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1
Hospedales, Timothy. "Bayesian multisensory perception." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2156.
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A key goal for humans and artificial intelligence systems is to develop an accurate and unified picture of the outside world based on the data from any sense(s) that may be available. The availability of multiple senses presents the perceptual system with new opportunities to fulfil this goal, but exploiting these opportunities first requires the solution of two related tasks. The first is how to make the best use of any redundant information from the sensors to produce the most accurate percept of the state of the world. The second is how to interpret the relationship between observations in each modality; for example, the correspondence problem of whether or not they originate from the same source. This thesis investigates these questions using ideal Bayesian observers as the underlying theoretical approach. In particular, the latter correspondence task is treated as a problem of Bayesian model selection or structure inference in Bayesian networks. This approach provides a unified and principled way of representing and understanding the perceptual problems faced by humans and machines and their commonality. In the domain of machine intelligence, we exploit the developed theory for practical benefit, developing a model to represent audio-visual correlations. Unsupervised learning in this model provides automatic calibration and user appearance learning, without human intervention. Inference in the model involves explicit reasoning about the association between latent sources and observations. This provides audio-visual tracking through occlusion with improved accuracy compared to standard techniques. It also provides detection, verification and speech segmentation, ultimately allowing the machine to understand ``who said what, where?'' in multi-party conversations. In the domain of human neuroscience, we show how a variety of recent results in multimodal perception can be understood as the consequence of probabilistic reasoning about the causal structure of multimodal observations. We show this for a localisation task in audio-visual psychophysics, which is very similar to the task solved by our machine learning system. We also use the same theory to understand results from experiments in the completely different paradigm of oddity detection using visual and haptic modalities. These results begin to suggest that the human perceptual system performs -- or at least approximates -- sophisticated probabilistic reasoning about the causal structure of observations under the hood.
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Ding, Yuhua. "An integrated approach to real-time multisensory inspection with an application to food processing." Diss., Available online, Georgia Institute of Technology, 2003:, 2003. http://etd.gatech.edu/theses/available/etd-11242003-180728/unrestricted/dingyuhu200312.pdf.
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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2004.Vachtsevanos, George J., Committee Chair; Dorrity, J. Lewis, Committee Member; Egerstedt, Magnus, Committee Member; Heck-Ferri, Bonnie S., Committee Co-Chair; Williams, Douglas B., Committee Member; Yezzi, Anthony J., Committee Member. Includes bibliography.
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Axenie, Cristian [Verfasser], Jörg [Akademischer Betreuer] [Gutachter] Conradt, and Jeffrey [Gutachter] Krichmar. "Synthesis of Distributed Cognitive Systems: Interacting Computational Maps for Multisensory Fusion / Cristian Axenie. Betreuer: Jörg Conradt. Gutachter: Jörg Conradt ; Jeffrey Krichmar." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1100689036/34.
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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.
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Petrovic, Vladimir. "Multisensor pixel-level image fusion." Thesis, University of Manchester, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715412.
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Pradhan, 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.
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Berg, 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.
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Wellington, Sean. "Algorithms for sensor validation and multisensor fusion." Thesis, Southampton Solent University, 2002. http://ssudl.solent.ac.uk/398/.
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Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate.
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Prajitno, Prawito. "Neuro-fuzzy methods in multisensor data fusion." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251258.
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Tannous, Halim Elie. "Interactive and connected rehabilitation systems for e-health." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2436/document.
