Journal articles: 'Data fusion techniques'

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P., Saranya. "Comparative Study on Different Data Fusion Techniques." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1650–66. http://dx.doi.org/10.37200/ijpr/v24i5/pr201837.

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Castanedo, Federico. "A Review of Data Fusion Techniques." Scientific World Journal 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/704504.

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The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.

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Yu, Hui Ming, Jian Zhong Guo, Yi Cheng, and Qian Lou. "Techniques and Methods of Spatial Data Fusion." Applied Mechanics and Materials 263-266 (December 2012): 3274–78. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.3274.

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Spatial data fusion is an important method of spatial data acquisition. The aim of multisource spatial data integration and fusion is to improve the information precision and information's utilization efficiency. Vector and raster are the two main spatial data structures. This article discusses vector data fusion from of data model fusion, semantic information fusion and coordinates unification, reviews the main methods of raster data fusion and discusses the key technologies of vector and raster data fusion, and proposes the future developments of spatial data fusion technique.

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Alofi, Afnan, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, and Hemalatha M. "A Review of Data Fusion Techniques." International Journal of Computer Applications 167, no. 7 (June 15, 2017): 37–41. http://dx.doi.org/10.5120/ijca2017914318.

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Wong, Pak Chung, Harlan Foote, David L. Kao, Ruby Leung, and Jim Thomas. "Multivariate Visualization with Data Fusion." Information Visualization 1, no. 3-4 (December 2002): 182–93. http://dx.doi.org/10.1057/palgrave.ivs.9500024.

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We discuss a fusion-based visualization method to analyze a multivariate climate dataset and its metadata. The primary difference between a conventional visualization and a fusion-based visualization is that the former draws on a single image whereas the latter draws on multiple see-through layers, which are then overlaid on each other to form the final visualization. We propose optimized colormaps to highlight subtle features that would not be shown with conventional colormaps. We present fusion techniques that integrate multiple single-purpose visualization techniques into the same viewing space. Our highly flexible fusion approach allows scientists to explore multiple parameters concurrently by mixing and matching images without frequently reconstructing new visualizations from the data for every possible combination. Although our primary visualization application is climate modeling, we show with examples that our fundamental design - fusing layers of data images for multivariate visualization - can be generalized for other information visualization applications.

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Jayanthi Kumari, T. R., and H. S. Jayanna. "i-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques." Journal of Intelligent Systems 29, no. 1 (May 3, 2018): 565–82. http://dx.doi.org/10.1515/jisys-2017-0047.

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Abstract In many biometric applications, limited data speaker verification plays a significant role in practical-oriented systems to verify the speaker. The performance of the speaker verification system needs to be improved by applying suitable techniques to limited data condition. The limited data represent both train and test data duration in terms of few seconds. This article shows the importance of the speaker verification system under limited data condition using feature- and score-level fusion techniques. The baseline speaker verification system uses vocal tract features like mel-frequency cepstral coefficients, linear predictive cepstral coefficients and excitation source features like linear prediction residual and linear prediction residual phase as features along with i-vector modeling techniques using the NIST 2003 data set. In feature-level fusion, the vocal tract features are fused with excitation source features. As a result, on average, equal error rate (EER) is approximately equal to 4% compared to individual feature performance. Further in this work, two different types of score-level fusion are demonstrated. In the first case, fusing the scores of vocal tract features and excitation source features at score-level-maintaining modeling technique remains the same, which provides an average reduction approximately equal to 2% EER compared to feature-level fusion performance. In the second case, scores of the different modeling techniques are combined, which has resulted in EER reduction approximately equal to 4.5% compared with score-level fusion of different features.

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Rajan, Deepu, and Subhasis Chaudhuri. "Data fusion techniques for super-resolution imaging." Information Fusion 3, no. 1 (March 2002): 25–38. http://dx.doi.org/10.1016/s1566-2535(01)00044-6.

