Dissertations / Theses on the topic 'Adaptive signal processing – Mathematics'
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Fabrizio, Giuseppe Aureliano. "Space-time characterisation and adaptive processing of ionospherically-propagated HF signals /." Title page, table of contents and abstract only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phf129.pdf.
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Yao, Ning. "Iterative algorithms for channel estimation and equalization /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202005%20YAO.
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Indra, Isara. "Very low bit rate video coding using adaptive nonuniform sampling and matching pursuit." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/15779.
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Wang, Yan Bo. "Adaptive decomposition of signals into mono-components." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2489954.
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Huang, Walter. "Implementation of adaptive digital FIR and reprogrammable mixed-signal filters using distributed arithmetic." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31653.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.Committee Chair: Anderson, David V.; Committee Member: Ferri, Bonnie H.; Committee Member: Hasler, Paul E.; Committee Member: Kang, Sung Ha; Committee Member: McClellan, James H.; Committee Member: Wolf, Wayne H. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Sadeghi, Parastoo School of Electrical Engineering And Telecommunications UNSW. "Modelling, information capacity, and estimation of time-varying channels in mobile communication systems." Awarded by:University of New South Wales. School of Electrical Engineering And Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/32310.
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In the first part of this thesis, the information capacity of time-varying fading channels is analysed using finite-state Markov channel (FSMC) models. Both fading channel amplitude and fading channel phase are modelled as finite-state Markov processes. The effect of the number of fading channel gain partitions on the capacity is studied (from 2 to 128 partitions). It is observed that the FSMC capacity is saturated when the number of fading channel gain partitions is larger than 4 to 8 times the number of channel input levels. The rapid FSMC capacity saturation with a small number of fading channel gain partitions can be used for the design of computationally simple receivers, with a negligible loss in the capacity. Furthermore, the effect of fading channel memory order on the capacity is studied (from first- to fourth-order). It is observed that low-order FSMC models can provide higher capacity estimates for fading channels than high-order FSMC models, especially when channel states are poorly observable in the presence of channel noise. To explain the effect of memory order on the FSMC capacity, the capacities of high-order and low-order FSMC models are analytically compared. It is shown that the capacity difference is caused by two factors: 1) the channel entropy difference, and 2) the channel observability difference between the high-order and low-order FSMC models. Due to the existence of the second factor, the capacity of high-order FSMC models can be lower than the capacity of low-order FSMC models. Two sufficient conditions are proven to predict when the low-order FSMC capacity is higher or lower than the high-order FSMC capacity. In the second part of this thesis, a new implicit (blind) channel estimation method in time- varying fading channels is proposed. The information source emits bits ???0??? and ???1??? with unequal probabilities. The unbalanced source distribution is used as a priori known signal structure at the receiver for channel estimation. Compared to pilot-symbol-assisted channel estimation, the proposed channel estimation technique can achieve a superior receiver bit error rate performance, especially at low signal to noise ratio conditions.
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Jalali, Sammuel. "Wireless Channel Equalization in Digital Communication Systems." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/cgu_etd/42.
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Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential.
The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation.
The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4.
For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple "cooperative" algorithms for the cases of two and three cooperative algorithms. The "select absolutely larger equalized signal" and "majority vote" methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research.
Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases.
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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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Viswanathan, Kartik. "Représentation reconstruction adaptative des hologrammes numériques." Thesis, Rennes, INSA, 2016. http://www.theses.fr/2016ISAR0012/document.
