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Gonos, Theophile. "Bio-inspired adaptive sensing". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6217.
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Sensor array calibration is a major problem in engineering, to which a biological approach may provide alternative solutions. For animals, perception is relative. The aim of this thesis is to show that the relativity of perception in the animal kingdom could also be applied to robotics with promising results. This thesis explores through various behaviours and environments the properties of homeostatic mechanisms in sensory cells. It shows not only that the phenomenon can solve partial failure of sensors but also that it can be used by robots to adapt to their (changing) environment. Moreover the system shows emergent properties as well as adaptation to the robot body or its behaviour. The homeostatic mechanisms in biological neurons maintain fi ring activity between predefi ned ranges. Our model is designed to correct out of range neuron activity over a relatively long period of time (seconds or minutes). The system is implemented in a robot’s sensory neurons and is the only form of adaptability used in the central network. The robot was fi rst tested extensively with a mechanism implemented for obstacle avoidance and wall following behaviours. The robot was not only able to deal with sensor manufacture defects, but to adapt to changing environments (e.g. adapting to a narrow environment when it was originally in an open world). Emergence of non-implemented behaviours has also been observed. For example, during wall following behaviour, the robot seemed, at some point, bored. It changed the direction it was following the wall. Or we also noticed during obstacle avoidance an emerging exploratory behaviour. The model has also been tested on more complex behaviours such as skototaxis, an escape response, and phonotaxis. Again, especially with skototaxis, emergent behaviours appeared such as unpredictability on where and when the robot will be hiding. It appears that the adaptation is not only driven by the environment but by the behaviour of the robot too. It is by the complex feedback between these two things that non-implemented behaviours emerge. We showed that homeostasis can be used to improve sensory signal processing in robotics and we also found evidence that the phenomenon can be a necessary step towards better behavioural adaptation to the environment.
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Hyttinen, Emil. "Adaptive Grasping Using Tactile Sensing". Licentiate thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206395.
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Grasping novel objects is challenging because of incomplete object data and because of uncertainties inherent in real world applications. To robustly perform grasps on previously unseen objects, feedback from touch is essential. In our research, we study how information from touch sensors can be used to improve grasping novel objects. Since it is not trivial to extract relevant object properties and deduce appropriate actions from touch sensing, we employ machine learning techniques to learn suitable behaviors. We have shown that grasp stability estimation based on touch can be improved by including an approximate notion of object shape. Further we have devised a method to guide local grasp adaptations based on our stability estimation method. Grasp corrections are found by simulating tactile data for grasps in the vicinity of the current grasp. We present several experiments to demonstrate the applicability of our methods. The thesis is concluded by discussing our results and suggesting potential topics for further research.Att greppa nya föremål är utmanande, både eftersom roboten inte har fullständig information om objekten och på grund av den inneboende osäkerheten i verkliga tillämpningar. Återkoppling från känselsensorer är viktigt för att kunna greppa föremål som inte påträffats tidigare. I vår forskning så studerar vi hur information från känselsensorer kan användas för att förbättra greppandet av nya föremål. Eftersom det är svårt att extrahera relevanta egenskaper om föremål och härleda lämpliga åtgärder, baserat på känselsensorer, så har vi använt maskininlärning för att lära roboten lämpliga beteenden. Vi har visat att uppskattningar av stabiliteten av ett grepp baserat på känselsensorer kan förbättras genom att även använda en grov approximation av föremålets form. Vi har även konstruerat en metod som vägleder lokala justeringar av grepp, baserat på vår metod som uppskattar stabiliteten av ett grepp. Dess justeringar hittas genom att simulera känselsensordata för grepp i närheten av det nuvarande greppet. Vi presenterar flera experiment som demonstrerar tillämpbarheten av våra metoder. Avhandlingen avslutas med en diskussion om våra resultat och förslag på möjliga ämnen för fortsatt forskning.
QC 20170510
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Rachuri, Kiran Kumar. "Smartphones based social sensing : adaptive sampling, sensing and computation offloading". Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648104.
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Vakili, Arash. "Adaptive spectrum sensing for cognitive radio networks". Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106425.
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Spectrum sensing is an important functionality of cognitive radio as a means to detect the presence or absence of the primary user (PU) in a certain spectrum band. Energy detection is a widely used spectrum sensing technique based on the assumption that the PU is either present or absent during the whole sensing period. However, this assumption is not realistic in a dynamic environment where the PU could appear or disappear at any time. The performance of the conventional energy detector (ED) actually deteriorates in the scenario where the PU activity status changes during the sensing period.Therefore, it is crucial to design a detector which can adapt to such an environment and reliably detect a change in the PU activity. Several sequential change detection techniques already exist in the literature; however, change detection in a fixed sensing duration has not been given enough attention. In this dissertation, three adaptive EDs are proposed to improve the detection performance in dynamic environments, where there is a single change in the PU activity during a fixed sensing period. In particular, we address the change detection problem using an exponential weighting approach and two theoretical approaches based on the composite hypothesis testing. In the first case, an intuitive idea of exponential weighting of the received energies is applied to design an adaptive ED that aims to satisfy the Neyman-Pearson (NP) criterion. The performance analysis and simulation results prove that the proposed adaptive ED outperforms the conventional ED and also the only existing adaptive ED in the literature that deals with the aforementioned issue. In the second case, two theoretical approaches based on the composite hypothesis testing are used to design two additional adaptive EDs that improve the change detection during the sensing period. The first approach, known as the generalized likelihood ratio test (GLRT), uses the maximum likelihood estimation (MLE) of the unknown change location in a likelihood ratio test. In this case, an iterative method is proposed to reduce the computational complexity of the MLE process. The second approach, referred to as composite-Bayesian, assumes that the unknown change location is a discrete random variable whose probability mass function (PMF) is available. The PU channel access pattern is modelled as a two-state Markov chain to obtain the PMF of the change location and the probability of occurrence of the two hypotheses. The resultant adaptive ED based on the GLRT approach aims to satisfy the NP criterion while the adaptive ED based on the composite-Bayesian approach aims to minimize the probability of error. It is demonstrated through simulations that these two proposed adaptive EDs have superior performance over the conventional ED. Furthermore, the GLRT-based adaptive ED outperforms the first proposed adaptive ED based on the exponential weighting approach.La détection de spectre est une fonctionnalité importante de la radio cognitive car elle permet de vérifier la présence ou l'absence d'un utilisateur principal (PU) sur une bande de spectre donnée. La détection de l'énergie est une méthode fréquemment utilisée pour y parvenir.Cette dernière s'appuie sur l'hypothèse que le PU est présent ou absent pour la totalité de la période de mesure. Cependant, cette hypothèse n'est pas réaliste pour un environnement dynamique dans lequel le PU peut apparaître ou disparaître à n'importe quel instant. En effet, les performances d'un détecteur d'énergie conventionnel (ED) se détériorent lorsque l'état du PU varie au cours de la période durant laquelle les mesures sont effectuées. C'est donc pour cette raison qu'il est nécessaire de concevoir un détecteurqui s'adapte bien à ce genre d'environnement et qui permet de détecter de manière fiable tout changement dans l'activité du PU. Plusieurs techniques de détection de changements séquentiels existent dans la littérature mais la détection de changement pour une durée fixe n'a pas été explorée suffisamment en détails. Dans le cadre de ce mémoire, trois EDs adaptatifs sont proposés dans le but d'améliorer les performances dans un environnement dynamique au sein duquel il y a un seul changement au niveau de l'activité du PU et ce durant une période de mesure de durée fixe. Pour tenter de résoudre cette problématique, une approche à pondération exponentielle et deux approches théoriques en lien avec le test d'hypothèse composée sont proposées. Dans le premier cas, une approche intuitive exploitant la pondération exponentielle de l'énergie mesurée est utilisée afin de concevoir un ED adaptatif qui satisfait le critère de Neyman-Pearson (NP). L'analyse des performances et des résultats de simulation prouvent que cette stratégie offre de meilleures performances par rapport aux ED conventionnels. Il s'agit également du seul ED adaptatif présent dans la littérature qui tente de résoudre la problématique précédemment mentionnée. Dans le second cas, deux approches théoriques fondées sur le test d'hypothèse composée sont utilisées afin de concevoir deux nouveaux EDs adaptatifs qui améliorent la détection de changements durant la période de mesure. La première approche s'appuie sur le test généralisé de vraisemblance (GLRT) et utilise une estimation de la vraisemblance maximale (MLE) de la position inconnue du changement. Dans ce cas, une méthode itérative est proposée pour réduire la complexité de calcul du processus de MLE. La deuxième approche, dite composée bayésienne, prend pour acquis que la position inconnue du changement est une variable aléatoire discrète dont la loi de probabilité (PMF) est connue. Pour cette dernière approche, les accès au canal sont modélisés par un modèle de Markov à deux états afin d'obtenir la PMF de la position du changement et la probabilité d'occurrence des deux hypothèses. Le ED adaptatif utilisant le GLRT tente de satisfaire le critère de NP tandis que le ED adaptatif utilisant l'approche de la composée bayésienne tente de minimiser la probabilité d'une erreur. Il est démontré à l'aide de simulations que ces deux EDs adaptatifs offrent des performances supérieures à celles du ED conventionnel. En outre, le ED adaptatif utilisant le GLRT surpasse le ED adaptive utilisant l'approche pondération exponentielle.
