A survey of multisensor fusion techniques, architectures and methodologies

Chandrasekaran, Balasubramaniyan; Gangadhar, Shruti; Conrad, James M.

doi:10.1109/secon.2017.7925311

Cited by 35 publications

(12 citation statements)

References 31 publications

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“…As the number of biomedical signals used to characterize the physiological state of a patient grows, effective approaches to fuse this information are needed, given that many of the signals may be irrelevant or are prone to uncertainty and measurement variability [6]. Several papers proposed data fusion techniques that identified reliable signals and distinguished relative importance among different signals.…”

Section: Emerging Trend 1: Selective Fusion Of Multiple Signals/ Domamentioning

confidence: 99%

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2020

Yearb Med Inform

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Objective: To highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics. Method: A broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers. Results: The query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals. Conclusions: The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

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Section: Emerging Trend 1: Selective Fusion Of Multiple Signals/ Domamentioning

confidence: 99%

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2020

Yearb Med Inform

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“…This level deals with the relationships between objects and observed events, attempting to provide a contextual description between the relationships [27] [29]. b) Level 3 -Threat Refinement The fusion process of this level attempts to create data for future predictions.…”

Section: A) Level 2 -Situation Refinementmentioning

confidence: 99%

“…The monitoring of system performance, including handling real time constraints is addressed at this level [29]. This level of the data fusion model does not perform any data processing operations, as it is more focused on identifying information required for data fusion improvement [35] [36].…”

Section: C) Level 4 -Process Refinementmentioning

confidence: 99%

Big Data Fusion Model for Heterogeneous Financial Market Data (FinDf)

Evans

Owda

Crockett

et al. 2018

Advances in Intelligent Systems and Computing

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The dawn of big data has seen the volume, variety, and velocity of data sources increase dramatically. Enormous amounts of structured, semi-structured and unstructured heterogeneous data can be garnered at a rapid rate, making analysis of such big data a herculean task. This has never been truer for data relating to financial stock markets, the biggest challenge being the 7 Vs of big data which relate to the collection, pre-processing, storage and real-time processing of such huge quantities of disparate data sources. Data fusion techniques have been adopted in a wide number of fields to cope with such vast amounts of heterogeneous data from multiple sources and fuse them together in order to produce a more comprehensive view of the data and its underlying relationships. Research into the fusing of heterogeneous financial data is scant within the literature, with existing work only taking into consideration the fusing of text-based financial documents. The lack of integration between financial stock market data, social media comments, financial discussion board posts and broker agencies means that the benefits of data fusion are not being realised to their full potential. This paper proposes a novel data fusion model, inspired by the data fusion model introduced by the Joint Directors of Laboratories, for the fusing of disparate data sources relating to financial stocks. Data with a diverse set of features from different data sources will supplement each other in order to obtain a Smart Data Layer, which will assist in scenarios such as irregularity detection and prediction of stock prices.

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“…At present, there is no effective fusion model and algorithm for information fusion of heterogeneous multi-sensor information [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] , but with the help of modern statistical theory, this problem can be effectively solved to a certain extent [ 16 -22 ] . As an important branch of statistical theory, DS evidence theory is widely used in the field of multi-sensor information fusion due to its ability to represent uncertain and unknown information clearly and its strong operability.…”

Section: Introductionmentioning

confidence: 99%

Research on Improved DS Evidence Theory

Lin

Xie

2021

Preprint

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In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved DS evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the BPA of each focal element to obtain a new evidence source. Then, the concept of credibility is introduced, and the synthetic rules are improved by using the credibility of evidence and the average support of evidence focal elements, so as to obtain the fusion results. The results show that compared with other algorithms, this algorithm can solve the problems of DS evidence theory in dealing with highly conflicting evidence to a certain extent, and the fused results are more reasonable and converge faster.

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A survey of multisensor fusion techniques, architectures and methodologies

Cited by 35 publications

References 31 publications

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Big Data Fusion Model for Heterogeneous Financial Market Data (FinDf)

Research on Improved DS Evidence Theory

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A survey of multisensor fusion techniques, architectures and methodologies

Cited by 35 publications

References 31 publications

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Big Data Fusion Model for Heterogeneous Financial Market Data (FinDf)

Research on Improved DS&nbsp;Evidence Theory

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Product

Resources

About

Research on Improved DS Evidence Theory