Дисертації з теми "Multisensor"
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Filippidis, Arthur. "Multisensor data fusion." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09ENS/09ensf482.pdf.
Повний текст джерелаPurohit, Madhavi. "Multisensor integration for a robot." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182456473.
Повний текст джерелаPetrovic, Vladimir. "Multisensor pixel-level image fusion." Thesis, University of Manchester, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715412.
Повний текст джерелаPradhan, Pushkar S. "Multiresolution based, multisensor, multispectral image fusion." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-07082005-140541.
Повний текст джерелаJeong, Soonho Tugnait Jitendra K. "Topics in multisensor maneuvering target tracking." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Summer/doctoral/JEONG_SOONHO_43.pdf.
Повний текст джерелаBonaccorso, Filippo. "Multisensor data fusion for robotic control." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1081.
Повний текст джерелаPINARDI, STEFANO. "Movements recognition with intelligent multisensor analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19297.
Повний текст джерелаRødningsby, Anders. "Multitarget Multisensor Trackingin the Presence of Wakes." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11913.
Повний текст джерелаBerg, Timothy Martin. "Model distribution in decentralized multisensor data fusion." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317852.
Повний текст джерелаHilmersson, Anette. "Multisensor Stress Monitoring For Non-Stationary Subjects." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28340.
Повний текст джерелаAtt mäta stress i realtid i verkliga situationer kan vara fördelaktigt för flera applikationer. Det som har legat som grund för denna uppsats är att kunna mäta stress under ADHD diagnostisering. Genom att kombinera de vanliga testerna med stressnivåer hos patienten hoppas man kunna utveckla nya metoder för diagnostisering. Att mäta fera parametrar samtidigt i realtid är inte något ofta utförs idag. För att komma igång har fera kroppsliga funktioner som påverkas på olika sätt av stress studerats. Alla dessa funktioner kan inte inkluderas i det system som önskas konstrueras antingen på grund av systemets karaktär eller på grund tidsbrist. Efter att undersökningen var klar beslutades det att konstruera två moduler. Den första använder en mätteknik som kallas PPG och används för att mäta hjärtfrekvens, även andningsfrekvensen estimeras och modulen är förberedd för att estimera blodsyre nivåa men signalbehandling och validering för detta är inte gjord. Den andra modulen mäter resistans i huden. I dessa moduler lades även till en temperatur sensor för att mäta hudtemperaturen. Tidsbrist har gjort att endast en av dem två modulerna kunnat realiserats. Den som realiserat är PPG modulen och modulen för hudresistans presenteras endast teoretiskt. PPG modulen genomför matningen med en transmissiv teknik på vänster öra och ger ut en acceptabel signal kvalité om sensorn får bra kontakt. Arbetet är avgränsat och inkluderar inte signalanalysen av signalen däremot visualiseras signalen i figurer.
Wellington, Sean. "Algorithms for sensor validation and multisensor fusion." Thesis, Southampton Solent University, 2002. http://ssudl.solent.ac.uk/398/.
Повний текст джерелаPrajitno, Prawito. "Neuro-fuzzy methods in multisensor data fusion." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251258.
Повний текст джерелаRemund, Quinn P. "Multisensor microwave remote sensing in the cryosphere /." Diss., CLICK HERE for online access, 2000. http://contentdm.lib.byu.edu/ETD/image/etd7.pdf.
Повний текст джерелаRemund, Quinn P. "Multisensor Microwave Remote Sensing in the Cryosphere." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/72.
Повний текст джерелаKlaus, Ferdinand. "Einführung in Techniken und Methoden der Multisensor-Datenfusion." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971151989.
Повний текст джерелаRobl, Christian. "Aufgabenorientierte Kopplung von Sensoren mit unterschiedlichen Abtasteigenschaften." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=959771166.
Повний текст джерелаDittmann, Brigitte. "Entwicklung und Realisierung eines Sensorsystems auf massenspektrometrischer Basis." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=96214715X.
Повний текст джерелаRühl, Thorsten. "Studien zur verbesserten Ausnutzung des Informationsgehaltes von Multisensorsystemen." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963710591.
Повний текст джерелаJia, Huamin. "Data fusion methodologies for multisensor aircraft navigation systems." Thesis, Cranfield University, 2004. http://hdl.handle.net/1826/781.
Повний текст джерелаKhider, Mohammed [Verfasser]. "Multisensor-Based Positioning for Pedestrian Navigation / Mohammed Khider." München : Verlag Dr. Hut, 2014. http://d-nb.info/1047994984/34.
Повний текст джерелаFernaÌndez, Mariano. "Failure detection and isolation in decentralised multisensor systems." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260155.
Повний текст джерелаTerpening, Brian Everett. "Application of Kalman filter on multisensor fusion tracking." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/26384.