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Oag, Thomas J. D. "Interferometric wavefront sensing for extreme adaptive optics". Thesis, Durham University, 2004. http://etheses.dur.ac.uk/3101/.
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Adaptive optics is concerned with the correction of phase distortions in wavefronts which degrade the quality of images produced by optical systems. It was originally developed for both astronomy and the military, where the Earth's atmosphere causes distortions, although other uses are now being developed. As ground based telescopes become increasingly large the size and complexity of adaptive optics systems also increase, creating "extreme adaptive optics”. This thesis deals with such an adaptive optics system. A novel self referenced phase shifting interferometer based on a liquid crystal (LC) waveplate is presented which can measure high spatial frequency phase distortions. This is then coupled to a LC spatial light modulator wavefront corrector. The geometry is matched such that there is no need for a wavefront reconstructor. The performance is measured in two stages. Firstly, spatially where static phase distortions are measured by the interferometer and corrected. Secondly, temporally where a simple analogue feedback is implemented to show correction over a single corrector pixel for fast time varying phase distortions. This work builds on other published research on using point diffraction interferometry in adaptive optics. The novelty lies in the development of a new implementation of a point diffraction interferometer, and in the demonstration of a high-speed closed loop single channel system. This work therefore contributes to the groundwork required to build an extreme adaptive optics system whose complexity scales linearly with the size (area) of the telescope aperture.
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Ash, Darren. "Optimal wavefront sensing for adaptive optics systems". Thesis, University of Kent, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297368.
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Roddier, Nicolas 1965. "Curvature sensing for Adaptive Optics: A computer simulation". Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/291948.
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This thesis describes computer simulations of a new wavefront sensing technique for Adaptive Optics based on local wavefront curvature measurements, along with edge slope measurements. The output signal from the curvature measurements, along with edge slope measurements. The output signal from the curvature sensor can be directly applied to the electrodes of a bimorph or membrane mirror. The mirror is used as an analog device to solve the Poisson Equation, providing a fast real time compensation for atmospheric disturbances. The open loop characteristics of the system are presented. The ideal response is analyzed, and side effects such as non-linearity, photon and diffraction noises are discussed. Closed loop simulations are presented thereafter. A seven actuator system showed a few unstable modes. A 13 actuator system with proper filtering corrects the atmospheric perturbations. To simulate atmospheric distorted wavefronts, an algorithm based on spectral decomposition of the Zernike covariance matrix was derived. This sensor can also be used to test large telescope mirrors using a modified program that solves the Poisson Equation with Neumann boundary conditions.
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van, Dam Marcos Alejandro. "Wave-front sensing for adaptive optics in astronomy". Thesis, University of Canterbury. Electrical and Computer Engineering, 2002. http://hdl.handle.net/10092/1149.
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Optical images of astronomical objects viewed through ground-based telescopes are blurred by the atmosphere. The atmosphere is turbulent and as a consequence the density of air is not evenly distributed. This results in random, time-varying variations in refractive index. The wave-fronts passing through the atmosphere become aberrated, degrading the quality of the images. One solution is to include an adaptive optics system in the telescope. The system estimates the aberration of the wave-fronts and compensates the wave-front in real time using a corrector element, typically a deformable mirror. An important problem is how to estimate the aberrations optimally using only a small amount of light. This procedure is called wave-front sensing and is the subject of the research of this thesis. For turbulence with Kolmogorov statistics, the wave-front slope contains 87% of the energy of the aberrations. Hence, it is crucial to estimate the slope accurately. The displacement of an image is directly proportional to the wave-front slope and is used to estimate the slope. The conventional way of measuring the average slope of the wave-front in a Shack-Hartmann sensor is from the centroid of the image at the focal plane. It is demonstrated that using the centroid estimator produces an estimate with infinite variance. The Cramer-Rao lower bound (CRLB) is a theoretical lower bound for the variance of an unbiased estimator. The variance of the maximum-likelihood (ML) estimate for the displacement of a diffraction-limited image approaches the CRLB using a relatively small number of photons. The ML estimator is extended to the case where the image is randomly blurred by atmospheric turbulence. It is found that the variance of the error of the slope estimator can be improved significantly at low turbulence levels by using the ML estimator instead of the centroid. Curvature sensors use two defocused images to estimate the wave-front aberrations. It is shown using the CRLB that the focal plane is the optimal plane to measure the slope and the error using defocused images is quantified. The effect of using broadband light on the accuracy of the slope estimate is also investigated. When using laser guide stars, it is not possible to estimate the slope of the wave-front directly from the image because the beam is displaced on both the upward and downward journey. However, the displacement is a weak function of wavelength due to dispersion. In theory, the difference in wave-front slope as a function of wavelength is proportional to the absolute slope. Centering algorithms were implemented on experimental data taken at the Observatoire de Lyon to confirm this relationship. There is strong evidence pointing to a linear relationship between two pairs of differential tilt measurements, but not between the differential and the absolute tilt. However, the data appears to have been affected by a systematic experimental error and a new experiment is needed. Phase retrieval is a non-linear technique used to recover the phase in the Fourier domain using intensity measurements at the image plane and additional constraints. A method is described to solve the phase retrieval problem using linear iterations near the solution, which provides both analytical insight into phase retrieval and numerical results. The algorithm finds the maximum a posteriori estimate of the phase using prior information about the statistics of the noise and the phase and converges well in practice. When phase retrieval is performed on data from subdivided apertures, there is a loss of information regarding the relative piston terms of the subapertures and this error is quantified. It is found that there is a smaller wavefront error when estimating the phase from a full aperture than from a subdivided aperture. Using a combination of intensity measurements from a full and a subdivided aperture is shown to result in a small improvement at very high photon levels only. Curvature sensors measure the wave-front aberrations via a linear relationship between the curvature of the wave-front and the intensity difference between two defocused images. In practice, their performance is limited by their non-linear behaviour, which is characterised by solving simultaneously the irradiance transport equation and the accompanying wave-front transport equation. It is shown how the presence of non-linear geometric terms limits the accuracy of the sensor and how diffraction effects limit the spatial resolution. The effect of photon noise on the sensor is also quantified. A novel technique for deriving wave-front aberrations from two defocused intensity measurements is derived. The intensity defines a probability density function and the method is based on the evolution of the cumulative density function of the intensity. In one dimension, the problem is easily solved using histogram specification with a linear relationship between the wave-front slope and the difference in the abscissas of the histograms. This method is insensitive to scintillation. In two dimensions, the procedure requires the use of the Radon transform. Simulation results demonstrate that very good reconstructions can be attained down to 100 photons in each detector.
