Tesis sobre el tema "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.
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Purohit, Madhavi. "Multisensor integration for a robot". Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182456473.
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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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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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Bonaccorso, Filippo. "Multisensor data fusion for robotic control". Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1081.
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Nowadays robots perform more and more tasks, from simpler ones such as automatic domestic vacuum cleaner, to highly skilled ones such as tele-surgery. To do so a robot continuously needs to know what to do and what has been done. A user controls robots through a system controller, which processes both his commands and information from the environment.
These information are collected by a measurement system, often called observer, which bring back them to the system controller. Measures are the information used to perform the control. The simpler measurement system can, in general, be considered as made up of two elements:
1. A sensor or transducer which is an element that produces a signal relating the quantity being measured. Sensors are elements that when subject to some physical change experience a related change.
2. A signal conditioner which takes the signal from the sensor and processes it to make it suitable for the specific application. The signal may be, for example, too small or too noisy.
Different kinds of sensors are used in robotics, such as position, velocity, force sensors and so on. Each application needs the right sensor to be chosen, the choice must satisfy different criterions such as performance, cost and feasibility. In this way both static, as range or sensitivity, and dynamic characteristics, as response time, of the sensor have to be considered.
In robotic applications there are two main subclasses of sensors: internal and external. The former bring information about the robot; the latter bring information about the environment. In a mobile robot, for example, encoders are internal sensors, while ultrasound sensors are external.
Multisensor data fusion seeks to combine data from multiple sensors to perform inferences that may not be possible from a single sensor. Different sensors have different strengths and weaknesses, so fused data from multiple sensors provides several advantages over data from a single sensor:
A wider and more accurate range of information by combining data from different types of sensors.
Redundancy.
Increased reliability.
Weaknesses compensation.
Sensors failure detection and handling.
Typical applications that can benefit from multiple sensors are, first of all, mobile robot navigation, target tracking, aircraft navigation and industrial tasks control.
There are a number of different ways to integrate or fuse information provided by multiple sensors.
Multisensor input system controller
The simplest approach to multisensor data fusion, is to use the information from each sensor as a separate input to the system controller. This approach may be the most appropriate if each sensor is providing information concerning completely different aspects of the environment. The major benefit gained through this approach is the increase in the extent of the environment that is able to be sensed. The only interaction between the sensors is indirect and based on the individual effect each sensor has on the controller and so on the whole robot.
If there is some degree of overlap between sensors, concerning some aspect of the environment they are able to sense, it may be possible for a sensor to directly influence the operation of another one. In this way the value of the combined information that the sensors provide, is greater than the sum of the value of the information provided by each sensor separately. This synergistic effect can be achieved either by using the information from one sensor to provide cues, or guide the operation of other sensors, or by actually combining or fusing the information from multiple sensors.
The whole research activity has dealt with two main topics: the RAPOLAC Project and the Mobile Robots. The former are discussed in the first four chapters while the latter in the following four.
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PINARDI, STEFANO. "Movements recognition with intelligent multisensor analysis". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19297.
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In movement science with inertial sensor many different methodologies resolving specific aspects of movement recognition have been proposed. They are very interesting, and useful, but none of them are generally explicative of what is going on in the semantic sense. When we go down to the movement recognition/classification area (for example in Ambient Intelligence) we do not have a feasible model that can be considered generally predictive or usable for activity recognition.
Also, in the field of movement recognition with inertial sensors many technological issues arise: technological diversity, calibration matters, sensor model problems, orientation and position of sensors, and a lot of numerous specificities that, with all the above aspects, and the lack of public dataset of movements sufficiently generic and semantically rich, contribute to create a strong barrier to any approach to a classification matters with wearable sensors.