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Crowley, James L., and Yves Demazeau. "Principles and techniques for sensor data fusion." Signal Processing 32, no. 1-2 (May 1993): 5–27. http://dx.doi.org/10.1016/0165-1684(93)90034-8.

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Yin, Hu, Yun Fei Lv, and Wei Wei Wang. "Reacher in Users Recommended of Social Data." Applied Mechanics and Materials 303-306 (February 2013): 2416–24. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2416.

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We discuss some key techniques associated with integrating user social data recommendation into entity search engine, which can provide entity search engine more accurate information and make up for automatically fetching information on Web. The goal of social data recommendation is to make search engine become a content provider, and solve some challenges that traditional architecture of search engine has faced with, such as limited resources, accurate search, etc. To this end, we describe the storage format of the user social recommended data and submission methods for them. For the purpose of fusing this structural information into entity search engine, we present formal definitions related to Web entity fusion, and give several important fusion operators, and discuss their properties. Finally, we propose a Web entity fusion algorithm, which exploits some techniques related to natural language processing such as sentence similarity computation and sentence fusion. Our experimental results show that the proposed algorithms are effective.

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Jing, Ren, and Zhao Xu. "Visualization of Traffic Data Using View Fusion Techniques." British Journal of Applied Science & Technology 17, no. 1 (January 10, 2016): 1–6. http://dx.doi.org/10.9734/bjast/2016/27685.

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Reinhart Kühne, D. "Data Fusion Techniques for Advanced Traffic Control Systems." IFAC Proceedings Volumes 33, no. 9 (June 2000): 337–42. http://dx.doi.org/10.1016/s1474-6670(17)38168-5.

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Friedrich, Bernhard, Irina Matschke, Essam Almasri, and Jürgen Mück. "Data Fusion Techniques for Adaptive Traffic Signal Control." IFAC Proceedings Volumes 36, no. 14 (August 2003): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)32396-0.

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Faouzi, Nour-Eddin El, and Lawrence A. Klein. "Data Fusion for ITS: Techniques and Research Needs." Transportation Research Procedia 15 (2016): 495–512. http://dx.doi.org/10.1016/j.trpro.2016.06.042.

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Zhu, Jubo. "Data fusion techniques for incomplete measurement of trajectory." Chinese Science Bulletin 46, no. 8 (April 2001): 627–30. http://dx.doi.org/10.1007/bf03182820.

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Cordova Neira, Manuel Alberto, Pedro Ribeiro Mendes Junior, Anderson Rocha, and Ricardo Da Silva Torres. "Data-Fusion Techniques for Open-Set Recognition Problems." IEEE Access 6 (2018): 21242–65. http://dx.doi.org/10.1109/access.2018.2824240.

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Gros, X. E., Zheng Liu, K. Tsukada, and K. Hanasaki. "Experimenting with pixel-level NDT data fusion techniques." IEEE Transactions on Instrumentation and Measurement 49, no. 5 (2000): 1083–90. http://dx.doi.org/10.1109/19.872934.

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Aftab, Shabib, Sagheer Abbas, Taher M. Ghazal, Munir Ahmad, Hussam Al Hamadi, Chan Yeob Yeun, and Muhammad Adnan Khan. "A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion." Mathematics 11, no. 3 (January 26, 2023): 632. http://dx.doi.org/10.3390/math11030632.

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This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.

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Liu, Tieming. "Software Vulnerability Mining Techniques Based on Data Fusion and Reverse Engineering." Wireless Communications and Mobile Computing 2022 (April 23, 2022): 1–6. http://dx.doi.org/10.1155/2022/4329034.