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La rééducation fonctionnelle classique comprend des séances thérapeutiques, des exercices à domicile et des mouvements avec ou sans l'aide de thérapeutes. Cette approche classique souffre de nombreuses limitations, en raison de l’incapacité de l’expert à suivre les séances à domicile du patient et du manque de motivation du patient pour répéter les exercices sans retour. Les jeux sérieux ont été présentés comme une solution à ces problèmes. Premièrement, des exergames ont été développés à l’aide d’une méthodologie de codesign, où patients, experts et développeurs ont pris part aux procédures de conception. Le capteur Kinect a été utilisé pour capturer la cinématique en temps réel au cours de l’exercice. Ensuite, une fusion de données a été étudier entre le capteur Kinect et les unités de mesure inertielles, afin d’augmenter la précision de l’estimation de l’angle des articulations, à l’aide d’une approche système de systèmes. Le système a été évalué par des patients avec différentes pathologies au cours de multiples campagnes d'évaluation. Les résultats obtenus montrent que les jeux sérieux peuvent être une solution pour des pathologies spécifiques. En outre, les experts ont été convaincus de la pertinence clinique de ce dispositif et ont estimé que les données proposées étaient suffisantes pour évaluer la situation du patient. La mise en place de tels systèmes maximiserait l’efficacité du programme de réadaptation. D'autre part, ce système permettrait également de réduire les limitations actuellement présentes dans les programmes de rééducation classiques, permettant aux patients de visualiser leurs mouvements et aux experts de suivre l'exécution de l'exercice à domicileConventional musculoskeletal rehabilitation consists of therapeutic sessions, home exercise assignment, and movement execution with or without the assistance of therapists. This classical approach suffers from many limitations, due to the expert’s inability to follow the patient’s home sessions, and the patient’s lack of motivation to repeat the same exercises without feedback. Serious games have been presented as a possible solution for these problems. This thesis was carried out in the eBioMed experimental platform of the Université de technologie de Compiège, and in the framework of the Labex MS2T. The aim of this thesis is to develop a real-time, serious gaming system for home-based musculoskeletal rehabilitation. First, exergames were developed, using a codesign methodology, where the patients, experts and developers took part in the design and implementation procedures. The Kinect sensor was used to capture real-time kinematics during each exercise. Next, data fusion was implemented between the Kinect sensor and inertial measurement units, to increase the accuracy of joint angle estimation, using a system of systems approach. In addition, graphical user interfaces were developed, for experts and patients, to suit the needs of different end-users, based on the results of an end-user acceptability study. The system was evaluated by patients with different pathologies through multiple evaluation campaigns. Obtained results showed that serious games can be a good solution for specific types of pathologies. Moreover, experts were convinced of the clinical relevance of this device, and found that the estimated data was more than enough to assess the patient’s situation during their home-based exercise sessions. Finally, during these three years, we have set the base for a home-based rehabilitation system that can be deployed at home or in a clinical environment. The implementation of such systems would maximize the efficiency of rehabilitation program, while saving the patient’s and expert’s time and money. On the other hand, this system would also reduce the limitation that are currently present in classical rehabilitation programs, allowing the patients to visualize their movements, and the experts to follow the home exercise execution
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Jia, Huamin. "Data fusion methodologies for multisensor aircraft navigation systems." Thesis, Cranfield University, 2004. http://hdl.handle.net/1826/781.
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The thesis covers data fusion for aircraft navigation systems in distributed sensor
systems. Data fusion methodologies are developed for the design, development,
analysis and simulation of multisensor aircraft navigation systems. The problems of
sensor failure detection and isolation (FDI), distributed data fusion algorithms and
inertial state integrity monitoring in inertial network systems are studied.
Various existing integrated navigation systems and Kalman filter architectures are
reviewed and a new generalised multisensor data fusion model is presented for the
design and development of multisensor navigation systems. Normalised navigation
algorithms are described for data fusion filter design of inertial network systems.
A normalised measurement model of skewed redundant inertial measurement units
(SRIMU) is presented and performance criteria are developed to evaluate optimal
configurations of SRIMUs in terms of the measurement accuracy and FDI capability.
Novel sensor error compensation filters are designed for the correction of SRIMU
measurement errors. Generalised likelihood ratio test (GLRT) methods are improved
to detect various failure modes, including short time and sequential moving-window
GLRT algorithms.
State-identical and state-associated fusion algorithms are developed for two forms of
distributed sensor network systems. In particular, innovative inertial network sensing
models and inertial network fusion algorithms are developed to provide estimates of
inertial vector states and similar node states. Fusion filter-based integrity monitoring
algorithms are also presented to detect network sensor failures and to examine the
consistency of node state estimates in the inertial network system.
The FDI and data fusion algorithms developed in this thesis are tested and their
performance is evaluated using a multisensor software simulation system developed
during this study programme. The moving-window GLRT algorithms for optimal
SRIMU configurations are shown to perform well and are also able to detect jump
and drift failures in an inertial network system. It is concluded that the inertial
network fusion algorithms could be used in a low-cost inertial network system and
are capable of correctly estimating the inertial vector states and the node states.
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Terpening, Brian Everett. "Application of Kalman filter on multisensor fusion tracking." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/26384.
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Approved for public release; distribution is unlimitedThe 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.
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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.
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Zouba, Valentin Nadia. "Multisensor fusion for monitoring elderly activities at home." Nice, 2010. http://www.theses.fr/2010NICE4001.