We have also to notice that a movement is a phenomenon explicitly or implicitly (voluntary or involuntary) controlled by brain. The individual free-will introduce a further matter when we want to temporary predict the movements looking at the close past. Pattern can change at any time when ambient, psychological context, age of the subject change. Also, pathological issues, and physiological differences and the will of the subject, introduce important differences. For all these reasons I considered that a semantical /lexical approach to movement recognition with sensors, driven by machine learning techniques could be a promising way to solve some of these challenge and problems.
In this Ph.D. Thesis wearable inertial sensors has been used to classify movements, the choice of inertial sensors has been driven by technological and practical advantages, they are cheap, lightweight, and - differently from video cameras - are not prone to the hidden face, or luminance problems. The main idea is to use inertial sensor to understand what a person is doing for ambient-intelligent, healthcare, medical-sport applications. My principal concerns was to propose a method that was not centered on technology issues but on data analysis, that could be a general framework and could also create a general representation of movement,that could be useful also in other area of research, like reasoning. Inertial sensors are treated just as an example, a particular type of sensors, the method is new, reusable, algorithmically simple, net and easy to understand. Accuracy is very high outperforming the best results given in literature, reducing the error rate of 4 times.
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Rødningsby, 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.
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TARGET tracking is an essential requirement for surveillance and control systems to interpret the environment. This environment may contain multiple targets, and the environmental information may be obtained by multiple sensors in a multitarget multisensor tracking system. In this thesis we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. This wake causes additional measurements to those originating from the target. When the measurements are processed, the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where targets are operating close to each other in the presence of wakes. In this thesis a probabilistic model is developed which reflects the probability that a false measurement originates from the wake behind a target. This wake model is integrated in the probabilistic data association filter (PDAF) to improve the track continuity for tracking single targets. The modified PDAF is further extended to handle multiple targets in the presence of wakes by using a probabilistic wake model for each of the targets in the multitarget environment that has a wake behind it. These single wake models are combined to form a joint wake model which augments the joint probabilistic data association filter (JPDAF) for both coupled and decoupled filtering. The wake-originated measurements may also cause confusion in the track initiation. To prevent this problem, a clustering method is proposed based on morphological operators which allows tracks to be initialized based on two-point differencing of the cluster centroids from succeeding scans. The modified PDAF is tested on data of a real scuba diver with an open breathing system. In this case the air bubbles produced by the diver form a wake which extends far behind the diver. The experiment showed that the above modifications of the PDAF improved the track continuity significantly. Finally, a relatively extensive simulation, based on real scuba diver data, is presented. Four different multitarget multisensor tracking scenarios are simulated, considering two targets with wakes that are: 1. Crossing each other. 2. Moving in parallel to each other. 3. One following after another. 4. Meeting and then passing each other. The results of these simulation scenarios show that the presented modifications improve the tracking performance, and the probability of lost tracks is significantly reduced. The targets are observed by two sensors, and it is shown that tracks estimated in a centralized fusion configuration are better than the local tracks estimated using data from individual sensors only. It is also shown that applying the wake model to targets that do not generate a wake, yields almost no deterioration of the tracking performance.
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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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Hilmersson, 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.
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Monitoring stress in real-time, in a non-laboratory environment can be benecial in several applications. One of these, which have been the motivation for this thesis, is to to perform this measurement during Attention decit hyperactivity disorder (ADHD) diagnosis. Monitoring several physiological responses to internal or external stimuli in a single soft-real-time system is nota solution widely used in an application like this. The thesis starts by studying several stress related responses in detail. Sensors for all of the responses are not implemented nor is it possible toimplement in to the desired system. After the study is was decided to implement two measurement modules. The first a Photo-plethysmogrophy (PPG) measurement module to measure heart rate and also estimate breathing. This module is prepared for estimating arterial blood oxygen levels but the calculation or verification have not been done. The second is Skin Conductance (SC) measurement module and in to both ofthese add a temperature sensor to measure the temperature of the skin. Time constraints limit the SC module to only be presented in theory. The PPG module on the other hand have been realisedin a prototype. This prototype performs the measurement in transmissive mode on the left earlobe, which leaves the hands free and it does not affect the hearing on that ear. The prototype giveout acceptable signal quality when good contact with the measurement site is achieved. The signalinterpretation, such as performing the signal analysis to count the beats per minute, is outside thescope of this thesis and will therefore not be presented but the signals can be seen in figures.Att 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.
