Relevant bibliographies by topics / Data fusion techniques

Academic literature on the topic 'Data fusion techniques'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Data fusion techniques"

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Carter, Duane B. "Analysis of Multiresolution Data fusion Techniques." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36609.

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In recent years, as the availability of remote sensing imagery of varying resolution has increased, merging images of differing spatial resolution has become a significant operation in the field of digital remote sensing. This practice, known as data fusion, is designed to enhance the spatial resolution of multispectral images by merging a relatively coarse-resolution image with a higher resolution panchromatic image of the same geographic area. This study examines properties of fused images and their ability to preserve the spectral integrity of the original image. It analyzes five current data fusion techniques for three complex scenes to assess their performance. The five data fusion models used include one spatial domain model (High-Pass Filter), two algebraic models (Multiplicative and Brovey Transform), and two spectral domain models (Principal Components Transform and Intensity-Hue-Saturation). SPOT data were chosen for both the panchromatic and multispectral data sets. These data sets were chosen for the high spatial resolution of the panchromatic (10 meters) data, the relatively high spectral resolution of the multispectral data, and the low spatial resolution ratio of two to one (2:1). After the application of the data fusion techniques, each merged image was analyzed statistically, graphically, and for increased photointerpretive potential as compared with the original multispectral images. While all of the data fusion models distorted the original multispectral imagery to an extent, both the Intensity-Hue-Saturation Model and the High-Pass Filter model maintained the original qualities of the multispectral imagery to an acceptable level. The High-Pass Filter model, designed to highlight the high frequency spatial information, provided the most noticeable increase in spatial resolution.
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Glaab, Enrico. "Analysing functional genomics data using novel ensemble, consensus and data fusion techniques." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12727/.

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Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings.
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Ansari, Abdul Wahab. "The control simulation of tactile sensors using constraint modelling techniques." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357684.

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Delgado, Prieto Miguel. "Contributions to electromechanical systems diagnosis by means data fusion techniques." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/97043.