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Software vulnerability mining is an important component of network attack and defense technology. To address the problems of high leakage rate and false positive rate of existing static analysis methods, this paper proposes a static analysis vulnerability detection technique based on data fusion for source code. By parsing the analysis results of different detection methods and fusing the data, the technique can effectively reduce the false positive rate and the false positive rate. A prototype of a scalable source code static analysis tool is designed and implemented, which can be automatically optimized by user feedback. Finally, an example is given to demonstrate how to uncover buffer overflow software vulnerabilities in the helpctr program based on reverse engineering techniques. The experimental results show that the false positive and false negative rates are significantly reduced compared to individual vulnerability detection methods.

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Pfeffer, Max, André Uschmajew, Adriana Amaro, and Ulrich Pfeffer. "Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma." Cancers 11, no. 10 (September 26, 2019): 1434. http://dx.doi.org/10.3390/cancers11101434.

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Uveal melanoma (UM) is a rare cancer that is well characterized at the molecular level. Two to four classes have been identified by the analyses of gene expression (mRNA, ncRNA), DNA copy number, DNA-methylation and somatic mutations yet no factual integration of these data has been reported. We therefore applied novel algorithms for data fusion, joint Singular Value Decomposition (jSVD) and joint Constrained Matrix Factorization (jCMF), as well as similarity network fusion (SNF), for the integration of gene expression, methylation and copy number data that we applied to the Cancer Genome Atlas (TCGA) UM dataset. Variant features that most strongly impact on definition of classes were extracted for biological interpretation of the classes. Data fusion allows for the identification of the two to four classes previously described. Not all of these classes are evident at all levels indicating that integrative analyses add to genomic discrimination power. The classes are also characterized by different frequencies of somatic mutations in putative driver genes (GNAQ, GNA11, SF3B1, BAP1). Innovative data fusion techniques confirm, as expected, the existence of two main types of uveal melanoma mainly characterized by copy number alterations. Subtypes were also confirmed but are somewhat less defined. Data fusion allows for real integration of multi-domain genomic data.

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Chhabra, Sakshi, and Dinesh Singh. "Data Fusion and Data Aggregation/Summarization Techniques in WSNs: A Review." International Journal of Computer Applications 121, no. 19 (July 18, 2015): 21–30. http://dx.doi.org/10.5120/21648-4755.

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Robitaille, Nicolas, and Simon Duchesne. "Label Fusion Strategy Selection." International Journal of Biomedical Imaging 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/431095.

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Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

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Navada, Bhagya, and Santhosh Venkata. "Filter design using data fusion for a pneumatic control valve." Serbian Journal of Electrical Engineering 18, no. 1 (2021): 49–61. http://dx.doi.org/10.2298/sjee2101049n.

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This paper presents a filter design technique for a pneumatic valve using data fusion techniques. The objective of this paper is to examine the suppression of the effect of parameters causing deviation from normal system performance using the technique of data fusion over time. The output of a system affected by inherited noise is processed by applying operations such as finding the statistical variance, time warping, interpolation, and extrapolation. These techniques are used to compute the transfer function of the filter, which when cascaded with the system will suppress the effect of noise on the process. The operation of the control valve is affected by characteristics such as stiction, structural deformation, etc. The characteristics of the system are studied and data for multiple time instances are extracted to carry out fusion across time by dynamic time warping. Tests show that the filter presented here can suppress the effects of stiction and mechanical deformation on the output signal.

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Krishnamurthi, Rajalakshmi, Adarsh Kumar, Dhanalekshmi Gopinathan, Anand Nayyar, and Basit Qureshi. "An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques." Sensors 20, no. 21 (October 26, 2020): 6076. http://dx.doi.org/10.3390/s20216076.

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Chen, Huiying, and Youfu Li. "Data fusion for three-dimensional tracking using particle techniques." Optical Engineering 47, no. 1 (2008): 016401. http://dx.doi.org/10.1117/1.2835013.

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Theoharatos, C., A. Makedonas, N. Fragoulis, V. Tsagaris, and S. Costicoglou. "DETECTION OF SHIP TARGETS IN POLARIMETRIC SAR DATA USING 2D-PCA DATA FUSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1017–24. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1017-2015.