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On constate une forte augmentation de l’intérêt porté sur l’utilisation des technologies vidéo 3D pour des besoins commerciaux, notamment par l’application de l’holographie, pour fournir des images réalistes, qui semblent vivantes. Surtout, pour sa capacité à reconstruire tous les parallaxes nécessaires, afin de permettre de réaliser une vision véritablement immersive qui peut être observée par quiconque (humains, machine ou animal). Malheureusement la grande quantité d'information contenue dans un hologramme le rend inapte à être transmis en temps réel sur les réseaux existants. Cette thèse présente des techniques afin de réduire efficacement la taille de l'hologramme par l'élagage de portions de l'hologramme en fonction de la position de l'observateur. Un grand nombre d'informations contenues dans l'hologramme n'est pas utilisé si le nombre d'observateurs d'une scène immersive est limité. Sous cette hypothèse, éléments de l'hologramme peuvent être décomposés pour que seules les parties requises sensibles au phénomène de diffraction vers un point d'observation particulier soient conservés. Les reconstructions de ces hologrammes élagués peuvent être propagées numériquement ou optiquement. On utilise la transformation en ondelettes pour capter les informations de fréquences localisées depuis l'hologramme. La sélection des ondelettes est basée sur des capacités de localisation en espace et en fréquence. Par exemple, les ondelettes de Gabor et Morlet possèdent une bonne localisation dans l'espace et la fréquence. Ce sont des bons candidats pour la reconstruction des hologrammes suivant la position de l'observateur. Pour cette raison les ondelettes de Shannon sont également utilisées. De plus l'application en fonction du domaine de fréquence des ondelettes de Shannon est présentée pour fournir des calculs rapides de l'élagage en temps réel et de la reconstructionWith the increased interest in 3D video technologies for commercial purposes, there is renewed interest in holography for providing true, life-like images. Mainly for the hologram's capability to reconstruct all the parallaxes that are needed for a truly immersive views that can be observed by anyone (human, machine or animal). But the large amount of information that is contained in a hologram make it quite unsuitable to be transmitted over existing networks in real-time. In this thesis we present techniques to effectively reduce the size of the hologram by pruning portions of the hologram based on the position of the observer. A large amount of information contained in the hologram is not used if the number of observers of an immersive scene is limited. Under this assumption, parts of the hologram can be pruned out and only the requisite parts that can cause diffraction at an observer point can be retained. For reconstructions these pruned holograms can be propagated numerically or optically. Wavelet transforms are employed to capture the localized frequency information from the hologram. The selection of the wavelets is based on the localization capabilities in the space and frequency domains. Gabor and Morlet wavelets possess good localization in space and frequency and form good candidates for the view based reconstruction system. Shannon wavelets are also employed for this cause and the frequency domain based application using the Shannon wavelet is shown to provide fast calculations for real-time pruning and reconstruction
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Testoni, Nicola <1980>. "Adaptive multiscale biological signal processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/1122/.
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Biological processes are very complex mechanisms, most
of them being accompanied by or manifested as signals
that reflect their essential characteristics and qualities. The
development of diagnostic techniques based on signal and
image acquisition from the human body is commonly retained
as one of the propelling factors in the advancements
in medicine and biosciences recorded in the recent past.
It is a fact that the instruments used for biological signal
and image recording, like any other acquisition system,
are affected by non-idealities which, by different degrees,
negatively impact on the accuracy of the recording. This
work discusses how it is possible to attenuate, and ideally
to remove, these effects, with a particular attention toward
ultrasound imaging and extracellular recordings.
Original algorithms developed during the Ph.D. research
activity will be examined and compared to ones in literature
tackling the same problems; results will be drawn on the
base of comparative tests on both synthetic and in-vivo
acquisitions, evaluating standard metrics in the respective
field of application. All the developed algorithms share an
adaptive approach to signal analysis, meaning that their
behavior is not dependent only on designer choices, but
driven by input signal characteristics too.
Performance comparisons following the state of the art
concerning image quality assessment, contrast gain estimation
and resolution gain quantification as well as visual
inspection highlighted very good results featured by the
proposed ultrasound image deconvolution and restoring
algorithms: axial resolution up to 5 times better than algorithms
in literature are possible. Concerning extracellular
recordings, the results of the proposed denoising technique
compared to other signal processing algorithms pointed
out an improvement of the state of the art of almost 4 dB.