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Electromechanical drives have traditionally found their field of application in the industrial sector. However, the use of such systems is spreading to other sectors within the field of transport, such as the automotive sector, or to the aircraft sector with the development of the concept of More Electric Aircraft (MEA). One of the major improvements of the MEA concept is related to the actuators of the primary flight controls, where so far only have been considered electrohydraulic actuators, although the current trend is to replace them with electromechanical actuators (EMA). Widespread use, in the future, of EMA in transport systems, is only possible with research and vances in algorithms for detection and diagnosis of faults that may occur both, in the electrical or mechanical parts, in order to ensure the reliability of the drive and the safety of users. During the last years, the study of electro-techanical systems and the fault diagnosis under varying conditions of torque and speed has been mandatory. Although these requirements have been studied deeply by different authors, most of the works are focused on single fault detection. Therefore, there is a lack of diagnosis methods able to detect different kinds of faults in an electro-mechanical actuator. There are very few studies related with diagnosis schemes capable of identifying various faults under different operating conditions, and even less analyzing deeply all the diagnosis chain to face the challenge from all possible perspectives. In this research work, it is proposed the nvestigation towards integral health monitoring schemes for electro-mechanical systems based on pattern recognition. In order to identify various faults under different operating conditions, the health monitoring scheme is developed from a data fusion point of view. The processing of great deals of information enhances the pattern recognition capabilities but, in turn, requires the mplementation of advanced techniques and methodologies. Therefore, first, it is proposed in this research work a review of the whole diagnosis chain, including the different stages (feature calculation, features reduction and classification), the methodologies and techniques. The review finishes by presenting the proposed strategies to take a step further in each diagnosis stage, proposing methodologies to be investigated which would allow a significant advance towards the integral diagnosis systems. In this sense, investigation towards a novel feature calculation methodology able to deal with non-stationary conditions is presented. Next, the feature reduction stage is covered by the proposal of collaborative methodologies by different techniques to improve the significance of the reduced feature set. Also, a more concrete approach is developed by non-lineal techniques, which are not commonly used. Finally, different classification structures are analyzed and novel classification architecture is proposed to be applied in multi-fault diagnosis problems. Experimental analyses are presented resulting from the application of the proposed strategies to different electro-mechanical arrangements. The obtained results achieve high performance levels, and the proposed methodologies can be adapted to the necessary diagnostic requirements. It should be noticed that the proposed contributions increase the information obtained from the system to a better understanding of its behavior and this, has a direct effect over the reliability of the system operation.
Els accionaments electromecànics han tingut tradicionalment el seu camp d'aplicació en el sector industrial. No obstant això l'ús d'aquest tipus de sistemes s'està estenent cap a altres sectors dins l'àmbit dels transports, com el sector de l'automòbil, o el sector de l'aeronàutica, amb el desenvolupament del concepte de l'Avió Més Elèctric (MEA). Una de les millores més importants del concepte MEA està relacionada amb els actuadors dels controls primaris de vol, on fins ara només s'han considerat actuadors electrohidràulics, encara que la tendència actual és reemplaçar-los per actuadors electromecànics (EMA). L'ús generalitzat, en el futur, d'accionaments EMA en sistemes de transport, passa per la investigació i els avenços en els algorismes de detecció i diagnòstic de fallides que es puguin produir, tant en la part elèctrica com en la mecànica, per tal de garantir la fiabilitat de l'accionament i la seguretat dels usuaris. Durant els últims anys, l'estudi de sistemes electromecànics i el diagnòstic de fallides en diverses condicions de parell i de règim de funcionament, han estat estudiats profundament per diferents autors, encara que la majoria dels treballs es centren en la detecció d'una única fallida. Per tant, hi ha una manca de mètodes de diagnòstic capaços de detectar diferents tipus de defectes en un actuador electromecànic. Hi ha molt pocs estudis relacionats amb els sistemes de diagnòstic, capaços d'identificar diverses fallides sota diferents condicions d'operació, i molt menys analitzar profundament tota la cadena de diagnòstic per afrontar el problema des de totes les perspectives possibles. En aquesta tesi, es proposa la investigació sobre tècniques per a la monitorització de condició de sistemes electromecànics, basada en el reconeixement de patrons. Per tal d'identificar diferents fallides sota diferents condicions d'operació, les tècniques propostes s'elaboren sota el prisma de la fusió de dades. El tractament de grans quantitats d'informació, millora els resultats dels algoritmes de reconeixement de patrons, però al seu torn, requereixen de l'aplicació de tècniques i metodologies avançades. Per tant, inicialment es realitza una revisió de la cadena de diagnòstic complerta, incloent les metodologies i tècniques per a les diferents etapes (càlcul d'indicadors, reducció de dimensionalitat i classificació). La revisió finalitza amb la presentació de les estratègies proposades com aportació en cada etapa de diagnòstic. Els resultats obtinguts permeten avenços significatius cap als sistemes de diagnòstic integrals. En aquest sentit, es presenta la investigació sobre metodologies de càlcul d'indicadors en condicions no estacionàries. A continuació, en l'etapa de reducció de dimensionalitat, es proposen metodologies col•laboratives aplicant diferents tècniques que permeten millorar la discriminació de classes, concretament es proposa un enfocament basant-se en tècniques no lineals, que no s'usen habitualment. Finalment, s'analitzen les diferents estructures de classificació i es proposa una arquitectura nova de classificació per ser aplicada en problemes de diagnòstic de múltiples fallides. Es presenten resultats experimentals de les diferents metodologies propostes, per a diferents configuracions electromecàniques. Els resultats obtinguts mostren un alt nivell de rendiment, i les metodologies proposades es poden adaptar als requisits de diagnòstic necessàries en diferents aplicacions. Es conclou que la informació resultant permet una millor comprensió del comportament del sistema sota test, i això té un efecte directe sobre la seva fiabilitat d'operació.
Los accionamientos electromecánicos han tenido tradicionalmente su campo de aplicación en el sector industrial. Sin embargo el uso de este tipo de sistemas se está extendiendo hacia otros sectores dentro del ámbito de los transportes, como el sector del automóvil, o el sector de la aeronáutica con el desarrollo del concepto del Avión Más Eléctrico (MEA). Una de las mejoras más importantes del concepto MEA está relacionada con los actuadores de los controles primarios de vuelo, donde hasta el momento sólo se han considerado actuadores electrohidráulicos, aunque la tendencia actual es remplazarlos por actuadores electromecánicos (EMA). El uso generalizado, en el futuro, de accionamientos EMA en sistemas de transporte, pasa por la investigación y los avances en los algoritmos de detección y diagnóstico de fallos que se puedan producir, tanto en la parte eléctrica como en la mecánica, con el fin de garantizar la fiabilidad del accionamiento y la seguridad de los usuarios. Durante los últimos años, el estudio de sistemas electromecánicos y el diagnóstico de fallos en diversas condiciones de par y de régimen de funcionamiento, han sido estudiados profundamente por diferentes autores, aunque la mayoría de los trabajos se centran en la detección de un único fallo. Por lo tanto, existe una falta de métodos de diagnóstico capaces de detectar diferentes tipos de defectos en un actuador electro-mecánico. Hay muy pocos estudios relacionados con los sistemas de diagnóstico, capaces de identificar diversos fallos bajo diferentes condiciones de operación, y mucho menos analizar profundamente toda la cadena de diagnóstico para afrontar el problema desde todas las perspectivas posibles. En esta tesis, se propone la investigación sobre técnicas para la monitorización de condición de sistemas electromecánicos, basados en el reconocimiento de patrones. Con el fin de identificar diferentes fallos bajo diferentes condiciones de operación, las técnicas propuestas se elaboran bajo el prisma de la fusión de datos. El tratamiento de grandes cantidades de información, mejora los resultados de los algoritmos de reconocimiento de patrones, pero a su vez, requieren de la aplicación de técnicas y metodologías avanzadas. Por lo tanto, inicialmente se realiza una revisión de la cadena de diagnóstico completa, incluyendo las metodologías y técnicas para las diferentes etapas (cálculo de indicadores, reducción de dimensionalidad y clasificación). La revisión finaliza con la presentación de las estrategias propuestas como aportación en cada etapa de diagnóstico. Los resultados obtenidos permiten avances significativos hacia los sistemas de diagnóstico integrales. En este sentido, se presenta la investigación sobre metodologías de cálculo de indicadores en condiciones no estacionarias. A continuación, en la etapa de reducción de dimensionalidad, se proponen metodologías colaborativas aplicando diferentes técnicas que permiten mejorar la discriminación de clases; concretamente se propone un enfoque basándose en técnicas no lineales, que no se usan habitualmente. Finalmente, se analizan las diferentes estructuras de clasificación y se propone una arquitectura novedosa de clasificación para ser aplicada en problemas de diagnóstico de múltiples fallos. Se presentan resultados experimentales de las diferentes metodologías propuestas, para diferentes configuraciones electro-mecánicas. Los resultados obtenidos muestran un alto nivel de rendimiento, y las metodologías propuestas se pueden adaptar a los requisitos de diagnóstico necesarias en diferentes aplicaciones. Se concluye que la información resultante permite una mejor comprensión del comportamiento del sistema bajo test, y esto tiene un efecto directo sobre su fiabilidad de operación.
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MacEwen, Clare. "Can data fusion techniques predict adverse physiological events during haemodialysis?" Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:1ef92d5d-920d-4ff4-b368-5e892527e675.