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Al, Marzouqi Hasan. "Curvelet transform with adaptive tiling". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52961.
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In this dissertation we address the problem of adapting frequency domain tiling using the curvelet transform as the basis algorithm.
The optimal tiling, for a given class of images, is computed using denoising performance as the cost function. The major adaptations considered are: the number of scale decompositions, angular decompositions per scale/quadrant, and scale locations. A global optimization algorithm combining the three adaptations is proposed. Denoising performance of adaptive curvelets is tested on seismic and face data sets. The developed adaptation procedure is applied to a number of different application areas. Adaptive curvelets are used to solve the problem of sparse data recovery from subsampled measurements. Performance comparison with default curvelets demonstrates the effectiveness of the adaptation scheme. Adaptive curvelets are also used in the development of a novel image similarity index. The developed measure succeeds in retrieving correct matches from a variety of textured materials. Furthermore, we present an algorithm for classifying different types of seismic activities.
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Ruggiu, Jean-Marc. "Optimal control for adaptive optics". Thesis, University of Kent, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342157.
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Mudhireddy, Venkata Naresh. "STUDY OF ADAPTIVE COMPRESSIVE SENSING FOR LOW POWER APPLICATIONS". OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1318.
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Compressive sensing (CS) technique potentially allows sparse signals to be sampled at rates lower than their Nyquist Rates, making it appealing for implementation of low-power sensors. This dissertation investigates techniques to further improve CS efficiency by adaptively adjusting the sampling rates or CS measurement sizes according to signal sparsity variations. The proposed technique is referred to as adaptive compressive sensing (ACS). The work first investigates the feasibility to perform ACS operation. The study indicates that the sparsity of many biomedical signals does change over the time and therefore such adaptive operations are feasible. This study further investigates the potential power saving by the proposed ACS operations and it shows significant power reduction, up to 11.02 to 32.18 percent, can be potentially achieved by the proposed ACS operation, in wireless sensor nodes whose power consumption is dominated by the transmitter power consumption. Finally, the potentials of using analog wavelet filter circuits to monitor the changes of signal sparsity is investigated. The capability to detect signal sparsity changes at the sensor node is critical for the practical implementation of the proposed ACS operation. The proposed sparsity detection circuit comprise of low-power analog wavelet circuits and additional simple circuits that count the number of threshold crossing at the output of the analog wavelet circuits. Simulation results demonstrate promising potentials of the proposed method.
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Aggarwal, Vikas. "Remote sensing and imaging in a reconfigurable computing environment". [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012171.
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Lloyd, Jennifer Anne. "An integrated circuit pressure sensing system with adaptive linearity calibration". Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10447.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 145-152).
by Jennifer Anne Lloyd.
Ph.D.
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Townson, Matthew James. "Correlation wavefront sensing and turbulence profiling for solar adaptive optics". Thesis, Durham University, 2016. http://etheses.dur.ac.uk/11661/.
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Ground based telescopes suffer from degraded resolution due to aberrations induced by the atmosphere which prevent them from reaching the diffraction limit. Adaptive Optics (AO) is a technology which corrects for this effect in real-time, restoring the resolution of a telescope. However, it only corrects for a very narrow field of view (FOV) around the guide source. Tomographic AO uses multiple guide sources to increase the size of the corrected FOV, however, these forms of AO are affected by the vertical distribution of turbulence in the atmosphere (turbulence profile). This thesis presents work to develop turbulence profiling instruments for daytime astronomy and improve centroiding techniques for correlating wavefront sensors (WFS) which are used in slope based turbulence profiling instruments. The development of centroiding techniques for use on extended objects is based on cross-correlation techniques. Two methods are presented, one for optimising centroiding parameters on cross-correlation images and another for improving the signal to noise in cross-correlation images created from images with large relative shifts by using supersized reference images. Choosing optimal centroiding parameters for correlating WFSs is demonstrated in simulation, optimising a windowed, thresholded center of mass. The creation and use of supersized reference images is also demonstrated in simulation, where they are created from WFS data and shown to drastically improve the accuracy of centroiding for centroiding extended objects which have continuous structure across the whole field. So-SLODAR (solar-slope detection and ranging) was developed as a slope based instrument for measuring the turbulence profile on the Swedish Solar Telescope (SST), La Palma. The technique is based on SLODAR, with developments to take advantage of the continuous structure of the solar surface offering multiple guide sources. A full description of the technique and its data reduction is presented, along with the first results from on-sky tests on the SST.
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ZEESHAN, ZEESHAN. "Progress in Adaptive Electrical Capacitance Tomography". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502179655927844.
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Dunlop, Matthew, i Phillip Poon. "Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C)". International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579667.
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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NVThe AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.
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Eldeeb, Yasser M. "An adaptive system for enhancing vehicle bodies assembly using range sensing". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0011/NQ52420.pdf.
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Rowe, Keja S. "Autonomous data processing and behaviors for adaptive and collaborative underwater sensing". Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77025.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 73).
In this thesis, I designed, simulated and developed behaviors for active riverine data collection platforms. The current state-of-the-art in riverine data collection is plagued by several issues which I identify and address. I completed a real-time test of my behaviors to insure they worked as designed. Then, in a joint effort between the NATO Undersea Research Center (NURC) and Massachusetts Institute of Technology (MIT) I assisted the Shallow Water Autonomous Mine Sensing Initiative (SWAMSI)'11 experiment and demonstrated the viability of multi-static sonar tracking techniques for seabed and sub-seabed targets. By detecting the backscattered energy at the monostatic and several bi-static angles simultaneously, the probabilities of both target detection and target classification should be improved. However, due to equipment failure, we were not able to show the benefits of this technique.
by Keja S. Rowe.
M.Eng.
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Yang, Ming-Der. "Adaptive short-term water quality forecasts using remote sensing and GIS /". The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu148794273980509.
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Alahmadi, Mohammed. "A Recursive Approach for Adaptive Parameters Selection in AMultifunction Radar". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1448981863.
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Liu, Changgeng. "Coherent Digital Holographic Adaptive Optics". Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5527.
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A new type of adaptive optics (AO) based on the principles of digital holography (DH) is proposed and developed for the use in wide-field and confocal retinal imaging. Digital holographic adaptive optics (DHAO) dispenses with the wavefront sensor and wavefront corrector of the conventional AO system. DH is an emergent imaging technology that gives direct numerical access to the phase of the optical field, thus allowing precise control and manipulation of the optical field. Incorporation of DH in an ophthalmic imaging system can lead to versatile imaging capabilities at substantially reduced complexity and cost of the instrument. A typical conventional AO system includes several critical hardware pieces: spatial light modulator, lenslet array, and a second CCD camera in addition to the camera for imaging. The proposed DHAO system replaces these hardware components with numerical processing for wavefront measurement and compensation of aberration through the principles of DH.
We first design an image plane DHAO system which is basically simulating the process the conventional AO system and replacing the hardware pieces and complicated control procedures by DH and related numerical processing. In this original DHAO system, CCD is put at the image plane of the pupil plane of the eye lens. The image of the aberration is obtained by a digital hologram or guide star hologram. The full optical field is captured by a second digital hologram. Because CCD is not at the conjugate plane of the sample, a numerical propagation is necessary to find the image of the sample after the numerical aberration compensation at the CCD plane. The theory, simulations and experiments using an eye model have clearly demonstrated the effectiveness of the DHAO. This original DHAO system is described in Chapter 2.