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Jahanchahi, Cyrus. "Quaternion valued adaptive signal processing." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24165.
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Recent developments in sensor technology, human centered computing and robotics have brought to light new classes of multidimensional data which are naturally represented as three- or four-dimensional vector-valued processes. Such signals are readily modeled as real vectors in R3 and R4, however, it has become apparent that there are advantages in processing such data in division algebras - the quaternion domain. The progress in the statistics of quaternion variable, particularly augmented statistics and widely linear modeling, has opened up a new front of research in vector sensor modeling, however, there are several key problems that need to be addressed in order to exploit the full power of quaternions in statistical signal processing. The principal problem lies in the lack of a mathematical framework, such as the CR-calculus in the complex domain, for the differentiation of non-holomorphic functions. Since most functions (including typical cost functions) in the quaternion domain are non-holomorphic, as defined by the Cauchy-Riemann-Fueter (CRF) condition, this presents a severe obstacle to solving optimisation problems and developing adaptive filtering algorithms in the quaternion domain. To this end, we develop the HR-calculus, an extension of the CR-calculus, allowing the differentiation of non-holomorphic functions. This is followed by the introduction of the I-gradient, enabling for generic extensions of complex valued algorithms to be derived. Using this unified framework we introduce the quaternion least mean square (QLMS), quaternion recursive least squares (QRLS), quaternion affine projection algorithm (QAPA) and quaternion Kalman filter. These estimators are made optimal for the processing of noncircular data, by proposing widely linear extensions of their standard versions. Convergence and steady state properties of these adaptive estimators are analysed and validated experimentally via simulations on both synthetic and real world signals.
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Figueroa, Toro Miguel E. "Adaptive signal processing and correlational learning in mixed-signal VLSI /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6856.
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Wyrsch, Sigisbert. "Adaptive subband signal processing for hearing instruments /." Zürich, 2000. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=13577.
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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals." Doctoral thesis, Umeå universitet, Strålningsvetenskaper, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.
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Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals. An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study. The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods. The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects. In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.
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Hermand, Jean-Pierre. "Environmentally-Adaptive Signal Processing in Ocean Acoustics." Doctoral thesis, Universite Libre de Bruxelles, 1993. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/212734.
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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals /." Umeå : Department of Radiation Sciences, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.
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Pazaitis, Dimitrios I. "Performance improvement in adaptive signal processing algorithms." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368771.
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Yaminysharif, Mohammad. "Accelerated gradient techniques and adaptive signal processing." Thesis, University of Strathclyde, 1987. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21496.
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The main objective of this thesis is to demonstrate the application of the accelerated gradient techniques to various fields of adaptive signal processing. A variety of adaptive algorithms based on the accelerated gradient techniques are developed and analysed in terms of the convergence speed, computational complexity and numerical stability. Extensive simulation results are presented to demonstrate the performance of the proposed algorithms when applied to the fields of adaptive noise cancelling, broad band adaptive array processing and narrow band adaptive spectral estimation. These results are very encouraging in terms of convergence speed and numerical stability of the developed algorithms. The proposed algorithms appear to be attractive alternatives to the conventional recursive least squares algorithms. In addition, the thesis includes a review chapter in which the conventional approaches (ranging from the least mean squares algorithm to the computationally demanding recursive least squares algorithm) to three types of minimization problems (namely unconstrained, linearly constrained and quadratically constrained) are discussed.
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Esparcia, Alcázar Anna Isabel. "Genetic programming for adaptive digital signal processing." Thesis, University of Glasgow, 1998. http://theses.gla.ac.uk/4780/.
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Kanagasabapathy, Shri. "Distributed adaptive signal processing for frequency estimation." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/49783.