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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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Remund, 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.
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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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Klaus, Ferdinand. "Einführung in Techniken und Methoden der Multisensor-Datenfusion". [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971151989.
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Robl, Christian. "Aufgabenorientierte Kopplung von Sensoren mit unterschiedlichen Abtasteigenschaften". [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=959771166.
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Dittmann, Brigitte. "Entwicklung und Realisierung eines Sensorsystems auf massenspektrometrischer Basis". [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=96214715X.
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Rühl, Thorsten. "Studien zur verbesserten Ausnutzung des Informationsgehaltes von Multisensorsystemen". [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963710591.
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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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Khider, Mohammed [Verfasser]. "Multisensor-Based Positioning for Pedestrian Navigation / Mohammed Khider". München : Verlag Dr. Hut, 2014. http://d-nb.info/1047994984/34.
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FernaÌndez, 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.
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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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Hinton, Andrew. "The development of multisensor arrays utilising conducting polymers". Thesis, Sheffield Hallam University, 1997. http://shura.shu.ac.uk/19796/.
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This thesis is concerned with the continued development of multisensor array sensing technology for the detection and classification of aromas. The technology applies the use of conducting polymers grown across a gap between metallic conductors. The electrochemically deposited films complete a circuit and providing electrical resistance. In this format the films act as chemical resistors (chemiresistors) the current flow being influenced by the polymers' molecular electronics. Devices of this nature are potentially useful as sensors for analytes which cause the reversible modulation of the films' molecular electronics, leading to a detectable resistance change. Variation in the chemical and physical properties of the conducting polymer films has led to the generation of sensing devices capable of providing rapid, meaningful sensory information. The development of multisensor arrays containing a series of sensing devices having broad ranging sensitivities, has enabled effective discrimination of sample analytes. The information generated from such an array provides a 'fingerprint' or patterned response relating directly to the sample analyte. Complex statistical processing techniques have been coupled with the sensor technology to categorise and differentiate between the 'fingerprints' obtained. Instrumentation based on multisensor array technology has been developed by Neotronics Scientific Ltd., who currently market the NOSE (Neotronics Olfactory Sensing Equipment) based upon conducting polymer sensors. The research project resulting in this thesis was intended to develop and examine conducting polymer chemiresistor technology and explore the parameters that contribute to the production of effective discriminating sensors for use in array devices. The study involved an investigation of the variables involved in the electropolymerisation protocols, and expansion of the sensing chemiresistors available. This was achieved by analysis of polymer fabrication methods, and the variation in monomer and electrolyte feedstocks used during polymerisation. Polymer film stability was a major feature of the work performed as the long-term effectiveness of a sensing device is governed by environmental stability allowing reproducible analysis. Sensor optimisation was investigated using an individual system to determine the effect of the electrodeposition protocols, surface morphology, baseline resistance and film thickness. Polymer composition and stability were studied using a series of electrochemical, spectroscopic and surface analysis techniques. The data obtained resulted in the fabrication of chemiresistors not previously tested in electronic nose technology. Experimental optimisation studies also allowed variation in the nature of the responses obtained. A final area of investigation was the analysis of chemiresistors within a multisensor array environment using the NOSE technology. A series of arrays were prepared and the sensors exposed to a number of single, pure, organic analytes. From this data information was obtained on sensor response relating to molecular size, shape, position and nature of functional groups. The multifaceted nature of these experiments increased the number and response characteristics accessible to Neotronics, and provided a contribution to the knowledge surrounding the interactions between conducting polymer films and volatile organic analytes.
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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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Shah, Shishir Kirit. "Probabilistic multifeature/multisensor integration for automatic object recognition /". Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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Kuhl, Mark R. "Ridge regression signal processing applied to multisensor position fixing". Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1183651058.