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Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

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Kumar, Umesh, Neha Gopaliya, Uma Sharma, and Sandeep Gupta. "Discrete Transform Based Image Fusion." International Journal of Multimedia Data Engineering and Management 8, no. 2 (April 2017): 43–49. http://dx.doi.org/10.4018/ijmdem.2017040105.

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With the advancement of image processing, the distinct area of image fusion has been explored. The word fusion represents a way of obtaining data acquired in several domains. A technique of merging useful data from input images is defined as image fusion. It improves features and performance. Fused image includes all the important features of input images without introducing any artifacts. This paper depicts the basic of image fusion and fusion techniques. Paper mainly focuses on frequency domain techniques. Image fusion widely used in surveillance, medical diagnosis, biometric, enhanced vision system and remote sensing.

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Zhang, Li-Chun. "On Proxy Variables and Categorical Data Fusion." Journal of Official Statistics 31, no. 4 (December 1, 2015): 783–807. http://dx.doi.org/10.1515/jos-2015-0045.

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Abstract The problem of inference about the joint distribution of two categorical variables based on knowledge or observations of their marginal distributions, to be referred to as categorical data fusion in this paper, is relevant in statistical matching, ecological inference, market research, and several other related fields. This article organizes the use of proxy variables, to be distinguished from other auxiliary variables, both in terms of their effects on the uncertainty of fusion and the techniques of fusion. A measure of the gains of efficiency is provided, which incorporates both the identification uncertainty associated with data fusion and the sampling uncertainty that arises when the theoretical bounds of the uncertainty space are unknown and need to be estimated. Several existing techniques for generating fusion distributions (or datasets) are described and some new ones proposed. Analysis of real-life data demonstrates empirically that proxy variables can make data fusion more precise and the constructed fusion distribution more plausible.

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Pires, Ivan, Nuno Garcia, Nuno Pombo, and Francisco Flórez-Revuelta. "From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices." Sensors 16, no. 2 (February 2, 2016): 184. http://dx.doi.org/10.3390/s16020184.

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This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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Jain, Vinayak. "An Overview on Data Mining and Data Fusion." Indian Journal of Data Mining 3, no. 1 (May 30, 2023): 1–5. http://dx.doi.org/10.54105/ijdm.a1624.053123.

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Strong adoption of Internet and Communication technologies across industries in the last two decades has led to large-scale digitization of business processes. While this has helped in the instant availability of information, over the period, the source and amount of this information have increased multi-fold giving rise to Big Data. With the increase in volume, the relevance of data in its raw format continues to decrease over time. According to HACE Theorem, Big Data has autonomous sources being distributed and decentralized data in a complex relationship with each other. Making sense of this ever-growing large pool of data has become increasingly difficult and has created a new problem waning the initial gains made via the digitization of systems and processes. This gave rise to the evolution of multiple Data Mining techniques that have helped to classify large volumes of data into relevant segments and drive value to help provide meaningful information. To extract and discover knowledge from data, Knowledge Discovering Databases (KDD) help in the refining of data. This paper discusses various data mining techniques that help to identify patterns and relationships to help make business decisions using data analysis. Furthermore, the Data Fusion method is reviewed which deals with joint analysis of multiple inter-related datasets providing multiple complementary views to help further with precise decision-making.

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Kumari, T. R. Jayanthi, and H. S. Jayanna. "Limited Data Speaker Verification: Fusion of Features." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3344. http://dx.doi.org/10.11591/ijece.v7i6.pp3344-3357.