Повний текст джерелаThe use of Kalman filtering in tracking targets and the reconstruction of a target's track are addressed in two separate fusion schemes. First, the Kalman filter is used to provide estimates of the position and velocity of a target based upon observations of the target's bearing. Two sensors, a radar in receive mode and an infra-red sensor, take bearings to the target at different scan rates. This information is then fused within the filter to obtain the target's track. Secondly, range, bearing, and frequency are used in fusion. Kalman filtering, Kalman smoothing, and maneuver detection are all used in the reconstruction of a target's track. Improvements are implemented in the method of forcing the excitation matrix and the results documented.
Escamilla-Ambrosio, Ponciano Jorge. "Intelligent adaptive multisensor data fusion using hybrid architectures." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398626.
Повний текст джерелаHinton, Andrew. "The development of multisensor arrays utilising conducting polymers." Thesis, Sheffield Hallam University, 1997. http://shura.shu.ac.uk/19796/.
Повний текст джерелаZouba, Valentin Nadia. "Multisensor fusion for monitoring elderly activities at home." Nice, 2010. http://www.theses.fr/2010NICE4001.
Повний текст джерелаIn this thesis, an approach combining heterogeneous sensor data for recognizing elderly activities at home is proposed. This approach consists in combining data provided by video cameras with data provided by environmental sensors to monitor the interaction of people with the environment. The first contribution is a new sensor model able to give a coherent and efficient representation of the information provided by various types of physical sensors. This sensor model includes an uncertainty in sensor measurement. The second contribution is a multisensor based activity recognition approach. This approach consists in detecting people, tracking people as they move, recognizing human postures and recognizing activities of interest based on multisensor analysis and human activity recognition. To address the problem of heterogeneous sensor system, we choose to perform fusion at the high-level (event level) by combining video events with environmental events. The third contribution is the extension of a description language which lets users (i. E. Medical staff) to describe the activities of interest into formal models. The results of this approach are shown for the recognition of ADLs of real elderly people evolving in an experimental apartment called Gerhome equipped with video sensors and environmental sensors. The obtained results of the recognition of the different ADLs are encouraging
Gunatilaka, Ajith Harshalal. "Application of multisensor fusion to improve landmine detection /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488195633517176.
Повний текст джерелаShah, Shishir Kirit. "Probabilistic multifeature/multisensor integration for automatic object recognition /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
Повний текст джерелаKuhl, Mark R. "Ridge regression signal processing applied to multisensor position fixing." Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1183651058.
Повний текст джерелаLiimatainen, Saana Pauliina. "Hybrid framework for dynamic position determination in multisensor environments." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2399.
Повний текст джерелаAl-Jassar, Hala Khalid. "Retrieval of surface microwave emissivity using multisensor satellite measurements." Thesis, University College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283304.
Повний текст джерелаZhao, Haibin. "Multisensor integration and discrete geometry processing for coordinate metrology." Cachan, Ecole normale supérieure, 2010. http://tel.archives-ouvertes.fr/tel-00451244/fr/.
Повний текст джерелаMultisensor integration systems are used to fulfill the increasing requirements on the complexity and accuracy of coordinate metrology. Significant efforts are being devoted to develop multisensor integration systems that achieve holistic geometrical measurement with reduced uncertainty. This thesis investigates on the achievement of multisensor integration in coordinate metrology from two main parts: system function and data modeling, and discrete geometry processing. System function and data modeling allow obtaining clear understanding of function requirements and data specification of the multisensor integration system. Hierarchical function models of the system are built using IDEF0 methodology, which are decomposed into four basic activities. An ontology based method is employed to model the various data in the system with structured representation. Four data domains and their inter-relationships are specified to manage the multiple data in multisensor integration system clearly. Efficient and robust methods for discrete geometry processing to support multisensor integration are the main focus of this dissertation. This requires efficient methods for discrete differential geometry properties estimation, registration of discrete shapes, shape recognition and segmentation. Reliable estimation of normal and curvatures is essential for discrete shapes processing. Most discrete geometry processing procedures are based on the normal and curvatures’ information. A tensor based method is used for discrete curvature estimation. The proposed method provides better performance according to a comparative analysis. Registration is one of the most important and decisive steps of multisensor integration. Registration algorithms enable to align the different point data captured in the respective sensor’s coordinate system. A registration method is proposed to align different discrete shapes with unknown correspondences. A combination of curvature information and Euclidean distance is used to improve the performances of the classical ICP algorithm. Discrete shape recognition and segmentation are required in many applications. A new method based on shape index and curvedness is developed. The local surface type of a vertex is recognized according to its shape index value. The vertices are then clustered according to their local surfaces. A connected region algorithm is then implemented to generate the segmentation results. Considerable cases are tested and the method provides satisfying results. The above methods have been implemented in a new computerized system called DSP-COMS. A detailed case study of an industrial workpiece digitized by laser scanning and touch probing is presented. The measured point data are then processed using DSP-COMS. The results show the robustness of the above methods
Muller, Amanda Christine. "COGNITIVELY-ENGINEERED MULTISENSOR DATA FUSION SYSTEMS FOR MILITARY APPLICATIONS." Wright State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=wright1152542071.