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Liimatainen, Saana Pauliina. "Hybrid framework for dynamic position determination in multisensor environments". Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2399.
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Information about a user's context is crucial in obtaining the goal of ubiquitous computing. This thesis introduces a new approach in for looking at a special case of context; location information. Making devices location-aware is the first step of providing context-based services. Existing technologies for position determination are ill suited in terms of interoperability and heterogeneity. Furthermore, they rely either on vast and often expensive infrastructures to perform the position estimation or alternatively the mobile device is burdened with the responsibility of localising itself. Both of the current approaches have their trade-offs. The basis of this work is to maximise the availability of positioning services allowing mobility between different environments and surroundings while minimising the vulnerabilities of existing approaches. The work presents a managed positioning environment for indoor and outdoor surroundings, in which accuracy and precision can be improved by using a mix of fixed sensors and the sensing capabilities of mobile devices in a way that it allows the transformation of proximity data into absolute coordinates. It is believed that this also improves the availability of the positioning service as partial, imprecise or incomplete data is utilised rather than discarded. The usage of wireless local area networks along with PDAs, mobile phones and similar devices, as opposed to custom sensors ensures that maintenance and administrative costs are kept to a minimum. Furthermore, a dynamic feedback system is proposed in order minimise deployment and initialisation effort by allowing refining of location information in fixed sensors.
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Al-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.
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Zhao, Haibin. "Multisensor integration and discrete geometry processing for coordinate metrology". Cachan, Ecole normale supérieure, 2010. http://tel.archives-ouvertes.fr/tel-00451244/fr/.
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L'intégration de systèmes multi-capteurs est la réponse la plus adaptée aux besoins croissants en matière de complexité et d'exactitude de mesures en métrologie par coordonnées. Des efforts importants sont consacrés à développer des systèmes multi-capteurs qui permettent d'assurer une mesure globale de la géométrie avec une incertitude réduite. Cette thèse a pour objectif l'intégration de systèmes multi-capteurs en métrologie par coordonnées selon deux aspects: la modélisation des fonctions et des données et le traitement de la géométrie discrète. La modélisation des fonctions et des données permet une meilleure compréhension des exigences des fonctions du système et des spécifications des données pour l'intégration de systèmes multi-capteurs. Des modèles hiérarchiques fonctionnels du système sont construits en utilisant la méthodologie IDEF0 et sont décomposés en quatre activités de base. Une démarche à base d'ontologies est utilisée pour modéliser les différentes données et leur représentation structurée. Quatre domaines de données et de leurs relations sont spécifiés pour gérer les données multiples dans l'intégration des systèmes multi-capteurs. Des méthodes efficaces et robustes pour le traitement de la géométrie discrète dans le cadre de l'intégration multi-capteurs sont l'objet principal de cette thèse. Une estimation fiable de la normale et des courbures est essentielle pour le traitement des formes discrètes. Une méthode basée sur le tenseur de courbure est utilisée pour l'estimation des courbures discrètes. Une analyse comparative montre que la méthode proposée fournit de meilleures performances par rapport à d'autres méthodes existantesMultisensor 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
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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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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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Abmayr, Thomas. "Calibration and registration framework for multisensor panoramic color scanning". Aachen Shaker, 2009. http://d-nb.info/999731661/04.
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Wong, Yee Chin. "Target tracking in a multisensor environment using neural networks". Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/298748.