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The present work demonstrates experimental evaluation of speaker verification for different speech feature extraction techniques with the constraints of limited data (less than 15 seconds). The state-of-the-art speaker verification techniques provide good performance for sufficient data (greater than 1 minutes). It is a challenging task to develop techniques which perform well for speaker verification under limited data condition. In this work different features like Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Delta (4), Delta-Delta (44), Linear Prediction Residual (LPR) and Linear Prediction Residual Phase (LPRP) are considered. The performance of individual features is studied and for better verification performance, combination of these features is attempted. A comparative study is made between Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) through experimental evaluation. The experiments are conducted using NIST-2003 database. The experimental results show that, the combination of features provides better performance compared to the individual features. Further GMM-UBM modeling gives reduced equal error rate (EER) as compared to GMM.

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Veitch-Michaelis, J., J. P. Muller, J. Storey, D. Walton, and M. Foster. "DATA FUSION OF LIDAR INTO A REGION GROWING STEREO ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 107–12. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-107-2015.

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Stereo vision and LIDAR continue to dominate standoff 3D measurement techniques in photogrammetry although the two techniques are normally used in competition. Stereo matching algorithms generate dense 3D data, but perform poorly on low-texture image features. LIDAR measurements are accurate, but imaging requires scanning and produces sparse point clouds. Clearly the two techniques are complementary, but recent attempts to improve stereo matching performance on low-texture surfaces using data fusion have focused on the use of time-of-flight cameras, with comparatively little work involving LIDAR. A low-level data fusion method is shown, involving a scanning LIDAR system and a stereo camera pair. By directly imaging the LIDAR laser spot during a scan, unique stereo correspondences are obtained. These correspondences are used to seed a regiongrowing stereo matcher until the whole image is matched. The iterative nature of the acquisition process minimises the number of LIDAR points needed. This method also enables simple calibration of stereo cameras without the need for targets and trivial coregistration between the stereo and LIDAR point clouds. Examples of this data fusion technique are provided for a variety of scenes.

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Li, Guoquan, Zheng Yan, Yulong Fu, and Hanlu Chen. "Data Fusion for Network Intrusion Detection: A Review." Security and Communication Networks 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/8210614.

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Rapid progress of networking technologies leads to an exponential growth in the number of unauthorized or malicious network actions. As a component of defense-in-depth, Network Intrusion Detection System (NIDS) has been expected to detect malicious behaviors. Currently, NIDSs are implemented by various classification techniques, but these techniques are not advanced enough to accurately detect complex or synthetic attacks, especially in the situation of facing massive high-dimensional data. Besides, the inherent defects of NIDSs, namely, high false alarm rate and low detection rate, have not been effectively solved. In order to solve these problems, data fusion (DF) has been applied into network intrusion detection and has achieved good results. However, the literature still lacks thorough analysis and evaluation on data fusion techniques in the field of intrusion detection. Therefore, it is necessary to conduct a comprehensive review on them. In this article, we focus on DF techniques for network intrusion detection and propose a specific definition to describe it. We review the recent advances of DF techniques and propose a series of criteria to compare their performance. Finally, based on the results of the literature review, a number of open issues and future research directions are proposed at the end of this work.

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Valiente, David, Luis Payá, José M. Sebastián, Luis M. Jiménez, and Oscar Reinoso. "Dynamic Catadioptric Sensory Data Fusion for Visual Localization in Mobile Robotics." Proceedings 15, no. 1 (July 5, 2019): 2. http://dx.doi.org/10.3390/proceedings2019015002.

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This approach presents a localization technique within mobile robotics sustained by visual sensory data fusion. A regression inference framework is designed with the aid of informative data models of the system, together with support of probabilistic techniques such as Gaussian Processes. As a result, the visual data acquired with a catadioptric sensor is fused between poses of the robot in order to produce a probability distribution of visual information in the 3D global reference of the robot. In addition, a prediction technique based on filter gain is defined to improve the matching of visual information extracted from the probability distribution. This work reveals an enhanced matching technique for visual information in both, the image reference frame, and the 3D global reference. Real data results are presented to confirm the validity of the approach when working in a mobile robotic application for visual localization. Besides, a comparison against standard visual matching techniques is also presented. The suitability and robustness of the contributions are tested in the presented experiments.