Повний текст джерелаSilva, David Tiago Vieira da. "Multisensor calibration and data fusion using LIDAR and vision." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17967.
Повний текст джерелаEste trabalho expande um pacote de calibra çao extrínseca a sensores baseados em visão. Uma bola e usada como alvo de calibração para estimar a posição e orientação de vários sensores relativamente a um referencial comum. Primeiramente, e desenvolvido um algoritmo para deteção da bola na imagem, em seguida, são implementados e testados dois métodos para estimar a posição e orientação da câmara relativamente ao referencial de referência. Adicionalmente, uma interface gráfica e desenvolvida para o pacote de calibração. A interface permite a calibração de várias configurações de sensores, simplifica o processo de calibração e facilita a expansão do pacote a novos sensores. De forma a ilustrar o processo de expansão, o sensor Microsoft Kinect 3D-depth e integrado no pacote de calibração e respetiva interface. Por ultimo, utilizando a posição e orientaçao estimadas, os dados dos sensores são fundidos em um único referencial. Fusão sensorial e realizada no ATLASCAR 1, um veículo autónomo, e em testes laboratoriaisThe work presented in this thesis expands an existing extrinsic calibration package to vision-based sensors. A ball is used as a calibration target to estimate the pose of multiple sensors relative to a reference frame. First, an algorithm is developed to detect the ball in an image then, two methods are implemented and tested to estimate the camera pose relative to a reference frame. Additionally, a graphical user interface is developed for the extrinsic calibration package. The interface allows multiple sensor con gurations to be calibrated, greatly simpli es the calibration procedure and is easily expandable to new sensors. To illustrate the expansion process, Microsoft Kinect 3D-depth sensor is integrated in the calibration package and interface. Finally, from the estimated poses, sensor data fusion is achieved with ATLASCAR 1, an autonomous vehicle, and in indoors tests.
Abmayr, Thomas. "Calibration and registration framework for multisensor panoramic color scanning." Aachen Shaker, 2009. http://d-nb.info/999731661/04.
Повний текст джерелаWong, Yee Chin. "Target tracking in a multisensor environment using neural networks." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/298748.
Повний текст джерелаOgaja, Clement Surveying & Spatial Information Systems Faculty of Engineering UNSW. "A framework in support of structural monitoring by real time kinematic GPS and multisensor data." Awarded by:University of New South Wales. School of Surveying and Spatial Information Systems, 2002. http://handle.unsw.edu.au/1959.4/18662.
Повний текст джерелаRuiz, Ruiz Antonio Jesús. "Diseño de una arquitectura de localización geográfica para entornos de interior basada en información multisensor = a multisensor architecture for geographical positioning in indoor environments." Doctoral thesis, Universidad de Murcia, 2014. http://hdl.handle.net/10803/284895.
Повний текст джерелаIn this thesis we present the architecture of a localization system that uses data from multiple sensors available in commodity smartphones. Our main aim is to provide a solution able to support accurate location-based services, such as augmented reality applications, pursuing a good balance between accuracy and performance. We present the architecture which encompasses the overall system proposed. This architecture supports the design and development of services able to combine different types of sensors. Its layered structure and the specification of well-designed entities makes it able to support different system configurations. First we focus on the analysis of location fingerprinting techniques based on IEEE 802.11, feasible to determine the position of a device with a few meters of estimation error. Several refinements, based on the integration of additional sensors such as the camera or the inertial, are introduced to improve the efficiency of our solution. Using scale invariant features extracted from images we provide a solution for scene recognition that clearly improves the reliability of our result. Moreover, we take a step forward in the image analysis by including visual structure from motion techniques. It allows us to run off-line 3D reconstructions of the environment, and applying image resection techniques, we are able to provide precise estimations of both the 3D position and rotation of the device, obtaining an accuracy around 15 centimeters of error. Our multisensor solution works in two different stages. We first obtain a coarse-grained estimation based on WiFi signals, digital compass, and built-in accelerometer, making use of fingerprinting methods, probabilistic techniques, and motion estimators. Then, using the images captured by the camera, we carry out the image analysis focusing on the subset of the 3D model spatially delimited by the previously obtained coarse estimation. Because of the difficulties found to build accurate 3D models in large and repetitive environments, our proposal makes use of state-of-the-art IMU data processing techniques during the training phase, in order to reliably generate 3D representations of the targeted environment. This process solves typical scalability issues related to visually repetitive structures in large indoor scenarios. The fact of getting high accurate 3D representations of the testbed scenario improves the efficiency of camera resection techniques, reducing the estimation error to 5 centimeters, with response times below 250 milliseconds. The set of techniques presented supports a wide range of location-based applications, from those requiring a coarse estimation to those with high accuracy requirements.