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Intra-dialytic haemodynamic instability is a common and disabling problem which may lead to morbidity and mortality though repeated organ ischaemia, but it has proven difficult to link any particular blood pressure threshold with hard patient outcomes. The relationship between blood pressure and downstream organ ischaemia during haemodialysis has not been well characterised. Previous attempts to predict and prevent intra-dialytic hypotension have had mixed results, partly due to patient and event heterogeneity. Using the brain as the indicator organ, we aimed to model the dynamic relationship between blood pressure, real-time symptoms, downstream organ ischaemia during haemodialysis, in order to identify the most physiologically grounded, prognostic definition of intra-dialytic decompensation. Following on from this, we aimed to predict the onset of intra-dialytic decompensation using personalised, probabilistic models of multivariate, continuous physiological data, ultimately working towards an early warning system for intra-dialytic adverse events. This was a prospective study of 60 prevalent haemodialysis patients who underwent extensive, continuous physiological monitoring of haemodynamic, cardiorespiratory, tissue oxygenation and dialysis machine parameters for 3-4 weeks. In addition, longitudinal cognitive function testing was performed at baseline and at 12 months. Despite their use in clinical practice, we found that blood pressure thresholds alone have a poor trade off between sensitivity and specificity for predicting downstream tissue ischaemia during haemodialysis. However, the performance of blood pressure thresholds could be improved by stratification for the presence or absence of cerebral autoregulation, and personalising thresholds according to the individual lower limit of autoregulation. For patients without autoregulation, the optimal blood pressure target was a mean arterial pressure (MAP) of 70mmHg. A key finding was that cumulative intra-dialytic exposure to cerebral ischaemia, but not to hypotension per se, corresponded to change in executive cognitive function over 12 months. Therefore we chose cerebral ischaemia as the definition of intra-dialytic decompensation for predictive modelling. We were able to demonstrate that the development of cerebral desaturation could be anticipated from earlier deviations of univariate physiological data from the expected trajectory for a given patient, but sensitivity was limited by the heterogeneity of events even within one individual. The most useful phys- iological data streams included peripheral saturation variance, cerebral saturation variance, heart rate and mean arterial pressure. Multivariate data fusion techniques using these variables created promising personalised models capable of giving an early warning of decompensation. Future work will involve the refinement and prospective testing of these models. In addition, we envisage a prospective study assessing the benefit of autoregulation-guided blood pressure targets on short term outcomes such as patient symptoms and wellbeing, as well as longer term outcomes such as cognitive function.
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SKEPPE, LOVISA. "Classify Swedish bank transactions withearly and late fusion techniques." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156312.