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The advent of technology has brought to the field of engineering many tools that were once considered impractical. For example, the increased processing speed of microprocessors now allows measurements from image sensors to be used for target tracking or target identification in real time--a task once thought unachievable. Of late the advances made in artificial intelligence (AI), specifically the artificial neural network, have sprung many different applications among which the implementation of AI controllers being the most popular. However, these advances have been slow in their implementation in the field of target tracking for several reasons. First, there seems to be a lack of sound tracking architectures that can exploit the use of artificial intelligent agents. Second, there is some difficulty in fusing the different forms of information that can be measured from the various available sensors such as the image sensor, millimeter wave radar, Doppler radar, etc. Third, the increased computational complexity due to the employment of the various sensors could limit the practical usefulness of such a tracking system. This dissertation presents a novel framework in which various dissimilar sensors can be used simultaneously to track a highly agile and non-cooperative target. The proposed framework not only allows the usage of multiple sensors to yield a robust and accurate tracker but also maintain a reasonable computational requirement. Unlike the methods proposed in the literature for the design of multi-sensor tracking systems, this dissertation presents an AI-based system that can accept, process, and fuse measurements from any number of sensors of dissimilar forms. The principal contributions of this dissertation are the following: (i) a novel architecture of a three-layer feedforward neural-network-based tracking system with the ability to fuse measurements from dissimilar sensors; (ii) a powerful optimization algorithm for training the neural network; (iii) a novel mathematical target motion model to simplify the training and implementation of the proposed tracking system.
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Ogaja, 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.
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Due to structural damages from earthquakes and strong winds, engineers and scientists have focused on performance based design methods and sensors directly measuring relative displacements. Among the monitoring methods being considered include those using Global Positioning System (GPS) technology. However, as the technical feasibility of using GPS for recording relative displacements has been (and is still being) proven, the challenge for users is to determine how to make use of the relative displacements being recorded. This thesis proposes a mathematical framework that supports the use of RTK-GPS and multisensor data for structural monitoring. Its main contributions are as follows: (a) Most of the emerging GPS-based structural monitoring systems consist of GPS receiver arrays (dozens or hundreds deployed on a structure), and the issue of integrity of the GPS data generated must be addressed for such systems. Based on this recognition, a methodology for integrity monitoring using a data redundancy approach has been proposed and tested for a multi-antenna measurement environment. The benefit of this approach is that it verifies the reliability of both the measuring instruments and the processed data contrary to the existing methods that only verifies the reliability of the processed data. (b) For real-time structural monitoring applications, high frequency data ought to be generated. A methodology that can extract, in real-time, deformation parameters from high frequency RTK measurements is proposed. The methodology is tested and shown to be effective for determining the amplitude and frequency of structural dynamics. Thus, it is suitable for the dynamic monitoring of towers, tall buildings and long span suspension bridges. (c) In the overall effort of deformation analysis, large quantities of observations are required, both of causative phenomena (e.g., wind velocity, temperature, pressure), and of response effects (e.g., accelerations, coordinate displacements, tilt, strain, etc.). One of the problems to be circumvented is that of dealing with excess data generated both due to process automation and the large number of instruments employed. This research proposes a methodology based on multivariate statistical process control whose benefit is that excess data generated on-line is reduced, while maintaining a timely response analysis of the GPS data (since they can give direct coordinate results). Based on the above contributions, a demonstrator software system was designed and implemented for the Windows operating system. Tests of the system with datasets from UNSW experiments, the Calgary Tower monitoring experiment in Canada, the Xiamen Bank Building monitoring experiment in China, and the Republic Plaza Building monitoring experiment in Singapore, have shown good results.
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Ruiz, 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.