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Ghaffar, M. A. A., A. McKinstry, T. Maul, and T. T. Vu. "DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE SUPER-RESOLUTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 47–54. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-47-2019.

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Abstract. Data augmentation is a well known technique that is frequently used in machine learning tasks to increase the number of training instances and hence decrease model over-fitting. In this paper we propose a data augmentation technique that can further boost the performance of satellite image super resolution tasks. A super-resolution convolutional neural network (SRCNN) was adopted as a state-of-the-art deep learning model to test the proposed data augmentation technique. Different augmentation techniques were studied to investigate their relative importance and accuracy gains. We categorized the augmentation methods into instance based and channel based augmentation methods. The former refers to the standard approach of creating new data instances through applying image transformations to the original images such as adding artificial noise, rotations and translations to training samples, while in the latter we fuse auxiliary channels (or custom bands) with each training instance, which helps the model learn useful representations. Fusing auxiliary derived channels to a satellite image RGB combination can be seen as a spectral-spatial fusion process as we explain later. Several experiments were carried out to evaluate the efficacy of the proposed fusion-based augmentation method compared with traditional data augmentation techniques such as rotation, flip and noisy training inputs. The reconstruction quality of the high resolution output was quantitatively evaluated using Peak-Signal-To-Noise-Ratio (PSNR) and qualitatively through visualisation of test samples before and after super-resolving.

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Gupta, Abhishek, and Xavier Fernando. "Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges." Drones 6, no. 4 (March 28, 2022): 85. http://dx.doi.org/10.3390/drones6040085.

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This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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Tulay, Emine Elif, Barış Metin, Nevzat Tarhan, and Mehmet Kemal Arıkan. "Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases." Clinical EEG and Neuroscience 50, no. 1 (June 20, 2018): 20–33. http://dx.doi.org/10.1177/1550059418782093.

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Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers—especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification—especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

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Nada, Derradji, Mounir Bousbia-Salah, and Maamar Bettayeb. "EKF Based Fusion Techniques Applied to Wheelchair Navigation System." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (August 23, 2019): 304–16. http://dx.doi.org/10.2174/1570179415666180709125132.

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Background: The aim of this paper is to investigate data fusion techniques based on an Extended Kalman Filter (EKF), and more specifically, the nonlinear dynamic estimation of a wheelchair navigation system. Methods: Three data fusion techniques are presented and a comparison between them is studied. It combines the noisy measurement data coming from several sensors to obtain the best estimate of position while reducing the measurement uncertainties. Results: By using the MATLAB, the performance of these techniques is checked with simulated data and performance metrics are calculated for evaluation of the algorithms. Detailed mathematical expressions are provided which could be useful for algorithm implementation. Conclusion: The results show that the algorithm based on a measurement fusion technique gives a good estimate when compared with another one.

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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 (December 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 work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.

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Aqlan, Hesham Abdo Ahmed, Shoiab Ahmed, Ajit Danti, and S. N. Bharat. "Score Level Fusion Based Death Prediction using Data Mining Techniques." IOSR Journal of Computer Engineering 19, no. 02 (April 2017): 19–24. http://dx.doi.org/10.9790/0661-1902041924.

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., Ezhili G. "BUILDING EXTRACTION FROM REMOTE SENSING IMAGERIES BY DATA FUSION TECHNIQUES." International Journal of Research in Engineering and Technology 02, no. 03 (March 25, 2013): 347–50. http://dx.doi.org/10.15623/ijret.2013.0203021.

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Libby, E. W., and P. S. Maybeck. "Sequence comparison techniques for multisensor data fusion and target recognition." IEEE Transactions on Aerospace and Electronic Systems 32, no. 1 (January 1996): 52–65. http://dx.doi.org/10.1109/7.481249.