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It is widely recognised that future smart grids will heavily rely upon intelligent communication and signal processing as enabling technologies for their operation. Traditional tools for power system analysis, which have been built from a circuit theory perspective, are a good match for balanced system conditions. However, the unprecedented changes that are imposed by smart grid requirements, are pushing the limits of these old paradigms. To this end, we provide new signal processing perspectives to address some fundamental operations in power systems such as frequency estimation, regulation and fault detection. Firstly, motivated by our finding that any excursion from nominal power system conditions results in a degree of non-circularity in the measured variables, we cast the frequency estimation problem into a distributed estimation framework for noncircular complex random variables. Next, we derive the required next generation widely linear, frequency estimators which incorporate the so-called augmented data statistics and cater for the noncircularity and a widely linear nature of system functions. Uniquely, we also show that by virtue of augmented complex statistics, it is possible to treat frequency tracking and fault detection in a unified way. To address the ever shortening time-scales in future frequency regulation tasks, the developed distributed widely linear frequency estimators are equipped with the ability to compensate for the fewer available temporal voltage data by exploiting spatial diversity in wide area measurements. This contribution is further supported by new physically meaningful theoretical results on the statistical behavior of distributed adaptive filters. Our approach avoids the current restrictive assumptions routinely employed to simplify the analysis by making use of the collaborative learning strategies of distributed agents. The efficacy of the proposed distributed frequency estimators over standard strictly linear and stand-alone algorithms is illustrated in case studies over synthetic and real-world three-phase measurements. An overarching theme in this thesis is the elucidation of underlying commonalities between different methodologies employed in classical power engineering and signal processing. By revisiting fundamental power system ideas within the framework of augmented complex statistics, we provide a physically meaningful signal processing perspective of three-phase transforms and reveal their intimate connections with spatial discrete Fourier transform (DFT), optimal dimensionality reduction and frequency demodulation techniques. Moreover, under the widely linear framework, we also show that the two most widely used frequency estimators in the power grid are in fact special cases of frequency demodulation techniques. Finally, revisiting classic estimation problems in power engineering through the lens of non-circular complex estimation has made it possible to develop a new self-stabilising adaptive three-phase transformation which enables algorithms designed for balanced operating conditions to be straightforwardly implemented in a variety of real-world unbalanced operating conditions. This thesis therefore aims to help bridge the gap between signal processing and power communities by providing power system designers with advanced estimation algorithms and modern physically meaningful interpretations of key power engineering paradigms in order to match the dynamic and decentralised nature of the smart grid.
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Picciolo, Michael L. "Robust Adaptive Signal Processors." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/26993.
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Standard open loop linear adaptive signal processing algorithms derived from the least squares minimization criterion require estimates of the N-dimensional input interference and noise statistics. Often, estimated statistics are biased by contaminant data (such as outliers and non-stationary data) that do not fit the dominant distribution, which is often modeled as Gaussian. In particular, convergence of sample covariance matrices used in block processed adaptive algorithms, such as the Sample Matrix Inversion (SMI) algorithm, are known to be affected significantly by outliers, causing undue bias in subsequent adaptive weight vectors. The convergence measure of effectiveness (MOE) of the benchmark SMI algorithm is known to be relatively fast (order K = 2N training samples) and independent of the (effective) rank of the external interference covariance matrix, making it a useful method in practice for non-contaminated data environments. Novel robust adaptive algorithms are introduced here that perform superior to SMI algorithms in contaminated data environments while some retain its valuable convergence independence feature. Convergence performance is shown to be commensurate with SMI in non-contaminated environments as well. The robust algorithms are based on the Gram Schmidt Cascaded Canceller (GSCC) structure where novel building block algorithms are derived for it and analyzed using the theory of Robust Statistics. Coined M â cancellers after M â estimates of Huber, these novel cascaded cancellers combine robustness and statistical estimation efficiency in order to provide good adaptive performance in both contaminated and non-contaminated data environments. Additionally, a hybrid processor is derived by combining the Multistage Wiener Filter (MWF) and Median Cascaded Canceller (MCC) algorithms. Both simulated data and measured Space-Time Adaptive Processing (STAP) airborne radar data are used to show performance enhancements. The STAP application area is described in detail in order to further motivate research into robust adaptive processing.Ph. D.