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En esta tesis presentamos la arquitectura de un sistema de localización que hace uso de los datos obtenidos a partir de los múltiples sensores disponibles en los smartphones actuales. Nuestro principal objetivo es ofrecer una solución capaz de soportar servicios basados en localización precisos, tales como aplicaciones de realidad aumentada, alcanzando un buen equilibrio entre la precisión y el rendimiento obtenidos. Presentaremos la arquitectura que abarca el diseño de nuestro sistema propuesto. Dicha arquitectura soporta el diseño y desarrollo de servicios capaces de combinar sensores de distinta naturaleza. Su estructura organizada en capas así como la especificación detallada de sus entidades, permite soportar diferentes configuraciones de sistemas. En primer lugar nos centramos en el análisis de técnicas de localización basadas en huellas de señales IEEE 802.11, siendo capaces de determinar la posición de un dispositivo con unos pocos metros de error de estimación. Para mejorar la eficiencia de nuestra solución se proponen varias mejora basadas en la integración de sensores adicionales, tales como la cámara o sensores inerciales. Haciendo uso de técnicas para la extracción de características invariantes a escala de imágenes, somos capaces de mejorar de forma notable la fiabilidad de nuestra solución. Además, damos un paso adelante en el análisis de imágenes mediante la integración de técnicas de visión por computador tales como visual structure from motion. Esto nos permite hacer reconstrucciones 3D del entorno en el que nos encontramos, y aplicando técnicas de resección de imagen, somos capaces de estimar de forma precisa la posición 3D y la rotación de un dispositivo en el espacio, llegando a obtener precisiones alrededor de los 15 centímetros de error de media. Nuestra propuesta multisensor trabaja en dos fases diferentes. En primer lugar se obtiene una estimación de grano grueso basándonos en las señales WiFi, la brújula digital y el acelerómetro. Para ello utilizamos técnicas basadas en mapas de huellas, técnicas probabilísticas y estimadores de movimiento. Después, haciendo uso de las imágenes capturadas por la cámara, se lleva a cabo un proceso de análisis de imágenes centrándonos en la zona concreta del modelo 3D construido delimitada por la estimación previa obtenida. Debido a las dificultades encontradas para construir modelos 3D precisos en entornos grandes y repetitivos, durante la fase de entrenamiento nuestra propuesta hace uso de técnicas de procesamiento de datos obtenidos mediante unidades de medición inerciales (IMU). Este proceso nos permite reducir los problemas de escalabilidad relacionados con la aparición de elementos estructurales similares en grandes escenarios de interior. El hecho de obtener modelos 3D precisos del entorno mejora la eficiencia de las técnicas de resección de cámara. En consecuencia, el error de estimación disminuye hasta los 5 centímetros de media, con tiempos de respuesta por debajo de los 250 milisegundos. El conjunto de técnicas presentas soporta una amplia variedad de aplicaciones basadas en localización, desde aquellas que requieren un posicionamiento aproximado hasta aquellas con altos requisitos de precisión.In 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.
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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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Hausmann, Felix [Verfasser]. "Entwicklung einer Multisensor-Heissfilmtechnik zur Transitionserkennung im Reiseflug / Felix Hausmann". Aachen : Shaker, 2004. http://d-nb.info/1172609527/34.
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Anglesea, 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.
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Mirabadi, 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.
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Atkinson, 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.
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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (leaves 60-61).
by Kevin E. Atkinson.
M.Eng.and S.B.
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Nguyen, Trang. "Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking". ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/20.
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Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem.
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Vicari, 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.