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Nikolakopoulos, Konstantinos G. "Comparison of Nine Fusion Techniques for Very High Resolution Data." Photogrammetric Engineering & Remote Sensing 74, no. 5 (May 1, 2008): 647–59. http://dx.doi.org/10.14358/pers.74.5.647.

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Silvestri, Michele, Lucia Bertacchini, Caterina Durante, Andrea Marchetti, Elisa Salvatore, and Marina Cocchi. "Application of data fusion techniques to direct geographical traceability indicators." Analytica Chimica Acta 769 (March 2013): 1–9. http://dx.doi.org/10.1016/j.aca.2013.01.024.

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Liu, Zheng, David S. Forsyth, Jerzy P. Komorowski, Koichi Hanasaki, and Thia Kirubarajan. "Survey: State of the Art in NDE Data Fusion Techniques." IEEE Transactions on Instrumentation and Measurement 56, no. 6 (December 2007): 2435–51. http://dx.doi.org/10.1109/tim.2007.908139.

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Peukert, Dylan, Chaoshui Xu, and Peter Dowd. "A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion." Minerals 12, no. 11 (October 27, 2022): 1364. http://dx.doi.org/10.3390/min12111364.

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Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by discarding waste prior to further processing. In this literature review, recent investigations of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated to inform optimal sensor selection. Additionally, the fusing of data from multiple sensing techniques to improve characterization of the sensed material and hence sorting capability is investigated. It was found that the key to effective implementation of sensor-based sorting is the selection of a sensing technique which can sense a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classes of potential sensor fusion sorting applications in mineral processing are proposed and illustrated with example cases. It was also determined that the main holdup for implementing sensor fusion is a lack of correlatable data on the response of multiple sensing techniques for the same ore sample. A combined approach of experimental testing supplemented by simulations is proposed to provide data to enable the evaluation and development of sensor fusion techniques.

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Amaro, Adriana, Max Pfeffer, Ulrich Pfeffer, and Francesco Reggiani. "Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma." Biomedicines 10, no. 12 (December 13, 2022): 3240. http://dx.doi.org/10.3390/biomedicines10123240.

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There is a growing number of multi-domain genomic datasets for human tumors. Multi-domain data are usually interpreted after separately analyzing single-domain data and integrating the results post hoc. Data fusion techniques allow for the real integration of multi-domain data to ideally improve the tumor classification results for the prognosis and prediction of response to therapy. We have previously described the joint singular vector decomposition (jSVD) technique as a means of data fusion. Here, we report on the development of these methods in open source code based on R and Python and on the application of these data fusion methods. The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset was used as a benchmark to evaluate the potential of the data fusion approaches to the improve molecular classification of cancers in a clinically relevant manner. Our data show that the data fusion approach does not generate classification results superior to those obtained using single-domain data. Data from different domains are not entirely independent from each other, and molecular classes are characterized by features that penetrate different domains. Data fusion techniques might be better suited for response prediction, where they could contribute to the identification of predictive features in a domain-independent manner to be used as biomarkers.

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Pise, A. Audumbar, and Radhika Kapshikar. "Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques." Fusion: Practice and Applications 8, no. 2 (2022): 51–70. http://dx.doi.org/10.54216/fpa.080205.