Different from the conventional AO system, DHAO is a coherent imaging modality which gives more access to the optical field and allows more freedom in the optical system design. In fact, CCD does not have to be put at the image plane of the CCD. This idea was first explored by testing a Fourier transform DHAO system (FTDHAO). In the FTDHAO, the CCD can directly record the amplitude point spread function (PSF) of the system, making it easier to determine the correct guide star hologram. CCD is also at the image plane of the target. The signal becomes stronger than the image plane DHAO system, especially for the phase aberration sensing. Also, the numerical propagation is not necessary. In the FTDHAO imaging system, the phase aberration at the eye pupil can be retrieved by an inverse Fourier transform (FT) of the guide star hologram and the complex amplitude of the full field optical field at the eye pupil can be obtained by an inverse FT of the full field hologram. The correction takes place at the eye pupil, instead of the CCD plane. Taking FT of the corrected field at the eye pupil, the corrected image can be obtained. The theory, simulations, and experiments on FTDHAO are detailed in chapter 3.
The successful demonstration of FTDHAO encourages us to test the feasibility of putting CCD at an arbitrary diffraction plane in the DHAO system. Through theoretical formulation by use of paraxial optical theory, we developed a correction method by correlation for the general optical system to perform the DHAO. In this method, a global quadratic phase term has to be removed before the correction operation. In the formulation, it is quite surprising to find that the defocus term can be eliminated in the correlation operation. The detailed formulations, related simulations, and experimental demonstrations are presented in Chapter 4.
To apply the DHAO to the confocal retinal imaging system, we first transformed the conventional line-scanning confocal imaging system into a digital form. That means each line scan is turned into a digital hologram. The complex amplitude of the optical field from each slice of the sample and aberration of the optical system can be retrieved by digital holographic process. In Chapter 5, we report our experiments on this digital line-scanning confocal imaging system. This digital line-scanning confocal image absorbs the merits of the conventional line-scanning confocal imaging system and DH. High-contrast intensity images with low coherent noise, and the optical sectioning capability are made available due to the confocality. Phase profiles of the samples become accessible thanks to DH. The quantitative phase map is even better than that from the wide field DH.
We then explore the possibility of applying DHAO to this newly developed digital line-scanning confocal imaging system. Since optical field of each line scan can be achieved by the DH, the aberration contained in this field can be eliminated if we are able to obtain the phase aberration. We have demonstrated that the phase aberration can be obtained by a guide star hologram in the wide field DHAO systems. We then apply this technique to acquire the aberration at the eye pupil, remove this aberration from the optical fields of the line scans and recover the confocal image. To circumvent the effect of phase aberration on the line illumination, a small collimated laser beam is shone on the cylindrical lens. Thus the image is solely blurred by the second passage through the aberrator. This way, we can clearly demonstrate the effect of DHAO on the digital line-scanning confocal image system. Simulations and experiments are presented in chapter 6, which clearly demonstrates the validity of this idea. Since line-scanning confocal imaging system using spatially coherent light sources has proven an effective imaging tool for retinal imaging, the presented digital adaptive optics line-scanning confocal imaging system is quite promising to become a compact digital adaptive optics laser scanning confocal ophthalmoscope.
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Poon, Phillip K., i Phillip K. Poon. "Practical Considerations In Experimental Computational Sensing". Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/623022.
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Computational sensing has demonstrated the ability to ameliorate or eliminate many trade-offs in traditional sensors. Rather than attempting to form a perfect image, then sampling at the Nyquist rate, and reconstructing the signal of interest prior to post-processing, the computational sensor attempts to utilize a priori knowledge, active or passive coding of the signal-of-interest combined with a variety of algorithms to overcome the trade-offs or to improve various task-specific metrics. While it is a powerful approach to radically new sensor architectures, published research tends to focus on architecture concepts and positive results. Little attention is given towards the practical issues when faced with implementing computational sensing prototypes. I will discuss the various practical challenges that I encountered while developing three separate applications of computational sensors. The first is a compressive sensing based object tracking camera, the SCOUT, which exploits the sparsity of motion between consecutive frames while using no moving parts to create a psuedo-random shift variant point-spread function. The second is a spectral imaging camera, the AFSSI-C, which uses a modified version of Principal Component Analysis with a Bayesian strategy to adaptively design spectral filters for direct spectral classification using a digital micro-mirror device (DMD) based architecture. The third demonstrates two separate architectures to perform spectral unmixing by using an adaptive algorithm or a hybrid techniques of using Maximum Noise Fraction and random filter selection from a liquid crystal on silicon based computational spectral imager, the LCSI. All of these applications demonstrate a variety of challenges that have been addressed or continue to challenge the computational sensing community. One issue is calibration, since many computational sensors require an inversion step and in the case of compressive sensing, lack of redundancy in the measurement data. Another issue is over multiplexing, as more light is collected per sample, the finite amount of dynamic range and quantization resolution can begin to degrade the recovery of the relevant information. A priori knowledge of the sparsity and or other statistics of the signal or noise is often used by computational sensors to outperform their isomorphic counterparts. This is demonstrated in all three of the sensors I have developed. These challenges and others will be discussed using a case-study approach through these three applications.
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Robertson, Paul. "A self adaptive architecture for image understanding". Thesis, University of Oxford, 2001. http://ora.ox.ac.uk/objects/uuid:01f16b87-63be-4b55-9e52-14738fefed57.
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This thesis develops a self-adaptive architecture for image understanding that addresses certain kinds of lack of robustness common in image understanding programs. The architecture provides support for making image understanding programs that can manipulate their own semantics and thereby adjust their structure in response to changes in the environment that might cause static image understanding systems to fail. The general approach taken has been to explore the ideas of self-adaptive software and implement an architectural framework that addresses a class of problems that we term "interpretation problems" common in image understanding. Self-adaptive software is a relatively new idea and this thesis represents one of the first implementations of the general idea. The general idea is that to make programs robust to changing environmen- tal conditions that they should be "aware" of their relationship with the environment and be able to restructure themselves at runtime in order to "track" changes in the environment. The implementation takes the form of a multi-layered reflective interpreter that ma- nipulates and runs simple agents. The interpreter framework utilizes Monte-Carlo sam- pling as a mechanism for estimating most likely solutions, uses Minimum Descriptin Length (MDL) as a central coordinating device, and includes a theorem prover based compiler to restructure the program when necessary. To test the architectural ideas developed in the thesis a test domain of interpreting aerial images was chosen. Much of the research described in the thesis addresses issues in that problem domain. The task of the program is to segment, label, and parse aerial images so as to produce an image description similar to descriptions produced by a human expert. An image corpus is developed that is used as the source of domain knowledge. The first processing stage of the program segments the aerial images into segments similar to those found in the annotated corpus. To accomplish this a new segmentation algorithm that we call semantic segmentation was developed that not only used MDL as a principle to drive the low-level segmentation but also allows higher level semantics to influence the segmentation. In our usage of the algorithm those semantics take the form of labeling and parsing the resulting segmentation. The second stage labels the regions and parses the regions into a parse tree. To do this we develop a 2D statistical parser. Rules of grammar are induced from the corpus and an MDL parser finds approximations to the most probable parse of the regions of the segmented image.
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Schgallis, Richard J. "Phase diversity wavefront sensing for control of space based adaptive optics systems". Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Dec%5FSchgallis.pdf.
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Thesis (M.S. in Applied Physics)--Naval Postgraduate School, December 2007.Thesis Advisor(s): Agrawal, Brij ; Larazza, Andres. "December 2007." Description based on title screen as viewed on January 18, 2008. Includes bibliographical references (p. 53-54). Also available in print.
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Derakhshani, Mahsa. "Distributed opportunistic spectrum access via adaptive carrier sensing in cognitive radio networks". Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119593.