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Categorising bank transactions to predened categories are essential for getting a good overview of ones personal nance. Tink provides a mobile app for automatic categorisation of bank transactions. Tink's categorisation approach is a clustering technique with longest prex match based on merchant.This thesis will examine if a machine learning model can learn to classify transactions based on its purchase, what was bought, instead of merchant.This thesis classies bank transactions in a supervised learning setting by exploring early and late fusion schemes on three types of modalities (text, amount,date) found in Swedish bank transactions. Experiments are carried out with Naive Bayes, Support Vector Machines and Decision Trees. The dierent fusionschemes are compared with no fusion, learned on only one modality, and stacked classication, learning models in a pipe-lined fashion.The early fusion concatenation schemes shows all worse performance than no fusion on the text modality. The late fusion experiments on the other hand shows no impact of modality fusion.Suggestions are made to change the feedback loop from user, to get more data labeled by users, which would potentially boost the other modalities importance
Att sköta sin privatekonomi med hjälp av kategorisering gör nog många människor omedvetet, en försöker helt enkelt få en känsla pa vad en lägger sina pengar på. För att kunna ge full översikt på hur ens privatekonomi ser ut, har Tink skapat en mobilapplikation for att automatiskt kategorisera banktransaktioner. Detta görs just nu med klustering och längsta prex matchning på forsäljningsställe. Kategoriseringen av banktransaktioner ger användaren en direkt återkoppling på hur pengaflödet ser ut samt till vad och när dessa köp görs. Den har uppsatsen kommer att undersoka om en maskininlärningsmodell kan lära sig att klassicera banktransaktioner baserat pa köp istället för försäljningsställe. Genom att undersöka två olika fusioneringsscheman på tre typer av modaliteter funna i banktransaktioner (text, pris och datum), ska vi forsoka uttröna dessa modaliteters påverkan på klassicering. De olika scheman är jamförda med ingen fusionering, dvs inlärning på endast en modalitet, och travad klassicering,dvs inlärning med era efterföljande modeller.Experimenten ar gjorda med supervised-learning och inlärningsmodellerna är Naive Bayes, Support Vector Machines samt Beslutstrad. Experimenten visar på att klassicering på text, alltså försäljningsställe ger bäst resultat i jämförelse med alla de andra experimenten. I de tidiga fusionsexperimenten visar alla modalitet-sammanslagningar sämre resultat än ingen fusion på bara text. De sena fusion experimenten visar å andra sidan ingen skillnad alls efter fusionering med modaliteternas pris och datum. Förslag på förbättrad klassicering på köp antas öka, alltså modaliteternas pris och datum bör vara mer betydande, om mer var datamärkt av användare.
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Rogowski, Justin. "Investigation into automatic identification of bottlenose dolphins using data fusion techniques." Thesis, University of Derby, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506682.

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Mesina, Justin E. "Urban Classification Techniques Using the Fusion of LiDAR and Spectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17420.