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Dans cette thèse, une approche combinant des données issues de capteurs hétérogènes pour la reconnaissance d'activités des personnes âgées à domicile est proposée. Cette approche consiste à combiner les données fournies par des capteurs vidéo avec des données fournies par des capteurs environnementaux pour suivre l'interaction des personnes avec l'environnement. La première contribution est un nouveau modèle de capteur capable de donner une représentation cohérente et efficace des informations fournies par différents types de capteurs physiques. Ce modèle inclue l'incertitude sur la mesure. La deuxième contribution est une approche, basée sur une fusion multicapteurs, pour la reconnaissance d'activités. Cette approche consiste à détecter la personne, suivre ses mouvements, reconnaître ses postures et ses activités d'intérêt, par une analyse multicapteurs et une reconnaissance d'activités humaines. Pour résoudre le problème de la présence de capteurs hétérogènes, nous avons choisi de réaliser la fusion à haut niveau (niveau événement) des différentes données issues des différents capteurs, en combinant les événements vidéo avec les événements environnementaux. La troisième contribution est l'extension d'un langage de description qui permet aux utilisateurs (ex. Le corps médical) de décrire les activités d'intérêt dans des modèles formels. Les résultats de cette approche sont montrés pour la reconnaissance des AVQ pour de vraies personnes âgées évoluant dans un appartement expérimental appelé GERHOME équipé de capteurs vidéo et de capteurs environnementaux. Les résultats obtenus de la reconnaissance des différentes AVQ sont encourageantsIn 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
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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.
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Silva, 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.
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Mestrado em Engenharia MecânicaEste 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.
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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.
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Kumar, Mrityunjay. "Model based image fusion." Diss., Connect to online resource - MSU authorized users, 2008.
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Jeong, 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.
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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.
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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, March 1998.Thesis advisor(s): Robert G. Hutchins. "March 1998." Includes bibliographical references (p. 237). Also available online.
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Murphy, Robin Roberson. "An architecture for intelligent robotic sensor fusion." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/8226.
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Ghannam, Sherin Ghannam. "Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/81092.
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The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite.
To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat.
Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes.
In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM.
In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM.Ph. D.
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Wang, Fengzhen. "Neural networks for data fusion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ30179.pdf.
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Humali, I. Gokhan. "Sensor fusion for boost phase interception of ballistic missiles." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FHumali.pdf.
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Thesis (M.S. in Systems Engineering)--Naval Postgraduate School, Sept. 2004.Thesis advisor(s): Phillip E. Pace, Murali Tummala. Includes bibliographical references (p. 73-74). Also available online.
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Kiepert, Joshua. "A wireless sensor data fusion framework for contaminant detection." [Boise, Idaho] : Boise State University, 2009. http://scholarworks.boisestate.edu/td/61/.
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Zampieron, Jeffrey Michael Domenic. "Self-localization in ubiquitous computing using sensor fusion /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/2801.
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Raman, Srinivas. "Condition monitoring of industrial machines using wavelet packets and intelligent multisensor fusion." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/15224.
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Machine condition monitoring is an increasingly important area of research and plays an integral role in the economic competitiveness in many industries. Machine breakdown can lead to many adverse effects including increased operation and maintenance costs, reduced production output,
decreased product quality and even human injury or death in the event of a catastrophic failure. As a way to overcome these problems, an automated machine diagnostics scheme may be implemented, which will continuously monitor machine health for the purpose of prediction, detection, and diagnosis of faults and malfunctions. In this work, a signal-based condition
monitoring scheme is developed and tested on an industrial fish processing machine. A variety of faults are investigated including catastrophic on-off type failures, partial faults in gearbox components and sensor failures. The development of the condition monitoring scheme is divided into three distinct subtasks: signal acquisition and representation, feature reduction, and classifier design. For signal acquisition, the machine is instrumented with multiple sensors to accommodate sensor failure and increase the reliability of diagnosis. Vibration and sound signals are continuously
acquired from four accelerometers and four microphones placed at strategic locations on the machine. The signals are efficiently represented using the wavelet packet transform and node energies are used to generate a feature vector. A measure for feature discriminant ability is chosen and the effect of choosing different analyzing wavelets is investigated. Since the dimensionality of the feature vector can become very large in multisensor applications, various means of feature reduction are investigated to reduce the computational cost and improve the classification accuracy. Local Discriminant Bases, a popular and complementary approach to wavelet-based feature selection is introduced and the drawbacks in the context of multisensor applications are highlighted. To address these issues, a genetic algorithm is proposed for feature selection in robust condition monitoring applications. The fitness function of the genetic algorithm consists of three criteria that are considered to be important in fault classification: feature set size, discriminant ability, and sensor diversity. A procedure to adjust the weights is
presented. The feature selection scheme is validated using a data set consisting of one healthy machine condition and five faulty conditions. For classifier design, the theoretical foundations of two popular non-linear classifiers are presented. The performance of Support Vector Machines (SVM) and Radial Basis Function (RBF) networks are compared using features obtained from a filter selection scheme and a wrapper selection scheme. The classifier accuracy is determined under conditions of complete
sensor data and corrupted sensor data. Different kernel functions are applied in the SVM to determine the effect of kernel variability on the classifier performance.