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The limitations of current static spectrum management policy drive the idea of a more dynamic access policy to improve the efficiency of radio spectrum usage and accommodate the increasing demand for wireless communication applications. Known as the opportunistic spectrum access (OSA), the new paradigm allows cognitive secondary users (SUs) to access the licensed spectrum, provided that the interference to the licensed primary users (PUs) is limited. In a cognitive radio network, since SUs are intended to track and take advantage of instantaneous spectrum opportunities, adaptive learning-based spectrum access schemes are desired to optimize spectrum utilization and ensure a peaceful coexistence of licensed and unlicensed systems. This thesis deals with the modeling, development and analysis of OSA schemes in a cognitive radio network from both SU and PU perspectives. The research objective is to maximize the overall throughput of SUs, while sufficiently protecting the ongoing operation of PUs.From the SU perspective, to avoid the high-risk data loss due to the random return of PUs, we present a dynamic hopping transmission strategy for SUs to access the temporarily idle frequency slots of a licensed frequency band, with adaptive activity factors. Upon applying the dual decomposition, the optimal activity factor allocation algorithm is developed. To facilitate spectrum sharing in a decentralized manner, we propose an adaptive carrier sense multiple access (CSMA) scheme. Based on the proposed CSMA scheme, learning-based distributed access algorithms for SUs are devised, including non-game-theoretic and game-theoretic approaches. The proposed algorithms can be independently performed by each SU to learn its optimal activity factors from the locally available information. To evaluate the effects of inevitable collisions among SUs in the proposed adaptive CSMA scheme, the collision probability and saturation throughput are studied by both analysis and simulation. Simulation results show significant performance improvements in terms of the achievable throughput compared to the conventional CSMA scheme. From the PU perspective, by applying the proposed access scheme to SUs, we study the interference caused by SUs to the PU due to miss-detection, and also its effects on the capacity-outage performance of the PU in a cognitive radio network. Based on the developed statistical models for the interference distribution, closed-form expressions for the capacity-outage probability of the PU are derived to examine the effects of various system parameters on the performance of the PU in the presence of interference from SUs. The model is extended to investigate the effects of cooperative sensing on the aggregate interference and the capacity-outage performance, considering OR (logical OR operation) and maximum likelihood cooperative detection techniques.Les limites de la politique d'utilisation statique du spectre ont conduit à l'idée d'une politique d'accès plus dynamique pour améliorer l'efficacité du spectre radio utilisé et accommoder l'augmentation de la demande des applications de communication sans fils. Connu sous le nom d'accès opportuniste au spectre (AOS), ce nouveau model permet à un utilisateur secondaire (US) cognitif d'accéder à un spectre licencié, tout en limitant l'interférence de l'utilisateur primaire (UP) licencié. Dans un réseau radio cognitif, puisque les USs sont sensés traquer et profiter des instants d'opportunité spectrale, des schémas d'accès spectral basés sur l'apprentissage adaptatif sont désirés pour optimiser l'utilisation spectrale et assurer une parfaite coexistence entre les systèmes licenciés et non licenciés. Cette thèse se consacre à la modélisation, au développement et à l'analyse des schémas d'AOS dans les réseaux radio cognitif du point de vue des UP et US. L'objectif de cette recherche est de maximiser le flux total des USs, tout en protégeant suffisamment le fonctionnement de l'UP. Du point de vue de l'US, afin d'éviter un risque élevé de perte de données causée par le retour aléatoire de l'UP, nous présentons une stratégie de transmission basée sur le saut dynamique pour les USs afin d'accéder aux blocs de fréquences temporairement libres dans une bande licenciée, avec un facteur d'activité adaptatif. Lors de l'application de la double décomposition, un algorithme d'allocation optimale des facteurs d'activité est développé. Afin de faciliter le partage du spectre de manière décentralisée, nous proposons un schéma adaptif basé sur la technique CSMA (accès multiple avec détection de porteuses). En se basant sur le schéma proposé, des algorithmes d'accès distribués pour les USs basés sur l'apprentissage sont conçus, incluant des approches basées sur la théorie des jeux et d'autres non. Les algorithmes proposés peuvent être utilisés indépendamment par chaque US pour apprendre son facteur d'activité optimal à partir de l'information localement disponible. Pour évaluer les effets de collisions inévitables entre les USs dans le schéma CSMA proposé, la probabilité de collision et le flux de saturation sont étudiés analytiquement et à travers des simulations. Les résultats des simulations démontrent une amélioration considérable de performance, particulièrement de point de vue de débit réalisé par rapport à celui réalisé selon le CSMA conventionnel.Du point de vue de l'UP, nous employons le schéma d'accès proposé pour les USs, et étudions l'interférence causée par les USs aux UPs à la suite d'une erreur de détection, ainsi que ses effets sur la capacité de coupure de l'UP dans un réseau radio cognitif. En se basant sur les modèles statistiques de la distribution de l'interférence, des expressions exactes de la probabilité de la capacité de coupure pour l'UP sont dérivées afin d'examiner les effets des différents paramètres du système sur la performance de l'UP en présence des USs interférant. Le modèle est étendu pour investiguer les effets de la détection coopérative sur l'interférence totale et la capacité de coupure, en considérant l'operateur logique OR et une détection coopérative de maximum de vraisemblance.
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Crass, Jonathan. "The Adaptive Optics Lucky Imager : combining adaptive optics and lucky imaging". Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/245653.
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One of the highest resolution astronomical images ever taken in the visible were obtained by combining the techniques of adaptive optics and lucky imaging. The Adaptive Optics Lucky Imager (AOLI), being developed at Cambridge as part of a European collaboration, combines these two techniques in a dedicated instrument for the first time. The instrument is designed initially for use on the 4.2m William Herschel Telescope (WHT) on the Canary Island of La Palma. This thesis describes the development of AOLI, in particular the adaptive optics system and a new type of wavefront sensor, the non-linear curvature wavefront sensor (nlCWFS), being used within the instrument. The development of the nlCWFS has been the focus of my work, bringing the technique from a theoretical concept to physical realisation at the WHT in September 2013. The non-linear curvature wavefront sensor is based on the technique employed in the conventional curvature wavefront sensor where two image planes are located equidistant either side of a pupil plane. Two pairs of images are employed in the nlCWFS providing increased sensitivity to both high- and low- order wavefront distortions. This sensitivity is the reason the nlCWFS was selected for use with AOLI as it will provide significant sky-coverage using natural guide stars alone, mitigating the need for laser guide stars. This thesis is structured into three main sections; the first introduces the non-linear curvature wavefront sensor, the relevant background and a discussion of simulations undertaken to investigate intrinsic effects. The iterative reconstruction algorithm required for wavefront reconstruction is also introduced. The second section discusses the practical implementation of the nlCWFS using two demonstration systems as the precursor to the optical design used at the WHT and includes details of subsequent design changes. The final section discusses data from both the WHT and a laboratory setup developed at Cambridge following the observing run. The long-term goal for AOLI is to undertake science observations on the 10.4m Gran Telescopio Canarias, the world's largest optical telescope. The combination of AO and lucky imaging, when used on this telescope, will provide resolutions a factor of two higher than ever before achieved at visible wavelengths. This offers the opportunity to probe the Cosmos in unprecedented detail and has the potential to significantly advance our understanding of the Universe.
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Kemppainen, A. (Anssi). "Adaptive methods for autonomous environmental modelling". Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526218519.