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Target detection using multi fusion data is one of the common techniques used in military as well as defence units. The usage of a wide variety of sensors is now possible due to modern data fusion technology. The major problem is the existing multi-sensor fusion technique is loss of data and delay is message transfer. To overcome the existing problems, proposed work includes optimization, machine learning, and soft computing techniques. Multi Sensor Data Fusion (MSDF) is becoming an increasingly significant field of study and is being explored by a broad range of individuals. Data defects, outliers, misleading data, conflicting data, and data association are some data fusion concerns. In addition to the statistical advantages of more independent observations, the precision of an observation may be improved by using a variety of different types of sensors. Target tracking has earned a lot of attention in recent years in the realm of surveillance and measurement systems, particularly those in which the state of a target is approximated based on measurements. Academics as well as implementers in the fields of radar, sonar, and satellite surveillance are interested in the bearings-only tracking (BOT) problem. The BOT is the sole option available in many surveillance systems, such as those found aboard submarines. Significant difficulties arise because of the constrained observability of target states based only on bearing measurements. The work that is suggested tackles the limitations of EKF and its derivatives in controlling MSDF within the context of BOT. Specifically, the study identifies divergence as a primary challenge and works to devise solutions for it. It is recommended that two key methods of fusion, data level and feature level (or state level), be investigated in depth. This is in recognition of the fact that the MSDF may increase observability, thereby reducing the tendency of the tracking algorithm to diverge and realizing a better estimate of the states. The Information Filter, which is a casting of the Kalman Filter, and its expansions are employed via extensive simulation to lessen the influence of initial assumptions on the convergence of MSDF tracking algorithms. This is accomplished by using the Kalman Filter.

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Fassett, Daniel R., Ronald I. Apfelbaum, and John A. Hipp. "Comparison of fusion assessment techniques: computer-assisted versus manual measurements." Journal of Neurosurgery: Spine 8, no. 6 (June 2008): 544–47. http://dx.doi.org/10.3171/spi/2008/8/6/544.

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Object Fusion assessment after cervical arthrodesis can be subjective. Measures such as bridging bone quantification or extent of (limited) motion on dynamic studies are common but difficult to interpret and fraught with biases. We compared manual measurement and computer-assisted techniques in assessing fusion after anterior cervical discectomy and fusion (ACDF). Methods One hundred patients who underwent ACDF (512 intervertebral levels) were randomly selected for this radiographic review (follow-up 3–36 months). Two assessment techniques were performed by different observers, with each blinded to the results of the other. The manual spinous process displacement measurement technique was used to calculate motion between the spinous processes under magnification on a digital imaging workstation. Computer-assisted measurements of intervertebral angular motion were made using Quantitative Motion Analysis (QMA) software. Fusion criteria were arbitrarily set at 1 mm of motion for the manual technique and 1.5° of angular motion for the QMA technique. Results The manual measurement technique revealed fusion in 61.7% (316 of 512) of the interspaces assessed, and QMA revealed fusion in 64.3% (329 of 512). These two assessment techniques agreed in 87.5% of cases, with a correlation coefficient of 0.68 between the two data sets. In cases in which the two techniques did not agree, QMA revealed fusion and the manual measurement revealed nonfusion in 64% of the disagreements; 98% of the disagreements occurred when motion was < 2 mm or 2°. Conclusions Although osseous fusion after arthrodesis remains difficult to assess, new computer-assisted techniques may remove the subjectivity generally associated with assessing fusion.

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Scheibenreif, L., M. Mommert, and D. Borth. "CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 705–11. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-705-2022.

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Abstract. Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self-supervised learning. Deep neural network models are trained to maximize the similarity of latent representations obtained with different sensing techniques from the same location, while distinguishing them from other locations. We showcase this idea with two self-supervised data fusion methods and compare against standard supervised and self-supervised learning approaches on a land-cover classification task. Our results show that contrastive data fusion is a powerful self-supervised technique to train image encoders that are capable of producing meaningful representations: Simple linear probing performs on par with fully supervised approaches and fine-tuning with as little as 10% of the labelled data results in higher accuracy than supervised training on the entire dataset.

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Mehrpouya, Mehrshad, Daniel Tuma, Tom Vaneker, Mohamadreza Afrasiabi, Markus Bambach, and Ian Gibson. "Multimaterial powder bed fusion techniques." Rapid Prototyping Journal 28, no. 11 (March 15, 2022): 1–19. http://dx.doi.org/10.1108/rpj-01-2022-0014.

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Purpose This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels. Design/methodology/approach This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review. Findings The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies. Originality/value This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.

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Journal articles on the topic 'Data fusion techniques'

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