Finally, key areas of improvement in instrumentation, signal processing, feature selection, and classifier design are highlighted and suggestions are made for future research directions.
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Koh, Leonard Phin-Liong. "A neural network approach to multisensor data fusion for vessel traffic services." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA294251.
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Makkeasorn, Ammarin. "MULTISENSOR FUSION REMOTE SENSING TECHNOLOGY FOR ASSESSING MULTITEMPORAL RESPONSES IN ECOHYDROLOGICAL SYSTEMS." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4068.
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Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing–based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Environmental Engineering PhD
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Wallin, Mattias. "Multisensor fusion and control strategies for low cost hybrid stepper motor solutions." Thesis, Luleå tekniska universitet, Signaler och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65862.
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This thesis has explored if it is feasible to produce a good estimation of the rotational position of a stepper motor by using sensor fusion schemes to merge a sensorless position estimation (based on the back electromotive force) with the measurement from a magnetic rotational position sensor. The purpose was to find a cheaper alternative for position feedback in closed loop control from conventionally used rotational encoders and resolvers. Beyond the sensor fusion a suitable position control logic was also developed to verify the concept of a low cost closed loop hybrid stepper motor solution for high precision position applications. The sensor fusion and position control were simulated offline to first test the feasibility of the implementation, after which laboratory tests were performed to assess online performance. The extended Kalman filter implemented improved the performance of the magnetic rotational position sensor which was used exclusively at lower speeds (between 0-75 rpm) by decreasing its root-mean-square error by almost half from 0.0733 unfiltered to 0.0370 filtered (in mechanical degrees). When fusing both position signals at higher rotational speeds (75-400rpm) did the extended Kalman filter clearly improve position estimation accuracy compared to the single sources. It is not meaningful however to discuss the numeric improvement of the filter at these working points as this result is not conclusive but based on some fortunate conditions. This is because the two signals used for the fusion is diverging towards positive and negative error respectively for increasing rotational speeds making the fused estimate result in between. This basically means that the result from the fusion is outperforming two very bad signals, and is then not meaningful to use as a measure of how well the fusion is actually performing. Further work on the raw signals used for fusion need to be performed before a proper assessment on the fusion performance could be made.
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Stepenosky, Nicholas. "Data fusion of complementary information from parietal and occipital event related potentials for early diagnosis of Alzheimer's disease /." Full text available online, 2006. http://www.lib.rowan.edu/find/theses.
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Utete, Simukai. "Network management in decentralised sensing systems." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297308.
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Chow, Khin Choong. "Fusion of images from Dissimilar Sensor systems /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FChow.pdf.
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Thesis (M.S. in Combat Systems Technology)--Naval Postgraduate School, Dec. 2004.Thesis Advisor(s): Monique P. Fargues, Alfred W. Cooper. Includes bibliographical references (p. 73-75). Also available online.
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Manyika, James. "An information-theoretic approach to data fusion and sensor management." Thesis, University of Oxford, 1993. http://ora.ox.ac.uk/objects/uuid:6e6dd2a8-1ec0-4d39-8f8b-083289756a70.