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Abstract In this thesis, we consider autonomous environmental modelling, where robotic sensing platforms are utilized in environmental surveying. In order to allow a wide range of different environments, our models must be flexible to the data with some a prior assumptions. Respectively, in order to guide action planning, we need to have a unified sensing quality metric that depends on the prediction quality of our models. Finally, in order to be able to adapt to the observed information, at each iteration of the action planning algorithm, we must be able to provide solutions that aim at minimum travelling time needed to reach a certain level of sensing quality. These are the main topics in this thesis. At the center of our approaches are stationary and non-stationary Gaussian processes based on the assumption that the observed phenomenon is due to the diffusion of white noise, where diffusion kernel anisotropy and scale may vary between locations. For these models, we propose adaptation of diffusion kernels based on a structure tensor approach. Proposed methods are demonstrated with experiments that show, assuming sensor noise is not dominating, our iterative approach is able to return diffusion kernel values close to correct ones. In order to quantify how precise our models are, we propose a mutual information based sensing quality criterion, and prove that the optimal design using our sensing quality provides the best prediction quality for the model. To incorporate localization uncertainty in modelling, we also propose an approach where a posterior model is marginalized over sensing path distribution. The benefit is that this approach implicitly favors actions that result in previously visited or otherwise well-defined areas, meanwhile, maximizing the information gain. Experiments support our claims that our proposed approaches are best when considering predictive distribution quality. In action planning, our approach is to use graph-based approximation algorithms to obtain a certain level of model quality in an efficient way. In order account for spatial dependency and active localization, we propose adaptation methods that map sensing quality to vertex prices in a graph. Experiments demonstrate the benefit of our adaptation methods compared to the action planning algorithms that do not consider these specific featuresTiivistelmä Tässä väitöskirjassa tarkastellaan autonomista ympäristön mallinnusta, missä ympäristön kartoitukseen hyödynnetään robottimittausalustoja. Erilaisia ympäristöjä varten, käytettävien mallien tulee olla joustavia datalle tietyillä a priori oletuksilla. Mittausalustojen ohjaus vaatii vastaavasti yhtenäisen, mallien ennustuslaadusta riippuvan, kartoituksen laatumetriikan. Mukautuakseen uuteen informaatioon, ohjausalgoritmin tulee lisäksi pyrkiä joka iteraatiolla minimoimaan tietyn kartoituksen laadun saavuttava kulkuaika. Nämä ovat tämän väitöskirjan pääaiheet. Tämän väitöskirjan keskiössä ovat sellaiset stationaariset ja ei-stationaariset Gaussin prosessit, jotka perustuvat oletukseen että havaittu ilmiö johtuu valkoisen kohinan diffuusiosta. Diffuusiokernelin anisotrooppisuudelle ja skaalalle sallitaan paikkariippuvaisuus. Tässä väitöskirjassa esitetään näiden mallien mukauttamiseen rakennetensoripohjaisia menetelmiä. Suoritetut kokeet osoittavat, että esitetyt iteratiiviset mukauttamismenetelmät tuottavat lähes oikeita diffuusiokernelien arvoja, olettaen, että sensorikohina ei dominoi mittauksia. Mallien ennustustarkkuuden määrittämiseen esitetään keskinäisinformaatioon perustuva kartoituksen laatumetriikka. Väitöskirjassa todistetaan, että optimaalinen ennustuslaatu saavutetaan käyttämällä esitettyä laatumetriikkaa. Väitöskirjassa esitetään lisäksi laatumetriikka, jossa posteriori malli on marginalisoitu kartoituspolkujen jakauman yli. Tämän avulla voidaan huomioida paikannusepävarmuuden vaikutukset mallinnuksessa. Tällöin etuna on se, että kyseinen laatumetriikka suosii implisiittisesti sellaisia mittausalustojen ohjauksia, jotka johtavat aeimmin kartoitetuille tai helposti ennustettaville alueille samalla maksimoiden informaatiohyödyn. Suoritetut kokeet tukevat väittämiä, että väitöskirjassa esitetyt menetelmät tuottavat parhaan ennustusjakauman laadun. Mittausalustojen ohjaus vaatii vastaavasti yhtenäisen, mallien ennustuslaadusta riippuvan, kartoituksen laatumetriikan. Väitöskirjassa esitetään mukautusmenetelmiä kartoituksen laadun kuvaukseksi graafin solmujen kustannuksiksi. Tämän avulla sallitaan sekä spatiaalinen riippuvuus että aktiivinen paikannus. Mittausalustojen ohjaus vaatii vastaavasti yhtenäisen, mallien ennustuslaadusta riippuvan, kartoituksen laatumetriikan
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Baranec, Christoph James. "Astronomical Adaptive Optics using Multiple Laser Guide Stars". Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/193876.
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Over the past several years, experiments in adaptive optics involving multiple natural and laser guide stars have been carried out at the 1.55 m Kuiper telescope and the 6.5 m MMT telescope. The astronomical imaging improvement anticipated from both ground-layer and tomographic adaptive optics has been calculated. Ground-layer adaptive optics will reduce the effects of atmospheric seeing, increasing the resolution and sensitivity of astronomical observations over wide fields. Tomographic adaptive optics will provide diffraction-limited imaging along a single line of sight, increasing the amount of sky coverage available to adaptive optics correction.A new facility class wavefront sensor has been deployed at the MMT which will support closed-loop adaptive optics correction using a constellation of five Rayleigh laser guide stars and the deformable F/15 secondary mirror. The adaptive optics control loop was closed for the first time around the focus signal from all five laser signals in July of 2007, demonstrating that the system is working properly. It is anticipated that the full high-order ground-layer adaptive optics loop, controlled by the laser signals in conjunction with a tip/tilt natural guide star, will be closed in September 2007, with the imaging performance delivered by the system optimized and evaluated.The work here is intended to be both its own productive scientific endeavor for the MMT, but also as a proof of concept for the advanced adaptive optics systems designed to support observing at the Large Binocular Telescope and future extremely large telescopes such as the Giant Magellan Telescope.
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Koch, Matthias. "Investigations on halobacterial transducers with respect to membrane potential sensing and adaptive methylation". Diss., [S.l.] : [s.n.], 2005. http://edoc.ub.uni-muenchen.de/archive/00004954.
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Beal, David Nelson 1973. "Design and use of in-process sensing and adaptive compensation ni flexible assembly". Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43325.
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Hart, Michael, Stuart M. Jefferies i Neil Murphy. "Daylight operation of a sodium laser guide star for adaptive optics wavefront sensing". SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2016. http://hdl.handle.net/10150/624361.
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We report contrast measurements of a sodium resonance guide star against the daylight sky when observed through a tuned magneto-optical filter (MOF). The guide star was created by projection of a laser beam at 589.16 nm into the mesospheric sodium layer and the observations were made with a collocated 1.5-m telescope. While MOFs are used with sodium light detecting and ranging systems during the day to improve the signalto- noise ratio of the measurements, they have not so far been employed with laser guide stars to drive adaptive optics (AO) systems to correct atmospherically induced image blur. We interpret our results in terms of the performance of AO systems for astronomy, with particular emphasis on thermal infrared observations at the next generation of extremely large telescopes now being built. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
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Lopez-Juarez, Ismael. "On-line learning for robotic assembly using artificial neural networks and contact force sensing". Thesis, Nottingham Trent University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310849.
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Powers, Linda S., Yiming Zhang, Kemeng Chen, Huiqing Pan, Wo-Tak Wu, Peter W. Hall, Jerrie V. Fairbanks, Radik Nasibulin i Janet M. Roveda. "Low power real-time data acquisition using compressive sensing". SPIE-INT SOC OPTICAL ENGINEERING, 2017. http://hdl.handle.net/10150/626011.
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New possibilit ies exist for the development of novel hardware/software platforms havin g fast data acquisition capability with low power requirements. One application is a high speed Adaptive Design for Information (ADI) system that combines the advantages of feature-based data compression, low power nanometer CMOS technology, and stream computing [1]. We have developed a compressive sensing (CS) algorithm which linearly reduces the data at the analog front end, an approach which uses analog designs and computations instead of smaller feature size transistors for higher speed and lower power. A level-crossing sampling approach replaces Nyquist sampling. With an in-memory design, the new compressive sensing based instrumentation performs digitization only when there is enough variation in the input and when the random selection matrix chooses this input.
