Relevant bibliographies by topics / Data fusion algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data fusion algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Data fusion algorithms"

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ZHU, SHANFENG, QIZHI FANG, and WEIMIN ZHENG. "SOCIAL CHOICE FOR DATA FUSION." International Journal of Information Technology & Decision Making 03, no. 04 (2004): 619–31. http://dx.doi.org/10.1142/s0219622004001288.

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Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this w
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Quadri, S. A., and Othman Sidek. "Role of Algorithm Engineering in Data Fusion Algorithms." Journal of Computational Intelligence and Electronic Systems 2, no. 1 (2013): 29–35. http://dx.doi.org/10.1166/jcies.2013.1046.

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LIPOVETSKY, STAN. "DATA FUSION IN SEVERAL ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 03 (2013): 1350014. http://dx.doi.org/10.1142/s1793536913500143.

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Data fusion consists of the process of integrating several datasets with some common variables, and other variables available only in partial datasets. The main problem of data fusion can be described as follows. From one source, having X0 and Y0 datasets (with N0 observations by multiple x and y variables, n and m of those, respectively), and from another source, having X1 data (with N1 observations by the same nx-variables), we need to estimate the missing portion of the Y1 data (of size N1 by m variables) in order to combine all the data into one set. Several algorithms are considered in th
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Zhang, Jie. "Security Technology of Wireless Sensor Internet of Things Based on Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 11 (2017): 25. http://dx.doi.org/10.3991/ijoe.v13i11.7748.

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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of var
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Tan, Yuxiang, Yann Tambouret, and Stefano Monti. "SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/780519.

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The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evalua
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Wu, Jian, Liang Xu, Qi Chen, and Zhihui Ye. "Multi-sensor data fusion path combining fuzzy theory and neural networks." Intelligent Decision Technologies 18, no. 4 (2024): 3365–78. https://doi.org/10.3233/idt-240316.

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In the development of automation and intelligent systems, multi-sensor data fusion technology is crucial. However, due to the uncertainty and incompleteness of sensor data, how to effectively fuse these data has always been a challenge. To solve this problem, the study combines fuzzy theory and neural networks to study the process of multi-sensor data transmission and data fusion. Sensor network clustering algorithms based on whale algorithm optimized fuzzy logic and neural network data fusion algorithms based on sparrow algorithm optimized were designed respectively. The performance test resu
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Cui, Haiting, and Shanshan Li. "Controllable Clustering Algorithm for Associated Real-Time Streaming Big Data Based on Multi-Source Data Fusion." Wireless Communications and Mobile Computing 2022 (February 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/5244695.

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Aiming at the problems of poor security and clustering accuracy in current data clustering algorithms, a controllable clustering algorithm for real-time streaming big data based on multi-source data fusion is proposed. The FIR filter structure model is used to suppress network interference, and ant colony algorithm is used to detect the abnormal data in the big data. By optimizing the iteration, the pheromone concentration is placed in the front position as the abnormal data point, and the filter is introduced. The fusion scope of multi-source data fusion is set. Combined with the data similar
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Abdulhafiz, Waleed A., and Alaa Khamis. "Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion." Advances in Artificial Intelligence 2013 (November 3, 2013): 1–11. http://dx.doi.org/10.1155/2013/241260.

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Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a
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Gan, Hock, Iosif Mporas, Saeid Safavi, and Reza Sotudeh. "Speaker Identification Using Data-Driven Score Classification." Image Processing & Communications 21, no. 2 (2016): 55–63. http://dx.doi.org/10.1515/ipc-2016-0011.

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Abstract We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-
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Lv, Lihua. "RFID Data Analysis and Evaluation Based on Big Data and Data Clustering." Computational Intelligence and Neuroscience 2022 (March 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/3432688.

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The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The
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Dissertations / Theses on the topic "Data fusion algorithms"

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Aziz, Ashraf Mamdouh Abdel. "New data fusion algorithms for distributed multi-sensor multi-target environments." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA369780.

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Dissertation (Ph.D. in Electrical Engineering) Naval Postgraduate School, September 1999.<br>"September 1999". Dissertation supervisor(s): Robert Cristi, Murali Tummala. Includes bibliographical references (p. 199-214). Also avaliable online.
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Rivera, velázquez Josué. "Analysis and development of algorithms for data fusion in sensor arrays." Thesis, Montpellier, 2020. http://www.theses.fr/2020MONTS038.