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Approved for public release; distribution is unlimited
Combining different types of data from varying sensors has the potential to be more accurate than a single sensor. This research fused airborne LiDAR data and WorldView-2 (WV-2) multispectral imagery (MSI) data to create an improved classification image of urban San Francisco, California. A decision tree scenario was created by extracting features from the LiDAR, as well as NDVI from the multispectral data. Raster masks were created using these features and were processed as decision tree nodes resulting in seven classifications. Twelve regions of interest were created, then categorized and applied to the previous seven classifications via the maximum likelihood classification. The resulting classification images were then combined. A multispectral classification image using the same ROIs was also created for comparison. The fused classification image did a better job of preserving urban geometries than MSI data alone and suffered less from shadow anomalies. The fused results however, were not as accurate in differentiating trees from grasses as using only spectral results. Overall the fused LiDAR and MSI classification performed better than the MSI classification alone but further refinements to the decision tree scheme could probably be made to improve final results.
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De, Gregorio Ludovica. "Development of new data fusion techniques for improving snow parameters estimation." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/245392.

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Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.
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Adusumilli, Srujana. "Development of Statistical Learning Techniques for INS and GPS Data Fusion." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1398772813.

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Books on the topic "Data fusion techniques"

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Hall, David L. Mathematical techniques in multisensor data fusion. Boston: Artech House, 1992.

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L, Hall David. Mathematical techniques in multisensor data fusion. 2nd ed. Boston, MA: Artech House, 2004.

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L, Hall David. Mathematical techniques in multisensor data fusion. Boston: Artech House, 1992.

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Abackerli, A. J. Application of data fusion techniques to thermometry data. Teddington: National Physical Laboratory, 1999.

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L, Hall David. Mathematical techniques in multi-sensor data fusion. 2nd ed. Boston: Artech House, 2004.

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Mitchell, H. B. Image Fusion: Theories, Techniques and Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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service), ScienceDirect (Online, ed. Image fusion: Algorithms and applications. Amsterdam: Academic Press/Elsevier, 2008.

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Multi-sensor data fusion with MATLAB. Boca Raton: Taylor & Francis, 2010.

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

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

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Book chapters on the topic "Data fusion techniques"

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Zhao, Jun, Wei Wang, and Chunyang Sheng. "Data Preprocessing Techniques." In Information Fusion and Data Science, 13–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94051-9_2.

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Semerdjiev, E. "Association Techniques Used in Multisensor Data Fusion." In Multisensor Fusion, 481–95. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_21.

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Goodman, I. R., Ronald P. S. Mahler, and Hung T. Nguyen. "Data Fusion and Standard Techniques." In Mathematics of Data Fusion, 17–89. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8929-1_2.

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Aguilar-Crespo, J. A., J. M. Domínguez, E. de Pablo, and X. Alamán. "Fuzzy Logic Techniques for Sensor Fusion in Real-Time Expert Systems." In Data Fusion Applications, 79–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84990-9_9.

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Figueiras, João. "Data Fusion and Filtering Techniques." In Mobile Positioning and Tracking, 109–33. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119068846.ch5.

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Farrell, Kevin R., and Richard J. Mammone. "Data fusion Techniques for Speaker Recognition." In Modern Methods of Speech Processing, 279–97. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2281-2_12.

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Ogiela, Urszula, Makoto Takizawa, and Lidia Ogiela. "Fusion Techniques for Strong Data Protection." In Lecture Notes in Networks and Systems, 189–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90072-4_19.

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Crowley, James L. "Principles and Techniques for Sensor Data Fusion." In Multisensor Fusion for Computer Vision, 15–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02957-2_2.

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Shanthi, Elizabeth, and D. Sangeetha. "Analyzing Data Through Data Fusion Using Classification Techniques." In Computational Intelligence in Data Mining - Volume 2, 165–73. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_16.

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Zhang, Bo, Yunxiang Yang, Jing Guo, Chenshen Liu, Junjie Liu, Siyuan Chen, and Xiaobin Ning. "A Review of Data Fusion Techniques for Government Big Data." In Big Data, 38–55. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_4.

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Conference papers on the topic "Data fusion techniques"

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Kopp, Luis Filipe, Gabriel Martins, Claudio M. de Farias, Priscila M. V. Lima, and Luiz F. R. C. Carmo. "Sensor Data Prediction techniques for nodes in IoT (poster)." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011429.

