Conferences related to Data Analysis

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2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)

The conference will provide a forum for discussions and presentations of advancements inknowledge, new methods and technologies relevant to industrial electronics, along with their applications and future developments.


2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.


2019 IEEE International Professional Communication Conference (ProComm)

The scope of the conference includes the study, development, improvement, and promotion ofeffective techniques for preparing, organizing, processing, editing, collecting, conserving,teaching, and disseminating any form of technical information by and to individuals and groupsby any method of communication. It also includes technical, scientific, industrial, and otheractivities that contribute to the techniques and products used in this field.


2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

robotics, intelligent systems, automation, mechatronics, micro/nano technologies, AI,


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Periodicals related to Data Analysis

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


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Most published Xplore authors for Data Analysis

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Xplore Articles related to Data Analysis

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Noise threshold estimation in spectrum monitoring data analysis application

[{u'author_order': 1, u'affiliation': u'University of Electronic Science and Technology of China, Science and Technology on Electronic Information Control Laboratory, Chengdu, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086386834', u'full_name': u'Min Chen', u'id': 37086386834}, {u'author_order': 2, u'affiliation': u'Science and Technology on Electronic Information Control Laboratory, Chengdu, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086387386', u'full_name': u'Rong Shi', u'id': 37086387386}, {u'author_order': 3, u'affiliation': u'University of Electronic Science and Technology of China, Chengdu, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37532755300', u'full_name': u'Binbin He', u'id': 37532755300}] 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), 2018

With the rapid development of radio service and monitoring facilities, spectrum monitoring applications step into big data era. Based on the support of big data analysis technology, many algorithms are proposed to get valuable information through dealing with massive monitoring data. However, performances of these algorithms are limited by the usual methods of setting noise threshold. In this paper, we ...


An M2M Data Analysis Service System Based on Open Source Software Environments

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/38233436500', u'full_name': u'Shinji Kitagami', u'id': 38233436500}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37070528600', u'full_name': u'Moriki Yamamoto', u'id': 37070528600}, {u'author_order': 3, u'authorUrl': u'https://ieeexplore.ieee.org/author/37070600900', u'full_name': u'Hisao Koizumi', u'id': 37070600900}, {u'author_order': 4, u'authorUrl': u'https://ieeexplore.ieee.org/author/37317844100', u'full_name': u'Takuo Suganuma', u'id': 37317844100}] 2013 27th International Conference on Advanced Information Networking and Applications Workshops, 2013

Data analysis in a Machine-to-Machine (M2M) service system should concurrently satisfy three requirements, massive data analysis, real-time data analysis, and deep data analysis. However, for this purpose, it is necessary to introduce costly software products such as a Data Stream Management System (DSMS) into M2M service system. In this paper, we propose an M2M data analysis service system using open ...


Visual analytics of terrorist activities related to epidemics

[{u'author_order': 1, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38267713800', u'full_name': u'Enrico Bertini', u'id': 38267713800}, {u'author_order': 2, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233607000', u'full_name': u'Juri Buchm\xfcller', u'id': 38233607000}, {u'author_order': 3, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38242836400', u'full_name': u'Fabian Fischer', u'id': 38242836400}, {u'author_order': 4, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38242146100', u'full_name': u'Stephan Huber', u'id': 38242146100}, {u'author_order': 5, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233610100', u'full_name': u'Thomas Lindemeier', u'id': 38233610100}, {u'author_order': 6, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233609600', u'full_name': u'Fabian Maa\xdf', u'id': 38233609600}, {u'author_order': 7, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/37392086200', u'full_name': u'Florian Mansmann', u'id': 37392086200}, {u'author_order': 8, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233608600', u'full_name': u'Thomas Ramm', u'id': 38233608600}, {u'author_order': 9, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233605200', u'full_name': u'Michael Regenscheit', u'id': 38233605200}, {u'author_order': 10, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/37601356700', u'full_name': u'Christian Rohrdantz', u'id': 37601356700}, {u'author_order': 11, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233604500', u'full_name': u'Christian Scheible', u'id': 38233604500}, {u'author_order': 12, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/37282557600', u'full_name': u'Tobias Schreck', u'id': 37282557600}, {u'author_order': 13, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233604000', u'full_name': u'Stephan Sellien', u'id': 38233604000}, {u'author_order': 14, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233603600', u'full_name': u'Florian Stoffel', u'id': 38233603600}, {u'author_order': 15, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233606900', u'full_name': u'Mark Tautzenberger', u'id': 38233606900}, {u'author_order': 16, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/38233606500', u'full_name': u'Matthias Zieker', u'id': 38233606500}, {u'author_order': 17, u'affiliation': u'Data Analysis and Visualization Group, University of Konstanz, Germany', u'authorUrl': u'https://ieeexplore.ieee.org/author/37283138700', u'full_name': u'Daniel A. Keim', u'id': 37283138700}] 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), 2011

The task of the VAST 2011 Grand Challenge was to investigate potential terrorist activities and their relation to the spread of an epidemic. Three different data sets were provided as part of three Mini Challenges (MCs). MC 1 was about analyzing geo-tagged microblogging (Twitter) messages to characterize the spread of an epidemic. MC 2 required analyzing threats to a computer ...