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Esse trabalho utilizou sensores remotos, em escala local e regional, para caracterizar o padrão espectral da canola e propor metodologias de criação de máscaras de cultivo, através da classificação de imagens de satélite, e de geração de estimativas de variáveis biofísicas, a partir de índices de vegetação medidos ao longo do ciclo. As medições das variáveis biofísicas foram realizadas em parcelas experimentais, na Embrapa-Trigo no município de Coxilha, e em lavouras monitoradas nas mesorregiões Nordeste e Noroeste do Rio Grande do Sul, para as safras 2013 e 2014. As variáveis biofísicas medidas foram altura de plantas, matéria seca das folhas, da haste e das síliquas e, também, foi estimado o índice de área foliar. Os dados espectrais para as parcelas experimentais foram obtidos através de um espectrorradiômetro. Para as lavouras monitoradas, os dados espectrais foram obtidos dos produtos MCD43B4 e MOD09A1, medidos pelo sensor MODIS (satélites Terra/Aqua), e de imagens do sensor OLI (satélite Landsat 8). A partir destes foi realizada a caracterização espectral da canola ao longo do seu ciclo de desenvolvimento, gerando perfis completos e perfis das bandas espectrais. Os índices de vegetação foram utilizados para caracterizar o padrão espectral e para a criação de modelos de estimativas das variáveis biofísicas, os quais foram calculados usando as bandas espectrais simuladas. Os índices de vegetação foram utilizados para classificar as áreas cultivadas com canola para as mesorregiões Nordeste e Noroeste do Rio Grande do Sul e, posteriormente, aplicados os modelos de estimativas de variáveis biofísicas. A caracterização do padrão espectral da canola foi consistente entre os dois anos avaliados e para todos os sensores, com variação temporal semelhante a outras culturas agrícolas, exceto pela redução nos índices de vegetação durante a floração da cultura. Os modelos de estimativa das variáveis biofísicas, apresentaram coeficientes de determinação elevados, com exceção das variáveis matéria seca das folhas e índice de área foliar. A classificação da área cultivada com canola, utilizando os produtos MODIS, apresentou resultados coerentes com o esperado de acordo com dados de série histórica, apresentados pela CONAB. As estimativas de variáveis biofísicas mostraram coerência com os obtidos pelas medições nas lavouras monitoradas. Os resultados obtidos nesse estudo demonstram, portanto, o potencial da utilização de dados espectrais multisensor para o mapeamento de lavouras e realização de estimativas de variáveis biofísicas da cultura da canola.This 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.
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Pawlak, Robert James. "Multisensor track initiation method that addresses the missing measurement problem". Diss., Virginia Tech, 1992. http://hdl.handle.net/10919/38631.
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Kneissler, Andreas. "Multisensor-Strategie zur Optimierung des Anwalzverhaltens beim Radial-Axial-Ringwalzen". Aachen Shaker, 2008. http://d-nb.info/993898289/04.
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Kim, Youngwook. "Multisensor Translation and Continuity of Vegetation Indices Using Hyperspectral Data". Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/193681.
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The earth surface is monitored periodically by numerous satellite sensors which have different spectral response functions, image acquisition heights, atmosphere correction schemes, overpass times, and sun/view angle geometries. Temporal and spatial variations of land surface properties, such as vegetation index, Leaf Area Index (LAI), land surface temperature, and soil moisture, have been provided by long-term time series of various remote sensing datasets. Inter-sensor translation equations are required to build long-term time series by the combination of multiple sensors from historical to advanced and new satellite datasets. In the first chapter, inter-sensor translation equations of band reflectances and two vegetation indices (e.g. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) were derived using linear regression equations relative to Moderate Resolution Imaging Spectroradiometer (MODIS) values. The consistency and validation of inter-sensor transforms were investigated through statistical student's t-test and the root mean square error (RMSE).In the second chapter, cross-sensor extension of EVI and a 2-band EVI (without the blue band; EVI2) were investigated based on the continuity of both EVI's. Sensor specific red-blue coherencies were examined for the possibility of the EVI and EVI2 extension from MODIS sensor. The EVI continuity to MODIS was particularly problematic for the Visible Infrared Imager / Radiometer Suite (VIIRS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that have dissimilar blue bands from that of MODIS. The cross-sensor extension and compatibility of EVI2 were improved and provided the possibility to be lengthened to the Advanced Very High Resolution Radiometer (AVHRR) using its translation equation.Finally, we evaluated the use of sensor-specific EVI and NDVI data sets, using a time sequence of Hyperion images over Amazon rainforest in Tapajos National Forest, Brazil for the 2001 and 2002 dry seasons. We computed NDVI, EVI, and EVI2 with the convolution data of different global monitoring and high temporal resolution sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) from Hyperion, and evaluated their spectral deviations and continuity in the characterization of tropical forest phenology. Our analyses show that EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems evaluated.
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Respati, 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.