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Actuellement, la plupart des capteurs sont de nature `` intelligente '', ce qui signifie que les éléments de détection et l'électronique associée sont intégrés sur le même circuit. Parmi ces capteurs de nouvelle génération les systèmes micro-électro-mécaniques (MEMS) utilisent les technologies microélectroniques pour la fabrication par lots de capteurs à des volumes sans précédent et à des prix bas. Si ces composants sur étagère sont satisfaisants pour de nombreuses applications nécessitant un niveau de précision faible à moyen, ils ne peuvent toujours pas répondre pleinement aux besoins de pe
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Baravdish, Ninos. "Information Fusion of Data-Driven Engine Fault Classification from Multiple Algorithms." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176508.

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As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding
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Li, Lingjie Luo Zhi-Quan. "Data fusion and filtering for target tracking and identification /." *McMaster only, 2003.

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Bougiouklis, Theodoros C. "Traffic management algorithms in wireless sensor networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FBougiouklis.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Weillian Su. "September 2006." Includes bibliographical references (p. 79-80). Also available in print.
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Ayodeji, Akiwowo. "Developing integrated data fusion algorithms for a portable cargo screening detection system." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9901.

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Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.
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SAVARESE, FRANCESCO. "Data Fusion Methods and Algorithms in the Context of Autonomous Systems - A path planning algorithms analysis and optimization exploiting fused data." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2752655.

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Elbakary, Mohamed Ibrahim. "Novel Pixel-Level and Subpixel-Level Registration Algorithms for Multi-Modal Imagery Data." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1293%5F1%5Fm.pdf&type=application/pdf.

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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.

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Gnanapandithan, Nithya. "Data detection and fusion in decentralized sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2005. http://hdl.handle.net/2097/132.

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Books on the topic "Data fusion algorithms"

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Antony, Richard T. Principles of data fusion automation. Artech House, 1995.

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Abdelgawad, Ahmed, and Magdy Bayoumi. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9.

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Abdelgawad, Ahmed. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012.

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Klein, Lawrence A. Sensor and data fusion concepts and applications. SPIE Optical Engineering Press, 1993.

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Klein, Lawrence A. Sensor and data fusion concepts and applications. 2nd ed. SPIE, 1999.

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V, Dasarathy Belur, and Society of Photo-Optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications: 24-25 April 1997, Orlando, Florida. SPIE, 1997.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications VI: 3-5 April 2002, Orlando, [Florida] USA. SPIE, 2002.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications II: 16-17 April 1998, Orlando, Florida. SPIE, 1998.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2006 : 19-20 April 2006, Kissimmee, Florida, USA. SPIE, 2006.

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2011 : 27-28 April 2011, Orlando, Florida, United States. SPIE, 2011.

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Book chapters on the topic "Data fusion algorithms"

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Abdelgawad, Ahmed, and Magdy Bayoumi. "Proposed Centralized Data Fusion Algorithms." In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9_3.

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Abdelgawad, Ahmed, and Magdy Bayoumi. "Data Fusion in WSN." In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9_2.

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Beltz, Hayley, Timothy Rutledge, Raoul R. Wadhwa, et al. "Ranking Algorithms: Application for Patent Citation Network." In Information Fusion and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03643-0_21.

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Lei, Ming. "Data Association." In Optimal Estimation and Information Fusion: Theory and Algorithms. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3173-5_11.

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Reddy, Dugimpudi Abhishek, Deepak Yadav, Nishi Yadav, and Devendra Kumar Singh. "Impact on Security Using Fusion of Algorithms." In Innovative Data Communication Technologies and Application. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38040-3_65.

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Clark, James J., and Alan L. Yuille. "Data Fusion in Shape From Shading Algorithms." In Data Fusion for Sensory Information Processing Systems. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_7.

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Harris, Chris, Xia Hong, and Qiang Gan. "An introduction to modelling and learning algorithms." In Adaptive Modelling, Estimation and Fusion from Data. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-18242-6_1.

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Clark, James J., and Alan L. Yuille. "Data Fusion Applied to Feature Based Stereo Algorithms." In Data Fusion for Sensory Information Processing Systems. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_5.