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Yang, Ho. "Partially adaptive space-time processing." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13028.
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Seliktar, Yaron. "Space-time adaptive monopulse processing." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13075.
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Famorzadeh, Shahram. "BEEHIVE : an adaptive, distributed, embedded signal processing environment." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/14803.
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Monta, Peter. "Signal processing for high-definition television." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36997.
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Dugger, Jeffery Don. "Adaptive Analog VLSI Signal Processing and Neural Networks." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5294.
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Research presented in this thesis provides
a substantial leap from the study of interesting
device physics to fully adaptive analog networks
and lays a solid foundation for future development
of large-scale, compact, low-power adaptive parallel
analog computation systems.
The investigation described here started with
observation of this potential learning capability
and led to the first derivation and characterization of
the floating-gate pFET correlation learning rule.
Starting with two synapses sharing the same error signal,
we progressed from phase correlation experiments
through correlation experiments involving harmonically related sinusoids,
culminating in learning the Fourier series coefficients
of a square wave cite{kn:Dugger2000}.
Extending these earlier two-input node experiments to the general case
of correlated inputs required dealing with
weight decay naturally exhibited by the learning rule.
We introduced a source-follower floating-gate synapse
as an improvement over our earlier source-degenerated floating-gate synapse
in terms of relative weight decay cite{kn:Dugger2004}.
A larger network of source-follower floating-gate synapses was fabricated
and an FPGA-controlled testboard was designed and built.
This more sophisticated system provides an excellent
framework for exploring applications to multi-input, multi-node
adaptive filtering applications.
Adaptive channel equalization provided
a practical test-case illustrating the use
of these adaptive systems in solving real-world problems.
The same system could easily be applied to noise and echo cancellation
in communication systems and system identification tasks in
optimal control problems.
We envision the commercialization of these adaptive analog VLSI
systems as practical products within a couple of years.
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Lynch, Michael Richard. "Adaptive techniques in signal processing and connectionist models." Thesis, University of Cambridge, 1990. https://www.repository.cam.ac.uk/handle/1810/244884.
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This thesis covers the development of a series of new methods and the application of adaptive filter theory which are combined to produce a generalised adaptive filter system which may be used to perform such tasks as pattern recognition. Firstly, the relevant background adaptive filter theory is discussed in Chapter 1 and methods and results which are important to the rest of the thesis are derived or referenced. Chapter 2 of this thesis covers the development of a new adaptive algorithm which is designed to give faster convergence than the LMS algorithm but unlike the Recursive Least Squares family of algorithms it does not require storage of a matrix with n2 elements, where n is the number of filter taps. In Chapter 3 a new extension of the LMS adaptive notch filter is derived and applied which gives an adaptive notch filter the ability to lock and track signals of varying pitch without sacrificing notch depth. This application of the LMS filter is of interest as it demonstrates a time varying filter solution to a stationary problem. The LMS filter is next extended to the multidimensional case which allows the application of LMS filters to image processing. The multidimensional filter is then applied to the problem of image registration and this new application of the LMS filter is shown to have significant advantages over current image registration methods. A consideration of the multidimensional LMS filter as a template matcher and pattern recogniser is given. In Chapter 5 a brief review of statistical pattern recognition is given, and in Chapter 6 a review of relevant connectionist models. In Chapter 7 the generalised adaptive filter is derived. This is an adaptive filter with the ability to model non-linear input-output relationships. The Volterra functional analysis of non-linear systems is given and this is combined with adaptive filter methods to give a generalised non-linear adaptive digital filter. This filter is then considered as a linear adaptive filter operating in a non-linearly extended vector space. This new filter is shown to have desirable properties as a pattern recognition system. The performance and properties of the new filter is compared with current connectionist models and results demonstrated in Chapter 8. In Chapter 9 further mathematical analysis of the networks leads to suggested methods to greatly reduce network complexity for a given problem by choosing suitable pattern classification indices and allowing it to define its own internal structure. In Chapter 10 robustness of the network to imperfections in its implementation is considered. Chapter 11 finishes the thesis with some conclusions and suggestions for future work.