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CAI, GUOWEI, and SANKARAN MAHADEVAN. "Data Fusion of Multiple Sensing Techniques." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14093.

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Sahu, Gaurav, and Olga Vechtomova. "Adaptive Fusion Techniques for Multimodal Data." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-main.275.

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Dong, Hairong, and David Evans. "Data-Fusion Techniques and Its Application." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.237.

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Rashinkar, Pratiksha, and V. S. Krushnasamy. "An overview of data fusion techniques." In 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2017. http://dx.doi.org/10.1109/icimia.2017.7975553.

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Stateczny, Andrzej, and Izabela Bodus-Olkowska. "Sensor data fusion techniques for environment modelling." In 2015 16th International Radar Symposium (IRS). IEEE, 2015. http://dx.doi.org/10.1109/irs.2015.7226263.

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Venkataramani, Krithika, Shashwat Mishra, and Lovish Kumar. "Towards automated selection of data fusion techniques." In 2014 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2014. http://dx.doi.org/10.1109/spcom.2014.6983934.

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Stefanello, Debora Rodrigues, Jose Ahirton Batista Lopes Filho, Juan M. Mauricio Villanueva, and Will Ribamar Mendes Almeida. "Respiratory flow estimation using data fusion techniques." In 2012 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC). IEEE, 2012. http://dx.doi.org/10.1109/beiac.2012.6226038.

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Gordon, N., and D. Salmond. "Aspects of target tracking: problems and techniques." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1998. http://dx.doi.org/10.1049/ic:19980419.

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Bossi, Richard H., and James Nelson. "Data fusion for process monitoring and NDE." In Nondestructive Evaluation Techniques for Aging Infrastructure and Manufacturing, edited by Richard H. Bossi and Tom Moran. SPIE, 1996. http://dx.doi.org/10.1117/12.259214.

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Reports on the topic "Data fusion techniques"

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Kouri, Tina. Exploiting Social Media Sensor Networks through Novel Data Fusion Techniques. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1431510.

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Kothuri, Sirisha, Joseph Broach, Nathan McNeil, Kate Hyun, Stephen Mattingly, Md Mintu Miah, Krista Nordback, and Frank Proulx. Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes. Transportation Research and Education Center (TREC), 2022. http://dx.doi.org/10.15760/trec.273.

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Schildknecht, Thomas, and Monika Hager. Quantifying Space Environment Interactions with Debris Objects using Observation Data Fusion Techniques. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada611570.

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
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Knight, R. D., and B. A. Kjarsgaard. Comparative pXRF and Lab ICP-ES/MS methods for mineral resource assessment, Northwest Territories. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331239.

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The Geological Survey of Canada undertook a mineral resource assessment for a proposed national park in northern Canada (~ 33,500 km2) spanning the transition from boreal forest to barren lands tundra. Bedrock geology of this region is complex and includes the Archean Slave Craton, the Archean and Paleoproterozoic Rae domain of the Churchill Province, the Paleoproterozoic Thelon and Taltson magmatic-tectonic zones, and the Paleoproterozoic East Arm sedimentary basin. The area has variable mineral potential for lode gold, kimberlite-hosted diamonds, VMS, vein uranium and copper, SEDEX, as well as other deposit types. A comparison of analytical methods was carried out after processing the field collected samples to acquire both the < 2 mm and for the < 0.063 mm size fractions for 241 surficial sediment (till) samples, collected using a 10 x 10 km grid. Analytical methods comprised: 1) aqua regia followed by ICP-MS analysis, 2) 4-acid hot dissolution followed by ICP-ES/MS analysis, 3) lithium metaborate/tetraborate fusion methods followed by ICP-ES for major elements and ICP-MS for trace elements and, 4) portable XRF on dried, non-sieved sediment samples subjected to a granular segregation processing technique (to produce a clay-silt proxy) for seventeen elements (Ba, Ca, Cr, Cu, Fe, K, Mn, Ni, Pb, Rb, Sr, Th, Ti, U, V, Zn, and Zr) Results indicate that pXRF data do not replicate exactly the laboratory 4-acid and fusion data (in terms of precision and accuracy), but the relationship between the datasets is systematic as displayed in x-y scattergrams. Interpolated single element plots indicate that till samples with anomalies of high and low pXRF concentration levels are synonymous with high and low laboratory-based analytical concentration levels, respectively. The pXRF interpolations thus illustrate the regional geochemical trends, and most importantly, the significant geochemical anomalies in the surficial samples. These results indicate that pXRF spectrometry for a subset of elements is comparable to traditional laboratory methods. pXRF spectrometry also provides the benefit of rapid analysis and data acquisition that has a direct influence on real time sampling designs. This information facilitates efficient and cost-effective field projects (i.e. where used to identify regions of interest for high density sampling), and to prioritize samples to be analyzed using traditional geochemical methods. These tactics should increase the efficiency and success of a mineral exploration and/or environmental sampling programs.
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Barefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, June 2000. http://dx.doi.org/10.32747/2000.7573993.bard.