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Lei, Rujie, Min Xiang, Peng Wang, Ruiheng Ma, and Kun Yu. "Data Correction-Based Data Fusion Method for Internet of Things Terminals." In Smart Communications, Intelligent Algorithms and Interactive Methods. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5164-9_24.

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Cardille, Jeffrey A., Rylan Boothman, Mary Villamor, Elijah Perez, Eidan Willis, and Flavie Pelletier. "Data Fusion: Merging Classification Streams." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_20.

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AbstractAs the ability to rapidly produce classifications of satellite images grows, it will be increasingly important to have algorithms that can shift through them to separate the signal from inevitable classification noise. The purpose of this chapter is to explore how to update classification time series by blending information from multiple classifications made from a wide variety of data sources. In this lab, we will explore how to update the classification time series of the Roosevelt River found in Fortin et al. (Remote Sens Environ 238, 2020). That time series began with the 1972 laun
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Conference papers on the topic "Data fusion algorithms"

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Flynn, Conor, Christopher Ebersole, and Edmund Zelnio. "The comparability of model fusion to measured data in confuser rejection." In Algorithms for Synthetic Aperture Radar Imagery XXXII, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2025. https://doi.org/10.1117/12.3054007.

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Handke, Sebastian Thomas, Christian Steffes, and Wolfgang Koch. "Comparing Illuminator Selection Algorithms for Mobile Communication Based Passive Radar." In 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2024. https://doi.org/10.1109/sdf63218.2024.10773772.

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Lin, Honghong, Ziquan Liu, Wenxu Chen, et al. "Infrared Heating Anomaly Detection Method Based on Multi-Modal Data Fusion." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019516.

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Cao, Yanqin. "Comparative Analysis and Fusion Strategy Research of Image Generation Algorithms and Video Generation Algorithms." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070501.

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xu, xiaoxiang, FANZHANG LI, and LI ZHANG. "Fusion of data dimensionality reduction algorithms based on category representation theory." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055752.

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Guo, Jiadi. "Multimodal data fusion algorithm for power marketing inspection based on knowledge graph." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051850.

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Yu, Bo, Xiaogang Dong, Zhihua Chen, et al. "Research on knowledge fusion method of heterogeneous data in aerospace control software." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051996.

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Bather, J. "Tracking and data fusion." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010234.

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Julier, S. J. "Fusion without independence." In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications. IEE, 2008. http://dx.doi.org/10.1049/ic:20080050.

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Kurz, Aidan G., Arthur C. Depoian, and Colleen P. Bailey. "Multiband remote 3D-ViT data fusion." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2024. http://dx.doi.org/10.1117/12.3013935.

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Reports on the topic "Data fusion algorithms"

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DVore, Ronald A. New Theory and Algorithms for Scalable Data Fusion. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada587535.

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Wainwright, Martin. New Theory and Algorithms for Scalable Data Fusion. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada588861.

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Yocky, D. A., M. D. Chadwick, S. P. Goudy, and D. K. Johnson. Multisensor data fusion algorithm development. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/172138.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s
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Chou, Fu-Mao. An Algorithm-Level Test Bed for Level-One Data Fusion Research (CASE-ATTI). Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387790.

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Chou, K. C., and A. S. Willsky. Multiscale Riccati Equations and a Two-Sweep Algorithm for the Optimal Fusion of Multiresolution Data. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada459314.

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Brown, Douglas, and Genetha Anne Gray. Implementation of a data fusion algorithm for RODS, a real-time outbreak and disease surveillance system. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/876344.

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Guo, Ge, Jiageng Liu, and Guangheng Liu. Multi-sensor Fusion-based Vehicle Localization. SAE International, 2024. http://dx.doi.org/10.4271/epr2024023.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Multi-sensor fusion (MSF) is believed to be a promising tool for vehicular localization in urban environments. Due to the differences in principles and performance of various onboard vehicle sensors, MSF inevitably suffers from heterogeneous sources and vulnerability to cyber-attacks. Therefore, an essential requirement of MSF is the capability of providing a consumer-grade solution that operates in real-time, is accurate, and immune to abnormal conditions with guaranteed performance and quality of service for location-b
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