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Price, Emma J. "The use of residuals for adaptive signal processing." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433334.
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Maji, Suman Kumar. "Multiscale methods in signal processing for adaptive optics." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00909085.
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In this thesis, we introduce a new approach to wavefront phase reconstruction in Adaptive Optics (AO) from the low-resolution gradient measurements provided by a wavefront sensor, using a non-linear approach derived from the Microcanonical Multiscale Formalism (MMF). MMF comes from established concepts in statistical physics, it is naturally suited to the study of multiscale properties of complex natural signals, mainly due to the precise numerical estimate of geometrically localized critical exponents, called the singularity exponents. These exponents quantify the degree of predictability, locally, at each point of the signal domain, and they provide information on the dynamics of the associated system. We show that multiresolution analysis carried out on the singularity exponents of a high-resolution turbulent phase (obtained by model or from data) allows a propagation along the scales of the gradients in low-resolution (obtained from the wavefront sensor), to a higher resolution. We compare our results with those obtained by linear approaches, which allows us to offer an innovative approach to wavefront phase reconstruction in Adaptive Optics.
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Chan, M. K. "Adaptive signal processing algorithms for non-Gaussian signals." Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269023.
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Javidi, Soroush. "Adaptive signal processing algorithms for noncircular complex data." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6328.
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The complex domain provides a natural processing framework for a large class of signals encountered in communications, radar, biomedical engineering and renewable energy. Statistical signal processing in C has traditionally been viewed as a straightforward extension of the corresponding algorithms in the real domain R, however, recent developments in augmented complex statistics show that, in general, this leads to under-modelling. This direct treatment of complex-valued signals has led to advances in so called widely linear modelling and the introduction of a generalised framework for the differentiability of both analytic and non-analytic complex and quaternion functions. In this thesis, supervised and blind complex adaptive algorithms capable of processing the generality of complex and quaternion signals (both circular and noncircular) in both noise-free and noisy environments are developed; their usefulness in real-world applications is demonstrated through case studies. The focus of this thesis is on the use of augmented statistics and widely linear modelling. The standard complex least mean square (CLMS) algorithm is extended to perform optimally for the generality of complex-valued signals, and is shown to outperform the CLMS algorithm. Next, extraction of latent complex-valued signals from large mixtures is addressed. This is achieved by developing several classes of complex blind source extraction algorithms based on fundamental signal properties such as smoothness, predictability and degree of Gaussianity, with the analysis of the existence and uniqueness of the solutions also provided. These algorithms are shown to facilitate real-time applications, such as those in brain computer interfacing (BCI). Due to their modified cost functions and the widely linear mixing model, this class of algorithms perform well in both noise-free and noisy environments. Next, based on a widely linear quaternion model, the FastICA algorithm is extended to the quaternion domain to provide separation of the generality of quaternion signals. The enhanced performances of the widely linear algorithms are illustrated in renewable energy and biomedical applications, in particular, for the prediction of wind profiles and extraction of artifacts from EEG recordings.
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Cetin, Ediz. "Unsupervised adaptive signal processing techniques for wireless receivers." Thesis, University of Westminster, 2002. https://westminsterresearch.westminster.ac.uk/item/93q55/unsupervised-adaptive-signal-processing-techniques-for-wireless-receivers.
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Minelly, Shona. "Signal processing of His Purkinje System electrocardiograms." Thesis, University of Kent, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267381.
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Owens, Peter. "Advanced signal processing of high resolution electrocardiograms." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361399.