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The use of multi-sensor systems entails a Data Fusion and Sensor Management requirement in order to optimize the use of resources and allow the synergistic operation of sensors. To date, data fusion and sensor management have largely been dealt with separately and primarily for centralized and hierarchical systems. Although work has recently been done in distributed and decentralized data fusion, very little of it has addressed sensor management. In decentralized systems, a consistent and coherent approach is essential and the ad hoc methods used in other systems become unsatisfactory. This thesis concerns the development of a unified approach to data fusion and sensor management in multi-sensor systems in general and decentralized systems in particular, within a single consistent information-theoretic framework. Our approach is based on considering information and its gain as the main goal of multi-sensor systems. We develop a probabilistic information update paradigm from which we derive directly architectures and algorithms for decentralized data fusion and, most importantly, address sensor management. Presented with several alternatives, the question of how to make decisions leading to the best sensing configuration or actions, defines the management problem. We discuss the issues in decentralized decision making and present a normative method for decentralized sensor management based on information as expected utility. We discuss several ways of realizing the solution culminating in an iterative method akin to bargaining for a general decentralized system. Underlying this is the need for a good sensor model detailing a sensor's physical operation and the phenomenological nature of measurements vis-a-vis the probabilistic information the sensor provides. Also, implicit in a sensor management problem is the existence of several sensing alternatives such as those provided by agile or multi-mode sensors. With our application in mind, we detail such a sensor model for a novel Tracking Sonar with precisely these capabilities making it ideal for managed data fusion. As an application, we consider vehicle navigation, specifically localization and map-building. Implementation is on the OxNav vehicle (JTR) which we are currently developing. The results show, firstly, how with managed data fusion, localization is greatly speeded up compared to previous published work and secondly, how synergistic operation such as sensor-feature assignments, hand-off and cueing can be realised decentrally. This implementation provides new ways of addressing vehicle navigation, while the theoretical results are applicable to a variety of multi-sensing problems.
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Bellotto, Nicola. "Multisensor data fusion for simultaneous people tracking and recognition with a service robot." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495813.
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Modern service robots are designed to operate in private and public areas characterized by the presence of people. In order to move safely in the environment and provide useroriented services, these robots need to estimate the position and the identity of humans. Such a task, however, is made particularly difficult by the uncertainty of robot's observations and by the dynamic nature of human environments. The methods and the algorithms implemented have been extensively tested with mobile robots in dynamic human envhonments, evaluating the performance of the people tracking and recognition in many challenging situations. The solutions adopted include a number of contributions in the challenging areas of human-centred robotics and multisensor perception.
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Salustiano, Rogerio Esteves 1978. "Aplicação de tecnicas de fusão de sensores no monitoramento de ambientes." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/262032.
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Orientador: Carlos Alberto dos Reis FilhoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-05T17:36:56Z (GMT). No. of bitstreams: 1 Salustiano_RogerioEsteves_M.pdf: 3698724 bytes, checksum: a5c6d59ec19db38f5a0324243ddb1eb5 (MD5) Previous issue date: 2006
Resumo: Este trabalho propõe um sistema computacional no qual são aplicadas técnicas de Fusão de Sensores no monitoramento de ambientes. O sistema proposto permite a utilização e incorporação de diversos tipos de dados, incluindo imagens, sons e números em diferentes bases. Dentre os diversos algoritmos pertinentes a um sistema como este, foram implementados os de Sensores em Consenso que visam a combinação de dados de uma mesma natureza. O sistema proposto é suficientemente flexível, permitindo a inclusão de novos tipos de dados e os correspondentes algoritmos que os processem. Todo o processo de recebimento dos dados produzidos pelos sensores, configuração e visualização dos resultados é realizado através da Internet
Abstract: This work proposes a computer system in which Sensor Fusion techniques are applied to monitoring the environment. The proposed system allows the use and incorporation of different data types, including images, sounds and numbers in different bases. Among the existing algorithms that pertain to a system like this, those, which aim to combine data of the same nature, called Consensus Sensors, have been particularly implemented. The proposed system is flexible enough and allows the inclusion of new data types and their corresponding algorithms. The whole process of receiving the data produced by the sensors, configuration of produced results as well as their visualization is performed through the Internet
Mestrado
Eletrônica, Microeletrônica e Optoeletrônica
Mestre em Engenharia Elétrica
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Avor, John Kweku. "Application of sensor fusion to human locomotor system." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Li, Lingjie Luo Zhi-Quan. "Data fusion and filtering for target tracking and identification /." *McMaster only, 2003.
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Michael, Andrew M. "Imaging schizophrenia : data fusion approaches to characterize and classify /." Online version of thesis, 2009. http://hdl.handle.net/1850/9673.
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Bougiouklis, Theodoros C. "Traffic management algorithms in wireless sensor networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FBougiouklis.pdf.
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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2006.Thesis Advisor(s): Weillian Su. "September 2006." Includes bibliographical references (p. 79-80). Also available in print.