Data analysis anti-patterns in empirical software engineering

[{u'author_order': 1, u'affiliation': u"Department of Theoretical and Applied Sciences, Universit\xe0 degli Studi dell'Insubria, Como, Italy", u'authorUrl': u'https://ieeexplore.ieee.org/author/37265073000', u'full_name': u'Sandro Morasca', u'id': 37265073000}] 2013 1st International Workshop on Data Analysis Patterns in Software Engineering (DAPSE), 2013

The paper introduces the concept of data analysis anti-patterns, i.e., data analysis procedures that may lead to invalid results that may mislead decision makers. Two examples of anti-patterns are presented and discussed.


Impacts of public transportation fare reduction policy on urban public transport sharing rate based on big data analysis

[{u'author_order': 1, u'affiliation': u'MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086247391', u'full_name': u'Sijia Zhang', u'id': 37086247391}, {u'author_order': 2, u'affiliation': u'MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/38199495400', u'full_name': u'Shunping Jia', u'id': 38199495400}, {u'author_order': 3, u'affiliation': u'MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086255761', u'full_name': u'Cunrui Ma', u'id': 37086255761}, {u'author_order': 4, u'affiliation': u'MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086399782', u'full_name': u'Yuqiong Wang', u'id': 37086399782}] 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018

Urban transport is an important support system to the city. With the city's development, traffic congestion has become a major traffic problem nowadays and it is badly in need of solutions. Big data analysis has been widely used in the domain of transportation in recent years and it does great help to find solutions to different kinds of problems from ...


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Educational Resources on Data Analysis

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eLearning

No eLearning Articles are currently tagged "Data Analysis"

IEEE-USA E-Books

  • 2003-01-0504 Accidents Data Analysis for the Real World Safety Enhancement

    None

  • DATA ANALYSIS AND MACHINE LEARNING EFFORT IN HEALTHCARE

    This chapter examines the ideology of data‐driven healthcare and explores how new projects are defined and what is delivered as a proof of concept (POC). It discusses prerequisites and personnel qualifications for data science in healthcare; with a contributed subsection on the role of highperformance computing (HPC). The chapter provides a description of medical data and list the issues arising during data acquisition and transformation; a contributed subsection discusses de‐identification data sharing. It overviews the main themes of machine learning; the intention is to provide the readers with ideas and keywords for future independent exploration. The chapter presents a case study: prediction of rare adverse events based on traditional, nonspecific medical data. The enabling technology is unified electronic medical records (EMRs) that allow access, search, and simple sorting or comparison of hundreds of standard clinical features for millions of patients.

  • 2003-01-0154 Field Data Analysis of Rear Occupant Injuries Part II: Children, Toddlers and Infants

    None

  • Chapter 2 Data Analysis Software Requirements

    Racecar data acquisition used to be limited to well-funded teams in high- profile championships. Today, the cost of electronics has decreased dramatically, making them available to everyone. But the cost of any data acquisition system is a waste of money if the recorded data is not interpreted correctly. This book, updated from the best-selling 2008 edition, contains techniques for analyzing data recorded by any vehicle's data acquisition system. It details how to measure the performance of the vehicle and driver, what can be learned from it, and how this information can be used to advantage next time the vehicle hits the track. Such information is invaluable to racing engineers and managers, race teams, and racing data analysts in all motorsports. Whether measuring the performance of a Formula One racecar or that of a road-legal street car on the local drag strip, the dynamics of vehicles and their drivers remain the same. Identical analysis techniques apply. Some race series have restricted data logging to decrease the team’s running budgets. In these cases it is extremely important that a maximum of information is extracted and interpreted from the hardware at hand. A team that uses data more efficiently will have an edge over the competition. However, the ever-decreasing cost of electronics makes advanced sensors and logging capabilities more accessible for everybody. With this comes the risk of information overload. Techniques are needed to help draw the right conclusions quickly from very large data sets. In addition to updates throughout, this new edition contains three new chapters: one on techniques for analyzing tire performance, one that provides an introduction to metric- driven analysis, a technique that is used throughout the book, and another that explains what kind of information the data contains about the track.