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Schwarz, Christina. "Assembly and proving of a wave front sensing confocal Scanning Laser Ophthalmoscope". [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:16-opus-73802.
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Anderson, Jason Mitchell. "Non-Intrusive Sensing and Feedback Control of Serpentine Inlet Flow Distortion". Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/27120.
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A technique to infer circumferential total pressure distortion intensity found in serpentine inlet airflow was established using wall-pressure fluctuation measurements. This sensing technique was experimentally developed for aircraft with serpentine inlets in a symmetric, level flight condition. The turbulence carried by the secondary flow field that creates the non-uniform total pressure distribution at the compressor fan-face was discovered to be an excellent indicator of the distortion intensity. A basic understanding of the secondary flow field allowed for strategic sensor placement to provide a distortion estimate with a limited number of sensors. The microphone-based distortion estimator was validated through its strong correlation with experimentally determined circumferential total pressure distortion parameter intensities (DPCP).
This non-intrusive DPCP estimation technique was then used as a DPCP observer in a distortion feedback control system. Lockheed Martin developed the flow control technique used in this control system, which consisted of jet-type vortex generators that injected secondary flow to counter the natural secondary flow inherent to the serpentine inlet. A proportional-integral-derivative (PID) based control system was designed that achieved a requested 66% reduction in DPCP (from a DPCP of 0.023 down to 0.007) in less than 1 second. This control system was also tested for its ability to maintain a DPCP level of 0.007 during a quick ramp-down and ramp-up engine throttling sequence, which served as a measure of system robustness. The control system allowed only a maximum peak DPCP of 0.009 during the engine ramp-up. The successful demonstrations of this automated distortion control system showed great potential for applying this distortion sensing scheme along with Lockheed Martinâ s flow control technique to military aircraft with serpentine inlets.
A final objective of this research was to broaden the non-intrusive sensing capabilities in the serpentine inlet. It was desired to develop a sensing technique that could identify control efforts that optimized the overall inlet aerodynamic performance with regards to both circumferential distortion intensity DPCP and average pressure recovery PR. This research was conducted with a new serpentine inlet developed by Lockheed Martin having a lower length-to-diameter ratio and two flow control inputs. A cost function based on PR and DPCP was developed to predict the optimal flow control efforts at several Mach numbers. Two wall-mounted microphone signals were developed as non-intrusive inlet performance sensors in response to the two flow control inputs. These two microphone signals then replaced the PR and DPCP metrics in the original cost function, and the new non-intrusive-based cost function yielded extremely similar optimal control efforts.Ph. D.
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Stein, Andrew Neil. "Adaptive image segmentation and tracking : a Bayesian approach". Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/13397.
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Fuller, Ryan Michael. "Adaptive Noise Reduction Techniques for Airborne Acoustic Sensors". Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1355361066.
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Czarske, Jürgen, C. Leithold, Hannes Radner, Lars Büttner, Moritz Stürmer i U. Wallrabe. "Undisturbed interferometric sensing through a fluid interface by electrically-tunable lenses and micro mirrors". SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A34996.
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We have harnessed the power of various programmable photonics devices for an interferometric measurement technique. Distortion-free laser-based velocity measurements through a dynamic gas-liquid interface are enabled by a closed-loop optoelectronic system. We are employing electrically tunable lenses and micro mirrors to correct low-order wavefront distortions effectively. Our work represents a paradigm shift in interferometric velocity measurement techniques from using static to dynamic optical elements.
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Nastasi, Kevin Michael. "Autonomous and Responsive Surveillance Network Management for Adaptive Space Situational Awareness". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84931.
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As resident space object populations grow, and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space situational awareness using current human-in-the-loop methods. This dissertation develops several real-time adaptive approaches to autonomous sensor network management for tracking multiple maneuvering and non-maneuvering satellites with a diversely populated Space Object Surveillance and Identification network. The proposed methods integrate suboptimal Partially Observed Markov Decision Processes (POMDPs) with covariance inflation or multiple model adaptive estimation techniques to task sensors and maintain viable orbit estimates for all targets. The POMDPs developed in this dissertation use information-based and system-based metrics to determine the rewards and costs associated with tasking a specific sensor to track a particular satellite. Like in real-world situations, the population of target satellites vastly outnumbers the available set of sensors. Robust and adaptable tasking algorithms are needed in this scenario to determine how and when sensors should be tasked. The strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that multiple model adaptive estimation coupled with a multi-metric, suboptimal POMDP can effectively and efficiently task a diverse network of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for autonomous and adaptable sensor network command and control for real-world space situational awareness.Ph. D.
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Lloyd-Hart, Michael. "Novel techniques of wavefront sensing for adaptive optics with array telescopes using an artificial neural network". Diss., The University of Arizona, 1992. http://hdl.handle.net/10150/185749.
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Atmospheric turbulence causes severe degradation of the resolving and signal-to-noise properties of present optical telescopes. Diffraction-limited resolution can be recovered through the use of a deformable ('adaptive') optical element to correct the atmospheric wavefront error. An adaptive optics system operating in the near infrared (1.7 - 3.5 μm) has been developed for use at the Multiple Mirror Telescope (MMT), an array of six co-mounted 1.8 m telescopes, in which six flat mirrors are used to correct the wavefront tilt across each aperture, and the phase differences between apertures. This can reduce the error sufficiently to achieve a diffraction-limited image with a central peak of 0.06 arcseconds full width at half maximum at 2.2 μm wavelength. A number of algorithms are used to drive the adaptive mirror in a closed servo loop, including a trained artificial neural network which deduces the wavefront aberration from a pair of simultaneous in- and out-of-focus images of a star, taken at the combined focal plane of the telescope. Computer simulations have shown that the net is capable of deriving the wavefront for the full six-mirror aperture, and in practice, the net has been demonstrated in the lab to maintain two- and three-aperture diffraction-limited beam profiles in the presence of distorting effects. On the sky, with a real star, the net has successfully restored the diffraction limit for two adjacent MMT segments. High resolution images have been obtained of various objects with a wide-field camera looking in the field around the wavefront reference star. Work has also been carried out to characterise the wavefront aberration at the MMT, which confirms the Kolmogorov model of turbulence. Finally, a new algorithm is discussed which shows great promise for correction of phase errors in array telescopes.
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Pazand, Babak. "Location-free node scheduling schemes for energy efficient, fault tolerant and adaptive sensing in wireless sensor networks". University of Western Australia. School of Computer Science and Software Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0185.
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Node scheduling is one of the most effective techniques to maximize the lifetime of a wireless sensor network. It is the process of selecting a subset of nodes to monitor the sensor field on behalf of redundant nodes. At every round of the scheduling a small group of nodes are active while the rest of the sensor nodes are in sleep mode. In this thesis, we propose a novel node scheduling solution for wireless sensor networks. The main characteristic of our approach is its independence from location information as well as distance information. Moreover, it does not rely on unrealistic circular radio propagation models. In order to have a comprehensive solution, we have considered different relations between sensing range and transmission range. When these ranges are equal in addition to the case that transmission range is higher than sensing range, we devise a node scheduling scheme based on the concept of Minimum Dominating Set. Two heuristics are presented to determine a collection of minimum dominating sets of the graph of the wireless sensor network. At each round of the scheduling only one set is active. Minimum dominating sets are scheduled to be rotated periodically. Moreover, every set is synchronized prior to the end of its active period in order to minimize the effect of clock drift of sensor nodes. Two components are considered to address node failures during the on-duty period of minimum dominating sets. These are probing environment and adaptive sleeping. The former is responsible for probing the working nodes of the active set to detect any node failure. The latter adjusts the frequency of probing for minimizing the overhead of probing while preserving an adequate level of robustness for discovery of node failure. This framework is based on the PEAS protocol that has been developed by Fan Ye et al. [98, 99]. We propose a different node scheduling scheme with a three-tier architecture for the case that sensing range is higher than transmission range. The coverage tier includes a set of nodes to monitor the region of the interest. We propose a heuristic to determine a collection of d-dominating sets of the graph of the wireless sensor network. At every round of the scheduling one d-dominating set forms the coverage tier. Connectivity tier consists of sensor nodes that relay the data collected at the coverage tier back to the base station. Finally, the coverage management tier is responsible for managing different patterns of coverage such as cyclic or uniform coverage.