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The antibotulinal baceriocins, propionicin PLG-1 and jenseniin G., were the first to be identified, purified and characterized for the dairy propionibaceria and are produced by Propionibacterium thoenii P127 and P. thoenii/jensenii P126, respectively. Objectives of this project were to (a) produce polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1; (b) identify, clone and characterize the propionicin PLG-1 (plg-1) and jenseniin G (jnG) genes; and (3) develop gene transfer systems for dairy propionibacteria using them as models. Polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1 were produced in rabbits. Anti-PLG-1 antiserum had high titers (256,000 to 512,000), neutralized PLG-1 activity, and detected purified PLG-1 at 0.10 mg/ml (indirect ELISA) and 0.033 mg/ml (competitive indirect ELISA). Thirty-nine of 158 strains (most P. thoenii or P. jensenii) yielded cross-reacting material; four strains of P. thoenii, including two previously unidentified bacteriocin producers, showed biological activity. Eight propionicin-negative P127 mutants produced neither ELISA response nor biological activity. Western blot analyses of supernates detected a PLG-1 band at 9.1 kDa and two additional protein bands with apparent molecular weights of 16.2 and 27.5 kDa. PLG-1 polyclonal antibodies were used for detection of jenseniin G. PLG-1 antibodies neutralized jenseniin G activity and detected a jenseniin G-sized, 3.5 kDa peptide. Preliminary immunoprecipitation of crude preparations with PLG-1 antibodies yielded three proteins including an active 3-4 kDa band. Propionicin PLG-1 antibodies were used to screen a P. jensenii/thoenii P126 genomic expression library. Complete sequencing of a cloned insert identified by PLG-1 antibodies revealed a putative response regulator, transport protein, transmembrane protein and an open reading frame (ORF) potentially encoding jenseniin G. PCR cloning of the putative plg-1 gene yielded a 1,100 bp fragment with a 355 bp ORF encoding 118 amino acids; the deduced N-terminus was similar to the known PLG-1 N-terminus. The 118 amino acid sequence deduced from the putative plg-1 gene was larger than PLG-1 possibly due to post-translational processing. The product of the putative plg-1 gene had a calculated molecular weight of 12.8 kDa, a pI of 11.7, 14 negatively charged residues (Asp+Glu) and 24 positively charged residues (Arg+Lys). The putative plg-1 gene was expressed as an inducible fusion protein with a six-histidine residue tag. Metal affinity chromatography of the fused protein yielded a homogeneous product. The fused purified protein sequence matched the deduced putative plg-1 gene sequence. The data preliminarily suggest that both the plg-1 and jnG genes have been identified and cloned. Demonstrating that antibodies can be produced for propionicin PLG-1 and that those antibodies can be used to detect, monitor and compare activity throughout growth and purification was an important step towards monitoring PLG-1 concentrations in food systems. The unexpected but fortunate cross-reactivity of PLG-1 antibodies with jenseniin G led to selective recovery of jenseniin G by immunoprecipitation. Further refinement of this separation technique could lead to powerful affinity methods for rapid, specific separation of the two bacteriocins and thus facilitate their availability for industrial or pharmaceutical uses. Preliminary identification of genes encoding the two dairy propionibacteria bacteriocins must be confirmed; further analysis will provide means for understanding how they work, for increasing their production and for manipulating the peptides to increase their target species. Further development of these systems would contribute to basic knowledge about dairy propionibacteria and has potential for improving other industrially significant characteristics.
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