  • Chapter 2 Data Analysis Software Requirements

    None

  • Chapter 17 Data Analysis Using Metrics

    Racecar data acquisition used to be limited to well-funded teams in high- profile championships. Today, the cost of electronics has decreased dramatically, making them available to everyone. But the cost of any data acquisition system is a waste of money if the recorded data is not interpreted correctly. This book, updated from the best-selling 2008 edition, contains techniques for analyzing data recorded by any vehicle's data acquisition system. It details how to measure the performance of the vehicle and driver, what can be learned from it, and how this information can be used to advantage next time the vehicle hits the track. Such information is invaluable to racing engineers and managers, race teams, and racing data analysts in all motorsports. Whether measuring the performance of a Formula One racecar or that of a road-legal street car on the local drag strip, the dynamics of vehicles and their drivers remain the same. Identical analysis techniques apply. Some race series have restricted data logging to decrease the team’s running budgets. In these cases it is extremely important that a maximum of information is extracted and interpreted from the hardware at hand. A team that uses data more efficiently will have an edge over the competition. However, the ever-decreasing cost of electronics makes advanced sensors and logging capabilities more accessible for everybody. With this comes the risk of information overload. Techniques are needed to help draw the right conclusions quickly from very large data sets. In addition to updates throughout, this new edition contains three new chapters: one on techniques for analyzing tire performance, one that provides an introduction to metric- driven analysis, a technique that is used throughout the book, and another that explains what kind of information the data contains about the track.

  • Graphical Data Analysis

    In statistics, graphs or plots provide a very powerful means to visualize the meaning of data. This chapter discusses the plotting of data and particularly techniques for making probability plots. The display of data in graphs may allow the comparison of distributions and the identification of data that likely do or do not belong in the distribution under consideration. The chapter discusses graphs that represent the cumulative distribution or the reliability function versus time. It describes various parametric plots, such as Weibull plot, exponential plot, and normal probability plot, together with their confidence intervals. Finally, as for power law reliability growth, the Duane model and the Crow AMSAA model are discussed. These are connected to the Poisson distribution and are very useful for monitoring improvement programmes and checking the overall trend in reliability development.

  • Data Analysis

    This chapter presents data analysis techniques pertinent to various radio channels. The chapter first discusses the analysis of a single radio channel measurement to estimate the impulse response and the frequency response of the channel using basic spectral analysis techniques. It commences with the discrete Fourier transform (DFT) and the effect of the window functions on the estimated channel response. Then, the chapter addresses statistical analysis of time and space series. It defines and uses the RUNS test to determine the stationarity of a process. The last part of the chapter addresses high resolution parameter estimation techniques used in double directional analysis such as space‐alternating generalized expectation (SAGE), multiple signal classification (MUSIC) and estimation of signal parameters via the rational invariance technique (ESPRIT). This is followed by a discussion on the estimation of multiple input—multiple output (MIMO) channel capacity.

  • Molecular Bioengineering and Nanobioscience: Data Analysis and Processing Methods

    This chapter contains sections titled: * Introduction * Data Analysis and Processing Methods for Genomics in the Postgenomic Era * From Genomics to Proteomics * Protein Structure Determination * Conclusions

  • Soft Computing in Signal and Data Analysis: Neural Networks, NeuroFuzzy Networks, and Genetic Algorithms

    This chapter contains sections titled: * Introduction * Adaptive Networks * Neural Networks * Learning * Structural Adaptation * Neuro-Fuzzy Networks * Genetic Algorithms



Standards related to Data Analysis

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Guide for the statistical analysis of electrical insulation breakdown data


IEEE Application Guide for Distributed Digital Control and Monitoring for Power Plants


IEEE Recommended Practice for Inertial Sensor Test Equipment, Instrumentation, Data Acquisition, and Analysis

Recommended practices for gyroscope and accelerometer testing are discussed, ranging from the equipment and instrumentation employed to the way that tests are carried out and data are acquired and analyzed.


IEEE Recommended Practice for Radar Cross-Section Test Procedures

This recommended practice establishes processes for the measurement of the electromagnetic scattering from objects. It is written for the personnel responsible for the operation of test ranges, and not for the design of such ranges. It recommends procedures for testing and documenting the quality of the measurement system, for calibrating the measurement system, for carrying out the radar scattering measurements, ...



Jobs related to Data Analysis

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