that mobilicities reveal latent but at the same time interpretable mobility structures of the city. Our results provide evidence on how clustering and visualization of aggregated phone logs could be used in planning systems to interactively analyze city structure and population activity. © 2018 Copyright for the individual papers remains with the authors.

关键词： Non-negative matrix factorization

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Search for Coordinates of WSN Nodes using Multidimensional Scaling Method

Search for Coordinates of WSN Nodes using Multidimensional S...

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International Conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： Ilya Grishin Van Dai Pham Darina Okuneva Ruslan Kirichek Dept. of Communication Networks and Data Transmission Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia Dept. of Software Engineering and Computer Science Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781728192819

ISBN: (纸本)9781728192826

Nowadays, wireless sensor networks (WSN) are widely used for various Internet of Things applications. The sensor nodes are usually deployed randomly in the field. Thus, searching for node coordinates is one of the requirements to construct the network structure. With knowing distances between nodes and the coordinates of anchor nodes in a certain network fragment, we propose a method based on the multidimensional scaling for searching for desired node coordinates.

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Indefinite kernel spectral learning 30

Indefinite kernel spectral learning

引用

30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Dept. of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 LeuvenB-3001 Belgium

This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning. © 2018 University of Groningen. All rights reserved.

关键词： Clustering algorithms

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Sampling Free TDOA Localization in Millimeter Wave Networks

arXiv

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arXiv 2019年

作者： Shree Prasad, M. Panigrahi, Trilochan Hassan, Mahbub Ding, Ming Dept. of Electronics and Communication Engineering National Institute of Technology Goa School of Computer Science and Engineering University of New South Wales Sydney Australia Data61 Csiro Sydney Australia

Time difference of arrival (TDOA) is a widely used technique for localizing a radio transmitter from the difference in signal arrival times at multiple receivers. For TDOA to work, the individual receivers must estimate the respective signal arrival times precisely, which requires sampling the signal at least double the rate of its highest frequency content, commonly known as the Nyquist rate. Such sampling is less practical for the millimeter wave band comprising of frequencies in 30-300 GHz range. In this paper, we propose an energy detection architecture for accurately estimating the time of arrival from a single picosecond Gaussian pulse, which enables TDOA localization without sampling at the receiver. We derive the closed form expression of the estimated time of arrival and validate it via simulation. We demonstrate that the proposed sampling-free TDOA can localize millimeter wave transmitters as accurately as the conventional samplingbased TDOA. Copyright © 2019, The Authors. All rights reserved.

关键词： Time difference of arrival

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An OpenMetBuoy dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023

arXiv

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arXiv 2024年

作者： Rabault, Jean Taelman, Catherine Idžanović, Martina Hope, Gaute Nose, Takehiko Kristoffersen, Yngve Jensen, Atle Breivik, Øyvind Bryhni, Helge Thomas Hoppmann, Mario Demchev, Denis Korosov, Anton Johansson, Malin Eltoft, Torbjørn Dagestad, Knut-Frode Röhrs, Johannes Eriksson, Leif Moro, Marina Durán Rikardsen, Edel S.U. Waseda, Takuji Kodaira, Tsubasa Lohse, Johannes Desjonquères, Thibault Olsen, Sveinung Gundersen, Olav de Aguiar, Victor Cesar Martins Karlsen, Truls Babanin, Alexander Voermans, Joey Park, Jeong-Won Müller, Malte Norwegian Meteorological Institute IT Department Oslo0313 Norway UiT The Arctic University of Norway Dept. of Physics and Technology Tromsø9037 Norway Norwegian Meteorological Institute Division for Ocean and Ice Oslo0371 Norway Norwegian Meteorological Institute Division for Oceanography and Marine Meteorology Bergen5007 Norway The University of Tokyo Graduate School of Frontier Sciences Kashiwa277-8561 Japan University of Bergen Department of Earth Science Bergen5007 Norway University of Oslo Department of Mathematics Oslo0313 Norway University of Bergen Geophysical Institute Bergen5007 Norway Alfred-Wegener-Institut Helmholtz-Zentrum fuer Polar- und Meeresforschung Bremerhaven27580 Germany Department of Space Earth and Environment Chalmers University of Technology Gothenburg41296 Sweden Ocean and Sea Ice Remote Sensing Group Nansen Environmental and Remote Sensing Center Bergen5006 Norway Norwegian Meteorological Institute Remote sensing and data management division Oslo Norway University of Oslo Department of Geosciences Oslo0313 Norway Japan Agency for Marine-Earth Science and Technology Yokosuka237-0061 Japan Gothenburg University Gothenburg40508 Sweden Department of Infrastructure Engineering University of Melbourne Melbourne Australia Center of Remote Sensing and GIS Korea Polar Research Institute Incheon21990 Korea Republic of Norwegian Meteorological institute R&D Department Oslo0313 Norway