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Lin, Erwei Kam Moshe. "Detection in distributed sensor networks /." Philadelphia, Pa. : Drexel University, 2005. http://hdl.handle.net/1860/1303.
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Crow, Mason W. "Multiple sensor credit apportionment." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FCrow.pdf.
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Auephanwiriyakul, Sansanee. "A study of linguistic pattern recognition and sensor fusion /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999270.
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Jarrell, Jason A. "Employ sensor fusion techniques for determining aircraft attitude and position information." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5894.
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Thesis (M.S.)--West Virginia University, 2008.Title from document title page. Document formatted into pages; contains xii, 108, [9] p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 104-108).
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Pasika, Hugh Joseph Christopher. "Neural network sensor fusion : creation of a virtual sensor for cloud-base height estimation /." *McMaster only, 1999.
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Rokonuzzaman, Mohd. "Discrete event development framework for highly reliable sensor fusion systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0009/NQ42486.pdf.
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Tobias, Martin. "Probability Hypothesis Densities for Multitarget, Multisensor Tracking with Application to Passive Radar." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10514.
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The probability hypothesis density (PHD), popularized by Ronald Mahler, presents a novel and theoretically-rigorous approach to multitarget, multisensor tracking. Based on random set theory, the PHD is the first moment of a point process of a random track
set, and it can be propagated by Bayesian prediction and observation equations to form a multitarget, multisensor tracking filter. The advantage of the PHD filter lies in its ability to estimate automatically the expected number of targets present, to fuse easily different kinds of data observations, and to locate targets without performing any explicit report-to-track association.
We apply a particle-filter implementation of the PHD filter to realistic multitarget, multisensor tracking using passive coherent location (PCL) systems that exploit illuminators of opportunity such as FM radio stations.
The objective of this dissertation is to enhance the usefulness of the PHD particle filter for multitarget, multisensor tracking, in general, and within the context of PCL, in
particular. This involves a number of thrusts, including: 1) devising intelligent proposal densities for particle placement, 2) devising a peak-extraction algorithm for extracting information from the PHD, 3) incorporating realistic probabilities of detection and signal-to-noise ratios (including multipath effects) to model realistic PCL scenarios, 4) using range, Doppler, and direction of arrival (DOA) observations to test the target detection and data fusion capabilities of the PHD filter, and 5) clarifying the concepts behind FISST and the PHD to make them more accessible to the practicing engineer.
A goal of this dissertation is to serve as a tutorial for anyone interested in becoming familiar with the probability hypothesis density and associated PHD particle filter. It is hoped that, after reading this thesis, the reader will have gained a clearer understanding of the PHD and the functionality and effectiveness of the PHD particle filter.
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Remund, Quinn P. "Multisensor Microwave Remote Sensing in the Cryosphere." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/72.
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Because the earth's cryosphere influences global weather patterns and climate, the scientific community has had great interest in monitoring this important region. Microwave remote sensing has proven to be a useful tool in estimating sea and glacial ice surface characteristics with both scatterometers and radiometers exhibiting high sensitivity to important ice properties. This dissertation presents an array of studies focused on extracting key surface features from multisensor microwave data sets. First, several enhanced resolution image reconstruction issues are addressed. Among these are the optimization of the scatterometer image reconstruction (SIR) algorithm for NASA scatterometer (NSCAT) data, an analysis of Ku-band azimuthal modulation in Antarctica, and inter-sensor European Remote Sensing Satellite (ERS) calibration. Next, various methods for the removal of atmospheric distortions in image reconstruction of passive radiometer observations are considered. An automated algorithm is proposed which determines the spatial extent of sea ice in the Arctic and Antarctic regions from NSCAT data. A multisensor iterative sea ice statistical classification method which adapts to the temporally varying signatures of ice types is developed. The sea ice extent and classification algorithms are adopted for current SeaWinds scatterometer data sets. Finally, the automated inversion of large-scale forward electromagnetic scattering of models is considered and used to study the temporal evolution of the scattering properties of polar sea ice.
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Midwood, Sean A. "A computationally efficient and cost effective multisensor data fusion algorithm for the United States Coast Guard Vessel Traffic Services system." Thesis, Monterey, Calif. : Naval Postgraduate School, 1997. http://handle.dtic.mil/100.2/ADA333476.
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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 1997.Thesis Advisor(s): Murali Tummala. "September 1997." Includes bibliographical references (p. 61-62). Also available in print.
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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.
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