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Road traffic congestion is an increasing societal problem. Road agencies and users seeks accurate and reliable travel speed information. This thesis developed a network-scale traffic state prediction based on Convolutional Neural Network (CNN). The method can predict the speed over the network accurately by preserving road connectivity and incorporating historical datasets. When dealing with an extensive network, the thesis also developed a clustering method to reduce the complexity of the prediction. By accurately predict the traffic state over a network, traffic operators can manage the network more effectively and travellers can make informed decision on their journeys.
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Olson, 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.
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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, September 1999.Thesis advisor(s): Titus, Harold A. ; Loomis, Herschel. "September 1999". Includes bibliographical references (p. 79). Also available online.
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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.
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This thesis investigates the use of an auxiliary sensor, the GEMS device, for improving the quality of noisy speech and designing noise preprocessors to MELP speech coders. Use of auxiliary sensors for noise-robust
ASR applications is also investigated to develop speech enhancement algorithms that use acoustic-phonetic
properties of the speech signal.
A Bayesian risk minimization framework is developed that can incorporate the acoustic-phonetic properties
of speech sounds and knowledge of human auditory perception into the speech enhancement framework. Two noise suppression
systems are presented using the ideas developed in the mathematical framework. In the first system, an aharmonic
comb filter is proposed for voiced speech where low-energy frequencies are severely suppressed while
high-energy frequencies are suppressed mildly. The proposed
system outperformed an MMSE estimator in subjective listening tests and DRT intelligibility test for MELP-coded noisy speech.
The effect of aharmonic
comb filtering on the linear predictive coding (LPC) parameters is analyzed using a missing data approach.
Suppressing the low-energy frequencies without any modification of the high-energy frequencies is shown to
improve the LPC spectrum using the Itakura-Saito distance measure.
The second system combines the aharmonic comb filter with the acoustic-phonetic properties of speech
to improve the intelligibility of the MELP-coded noisy speech.
Noisy speech signal is segmented into broad level sound classes using a multi-sensor automatic
segmentation/classification tool, and each sound class is enhanced differently based on its
acoustic-phonetic properties. The proposed system is shown to outperform both the MELPe noise preprocessor
and the aharmonic comb filter in intelligibility tests when used in concatenation with the MELP coder.
Since the second noise suppression system uses an automatic segmentation/classification algorithm, exploiting the GEMS signal in an automatic
segmentation/classification task is also addressed using an ASR
approach. Current ASR engines can segment and classify speech utterances
in a single pass; however, they are sensitive to ambient noise.
Features that are extracted from the GEMS signal can be fused with the noisy MFCC features
to improve the noise-robustness of the ASR system. In the first phase, a voicing
feature is extracted from the clean speech signal and fused with the MFCC features.
The actual GEMS signal could not be used in this phase because of insufficient sensor data to train the ASR system.
Tests are done using the Aurora2 noisy digits database. The speech-based voicing
feature is found to be effective at around 10 dB but, below 10 dB, the effectiveness rapidly drops with decreasing SNR
because of the severe distortions in the speech-based features at these SNRs. Hence, a novel system is proposed that treats the
MFCC features in a speech frame as missing data if the global SNR is below 10 dB and the speech frame is
unvoiced. If the global SNR is above 10 dB of the speech frame is voiced, both MFCC features and voicing feature are used. The proposed
system is shown to outperform some of the popular noise-robust techniques at all SNRs.
In the second phase, a new isolated monosyllable database is prepared that contains both speech and GEMS data. ASR experiments conducted
for clean speech showed that the GEMS-based feature, when fused with the MFCC features, decreases the performance.
The reason for this unexpected result is found to be partly related to some of the GEMS data that is severely noisy.
The non-acoustic sensor noise exists in all GEMS data but the severe noise happens rarely. A missing
data technique is proposed to alleviate the effects of severely noisy sensor data. The GEMS-based feature is treated as missing data
when it is detected to be severely noisy. The combined features are shown to outperform the MFCC features for clean
speech when the missing data technique is applied.