San, Jose Antonio P. "Theater Ballistic Missile defense-multisensor fusion, targeting and tracking techniques." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA346055.
Повний текст джерелаThesis advisor(s): Robert G. Hutchins. "March 1998." Includes bibliographical references (p. 237). Also available online.
Hausmann, Felix [Verfasser]. "Entwicklung einer Multisensor-Heissfilmtechnik zur Transitionserkennung im Reiseflug / Felix Hausmann." Aachen : Shaker, 2004. http://d-nb.info/1172609527/34.
Повний текст джерелаAnglesea, John Robert Edward. "The optimisation of multisensor arrays in electronic odour sensing systems." Thesis, University of Derby, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270027.
Повний текст джерелаMirabadi, Ahmad. "Fault tolerant train navigation systems using a multisensor integration approach." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322911.
Повний текст джерелаAtkinson, Kevin E. (Kevin Edward) 1979. "Power-aware multisensor-management tracking on the Micro-AMPS platform." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87377.
Повний текст джерелаIncludes bibliographical references (leaves 60-61).
by Kevin E. Atkinson.
M.Eng.and S.B.
Nguyen, Trang. "Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking." ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/20.
Повний текст джерелаVicari, Matheus Boni. "Estimativas de variáveis biofísicas da canola com dados espectrais multisensor." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/129675.
Повний текст джерелаThis study used remote sensors, at local and regional levels, in order to characterize the spectral pattern of canola and propose methodologies to create crop masks, through satellite image classification, and generation of estimates of biophysical variables, from vegetation indices measured along the cycle. The measurements of biophysical variables were performed on experimental plots at Embrapa Trigo in Coxilha, and in crop sites monitored in the mesoregions Northeast and Northwest of Rio Grande do Sul, in 2013 and 2014. The biophysical variables measured were plant height, dry matter of the leaves, stem and pods and also, the leaf area index was estimated. The spectral data for the experimental plots were obtained using a spectroradiometer. For monitored crop fields, spectral data were obtained from the products MCD43B4 and MOD09A1, measured by MODIS (Terra / Aqua satellite) sensor, and images from the OLI sensor (Landsat 8). These data were used to perform the spectral characterization of canola along its development cycle, generating full spectral profiles and spectral bands profiles. The vegetation indices were used to characterize the spectral pattern and creating models to estimate the biophysical variables, which have been calculated using the simulated spectral bands. The vegetation indices were used to classify the areas planted with canola for the mesoregions Northeast and Northwest and then applied to the models for estimates of biophysical variables. The characterization of the canola's spectral pattern was consistent between the two years and for all sensors with temporal variation similar to other agricultural crops, except for the reduction in the vegetation indices during the flowering phase of culture. The biophysical variables estimation models showed high correlation coefficients, except for the variables dry matter of leaves and leaf area index. The canola classification using MODIS products, showed results consistent with the expected according to historical data series presented by CONAB. Estimates of biophysical variables were consistent with those obtained by measurements in the monitored fields. The results of this study show, therefore, the potential of using multi-sensor data for the spectral mapping of canola the estimation of biophysical variables.
Pawlak, Robert James. "Multisensor track initiation method that addresses the missing measurement problem." Diss., Virginia Tech, 1992. http://hdl.handle.net/10919/38631.
Повний текст джерелаKneissler, Andreas. "Multisensor-Strategie zur Optimierung des Anwalzverhaltens beim Radial-Axial-Ringwalzen." Aachen Shaker, 2008. http://d-nb.info/993898289/04.
Повний текст джерелаKim, Youngwook. "Multisensor Translation and Continuity of Vegetation Indices Using Hyperspectral Data." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/193681.
Повний текст джерелаRespati, Sara Wibawaning. "Network-scale arterial traffic state prediction: Fusing multisensor traffic data." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/202990/1/Sara%20Wibawaning_Respati_Thesis.pdf.
Повний текст джерелаOlson, Mark A. "Simulation of a multitarget, multisensor, track-splitting tracker for maritime surveillance." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA370905.
Повний текст джерелаThesis advisor(s): Titus, Harold A. ; Loomis, Herschel. "September 1999". Includes bibliographical references (p. 79). Also available online.
Demiroglu, Cenk. "Multisensor Segmentation-based Noise Suppression for Intelligibility Improvement in MELP Coders." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10455.
Повний текст джерела