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Dhanasekaran, Deepananthan. "A Locally Adaptive Spatial Interpolation Technique for the Generation of High-Resolution DEMs". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306112037.
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Al-Juboori, Ahmed O. A. S. "Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods". Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16794.
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The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS).
Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements.
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Tan, Feng Ph D. Massachusetts Institute of Technology. "Bridging adaptive estimation and control with modern machine learning : a quorum sensing inspired algorithm for dynamic clustering". Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/78193.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 89-92).
Quorum sensing is a decentralized biological process, by which a community of bacterial cells with no global awareness can coordinate their functional behaviors based only on local decision and cell-medium interaction. This thesis draws inspiration from quorum sensing to study the data clustering problem, in both the time-invariant and the time-varying cases. Borrowing ideas from both adaptive estimation and control, and modern machine learning, we propose an algorithm to estimate an "influence radius" for each cell that represents a single data, which is similar to a kernel tuning process in classical machine learning. Then we utilize the knowledge of local connectivity and neighborhood to cluster data into multiple colonies simultaneously. The entire process consists of two steps: first, the algorithm spots sparsely distributed "core cells" and determines for each cell its influence radius; then, associated "influence molecules" are secreted from the core cells and diffuse into the whole environment. The density distribution in the environment eventually determines the colony associated with each cell. We integrate the two steps into a dynamic process, which gives the algorithm flexibility for problems with time-varying data, such as dynamic grouping of swarms of robots. Finally, we demonstrate the algorithm on several applications, including benchmarks dataset testing, alleles information matching, and dynamic system grouping and identication. We hope our algorithm can shed light on the idea that biological inspiration can help design computational algorithms, as it provides a natural bond bridging adaptive estimation and control with modern machine learning.
by Feng Tan.
S.M.
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Grimes, Todd S. "Adaptive Power Analog-to-Digital Interface for Digital Systems". Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1483366560887816.
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Gao, Ying. "A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring". FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/132.
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Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.
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Su, Haibin. "Derivation of Coastal Bathymetry and Stream Habitat Attributes Using Remote Sensing Images and Airborne LiDAR". University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313688135.
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Dang, Hieu. "Adaptive multiobjective memetic optimization: algorithms and applications". Journal of Cognitive Informatics and Natural Intelligence, 2012. http://hdl.handle.net/1993/30856.
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The thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results.
This thesis also presents two applications of these algorithms. First, an optimal image data hiding technique has been formulated as a multiobjective optimization problem with conflicting objectives. In particular, trade-off factors in designing an optimal image data hiding are investigated to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and the adaptive multiobjective memetic optimization algorithm (AMMOA) to solve this challenging problem. This novel image data hiding approach has been implemented for many different test natural images with remarkable robustness and transparency of the embedded logo watermark. We also introduce a perceptual measure based on the relative Rényi information spectrum to evaluate the quality of watermarked images.
The second application is the problem of joint spectrum sensing and power control optimization for a multichannel, multiple-user cognitive radio network. We investigated trade-off factors in designing efficient spectrum sensing techniques to maximize the throughput and minimize the interference. To maximize the throughput of secondary users and minimize the interference to primary users, we propose a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is used again in the form of AMMOA. This algorithm learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference to the cognitive radio network.February 2016
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Mateen, Mala. "Development and Verification of the non-linear Curvature Wavefront Sensor". Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/581277.
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Adaptive optics (AO) systems have become an essential part of ground-based telescopes and enable diffraction-limited imaging at near-IR and mid-IR wavelengths. For several key science applications the required wavefront quality is higher than what current systems can deliver. For instance obtaining high quality diffraction-limited images at visible wavelengths requires residual wavefront errors to be well below 100 nm RMS. High contrast imaging of exoplanets and disks around nearby stars requires high accuracy control of low-order modes that dominate atmospheric turbulence and scatter light at small angles where exoplanets are likely to be found. Imaging planets using a high contrast corona graphic camera, as is the case for the Spectro-Polarimetric High-contrast Exoplanet Research (SPHERE) on the Very Large Telescope (VLT), and the Gemini Planet Imager (GPI), requires even greater wavefront control accuracy. My dissertation develops a highly sensitive non-linear Curvature Wavefront Sensor (nlCWFS) that can deliver diffraction-limited (λ/D) images, in the visible, by approaching the theoretical sensitivity limit imposed by fundamental physics. The nlCWFS is derived from the successful curvature wavefront sensing concept but uses a non-linear reconstructor in order to maintain sensitivity to low spatial frequencies. The nlCWFS sensitivity makes it optimal for extreme AO and visible AO systems because it utilizes the full spatial coherence of the pupil plane as opposed to conventional sensors such as the Shack-Hartmann Wavefront Sensor (SHWFS) which operate at the atmospheric seeing limit (λ/r₀). The difference is equivalent to a gain of (D/r₀)² in sensitivity, for the lowest order mode, which translates to the nlCWFS requiring that many fewer photons. When background limited the nlCWFS sensitivity scales as D⁴, a combination of D² gain due to the diffraction limit and D² gain due to telescope's collecting power. Whereas conventional wavefront sensors only benefit from the D² gain due to the telescope's collecting power. For a 6.5 m telescope, at 0.5 μm, and seeing of 0.5", the nlCWFS can deliver for low order modes the same wavefront measurement accuracy as the SHWFS with 1000 times fewer photons. This is especially significant for upcoming extremely large telescopes such as the Giant Magellan Telescope (GMT) which has a 25.4 m aperture, the Thirty Meter Telescope (TMT) and the European Extremely Large Telescope (E-ELT) which has a 39 m aperture.
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Engholm, Marcus. "Ultrasonic Arrays for Sensing and Beamforming of Lamb Waves". Doctoral thesis, Uppsala universitet, Signaler och System, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122189.
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Non-destructive testing (NDT) techniques are critical to ensure integrity and safety of engineered structures. Structural health monitoring (SHM) is considered as the next step in the field enabling continuous monitoring of structures. The first part of the thesis concerns NDT and SHM using guided waves in plates, or Lamb waves, to perform imaging of plate structures. The imaging is performed using a fixed active array setup covering a larger area of a plate. Current methods are based on conventional beamforming techniques that do not efficiently exploit the available data from the small arrays used for the purpose. In this thesis an adaptive signal processing approach based on the minimum variance distortionless response (MVDR) method is proposed to mitigate issues related to guided waves, such as dispersion and the presence of multiple propagating modes. Other benefits of the method include a significant increase in resolution. Simulation and experimental results show that the method outperforms current standard processing techniques. The second part of the thesis addresses transducer design issues for resonant ultrasound inspections. Resonant ultrasound methods utilize the shape and frequency of the object's natural modes of vibration to detect anomalies. The method considered in the thesis uses transducers that are acoustically coupled to the inspected structures. Changes in the transducer's electrical impedance are used to detect defects. The sensitivity that can be expected from such a setup is shown to highly depend on the transducer resonance frequency, as well as the working frequency of the instrument. Through simulations and a theoretical argumentation, optimal conditions to achieve high sensitivity are given.