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms. © 2024, CC BY.

关键词： Water waves

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Do Positive Twitter Messages Have Impact on Stock Liquidity?

Do Positive Twitter Messages Have Impact on Stock Liquidity?

引用

作者： Abdirahmani, Aliasghar Angulo, Cecilio Automatic Control Dept. Universitat Politècnica de Catalunya Pau Gargallo 14. BacelonaTech08028 Spain Knowledge Engineering Research Group Intelligent Data Science and Artificial Intelligence Research Center Univ. Politècnica Catalunya BarcelonaTech Spain

ISBN: (纸本)9781614999171

Social media is a phenomenon that has transformed the interaction and communication of individuals throughout the world. In recent times, social media has affected many aspects of human communication, as well as business. The main objective of our research is to figure out about the role of Twitter on stock market to find out how social media impacts on the business that predictions fail. © 2018 The authors and IOS Press.

关键词： Sentiment analysis

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A Model for Recommending Actions during the Collaborative Design of Visual Analytics Flows 4

A Model for Recommending Actions during the Collaborative De...

引用

4th International Conference on Information Systems and Computer science, INCISCOS 2019

作者： Arce-Orozco, Armando Gonzalez-Torres, Antonio Mata-Montero, Erick Bener, Ayse Jara-Watson, Hans School of Computing Costa Rica Institute of Technology Cartago Costa Rica Costa Rica School of Computing National University Heredia Costa Rica Costa Rica Dept. of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica School of Computing ULACIT Costa Rica Data Science Lab Ryerson University Toronto Ontario Canada

ISBN: (纸本)9781728155814

Visual Analytics (VA) is a process to facilitate the uncovering of knowledge from large and complex datasets to enable users to make informed decisions. However, the design of VA tools is frequently performed by multidisciplinary teams that can be physically in the same place or distributed across different locations. Therefore, there is an intrinsic collaboration need between team members and also of proposing methods to support these teams to work collaboratively during the design and implementation of VA systems. Although some proposals have made on the use of visual data flows and recommender systems, its combination with a Multi-Device Environment can benefit the discussion and exchange of knowledge during design and programming. Therefore, this article presents the design of an architecture that combines a Multi-Device Environment (MDE) with visual data flows and recommender systems for the design of VA tools in a collaborative way. The intention is to support designers and programmers during the design of visual data flows through the sharing and analysis of ideas in a collaborative environment supported by a recommender system that aids decision making during the design of flows. © 2019 IEEE.

关键词： Recommender systems

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Towards semantically enhanced data understanding

arXiv

引用

arXiv 2018年

作者： Schröder, Markus Hees, Jörn Jilek, Christian Dengel, Andreas Smart Data & Knowledge Services Dept. DFKI GmbH Computer Science Dept. TU Kaiserslautern Kaiserslautern Germany

In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets. Such knowledge-intensive tasks require a lot of documentation, which is necessary for data scientists to grasp the meaning of the data. Usually, documentation is separate from the data in various external documents, diagrams, spreadsheets and tools which causes considerable look up overhead. Moreover, other supporting applications are not able to consume and utilize such unstructured data. That is why we propose a methodology that uses a single semantic model that interlinks data with its documentation. Hence, data scientists are able to directly look up the connected information about the data by simply following links. Equally, they can browse the documentation which always refers to the data. Furthermore, the model can be used by other approaches providing additional support, like searching, comparing, integrating or visualizing data. To showcase our approach we also demonstrate an early prototype. Copyright © 2018, The Authors. All rights reserved.

关键词： data mining

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