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Baykal, Buyurman. "Underdetermined recursive least-squares adaptive filtering." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7790.
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Hayward, Stephen David. "Adaptive sensor array processing in non-stationary signal environments." Thesis, University of Birmingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368454.
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Hong, John Hyunchul Psaltis Demetri. "Optical computing for adaptive signal processing and associative memories /." Diss., Pasadena, Calif. : California Institute of Technology, 1987. http://resolver.caltech.edu/CaltechETD:etd-06142006-094757.
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Chen, Teyan. "Novel adaptive signal processing techniques for underwater acoustic communications." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1925/.
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The underwater acoustic channel is characterized by time-varying multipath propagation with large delay spreads of up to hundreds of milliseconds, which introduces severe intersymbol interference (ISI) in digital communication system. Many of the existing channel estimation and equalization techniques used in radio frequency wireless communication systems might be practically inapplicable to underwater acoustic communication due to their high computational complexity. The recursive least squares (RLS)-dichotomous coordinate descent (DCD) algorithm has been recently proposed and shown to perform closely to the classical RLS algorithm while having a significantly lower complexity. It is therefore a highly promising channel estimation algorithm for underwater acoustic communications. However, predicting the convergence performance of the RLS-DCD algorithm is an open issue. Known approaches are found not applicable, as in the RLS-DCD algorithm, the normal equations are not exactly solved at every time instant and the sign function is involved at every update of the filter weights. In this thesis, we introduce an approach for convergence analysis of the RLS-DCD algorithm based on computations with only deterministic correlation quantities. Equalization is a well known method for combatting the ISI in communication channels. Coefficients of an adaptive equalizer can be computed without explicit channel estimation using the channel output and known pilot signal. Channel-estimate (CE) based equalizers which re-compute equalizer coefficients for every update of the channel estimate, can outperform equalizers with the direct adaptation. However, the computational complexity of CE based equalizers for channels with large delay spread, such as the underwater acoustic channel, is an open issue. In this thesis, we propose a low-complexity CE based adaptive linear equalizer, which exploits DCD iterations for computation of equalizer coefficients. The proposed technique has as low complexity as O(Nu(K+M)) operations per sample, where K and M are the equalizer and channel estimator length, respectively, and Nu is the number of iterations such that Nu << K and Nu << M. Moreover, when using the RLS-DCD algorithm for channel estimation, the computation of equalizer coefficients is multiplication-free and division-free, which makes the equalizer attractive for hardware design. Simulation results show that the proposed adaptive equalizer performs close to the minimum mean-square-error (MMSE) equalizer with perfect knowledge of the channel. Decision feedback equalizers (DFEs) can outperform LEs, provided that the effect of decision errors on performance is negligible. However, the complexity of existing CE based DFEs normally grows squarely with the feedforward filter (FFF) length K. In multipath channels with large delay spread and long precursor part, such as in underwater acoustic channels, the FFF length K needs to be large enough to equalize the precursor part, and it is usual that K > M. Reducing the complexity of CE based DFEs in such scenarios is still an open issue. In this thesis, we derive two low complexity approaches for computing CE based DFE coefficients. The proposed DFEs operate together with partial-update channel estimators, such as the RLS-DCD channel estimator, and exploit complex-valued DCD iterations to efficiently compute the DFE coefficients. In the first approach, the proposed DFE has a complexity of O(Nu l log 2l) real multiplications per sample, where l is the equalizer delay and Nu is the number of iterations such that Nu << l. In the second proposed approach, DFE has a complexity as low as O(Nu K)+O(Nu B) + O(Nu M) operations per sample, where B is the feedback filter (FBF) length and Nu << M. Moreover, when the channel estimator also exploits the DCD iterations, e.g. such as in the RLS-DCD adaptive filter, the second approach is multiplication-free and division-free, which makes the equalizer attractive for hardware implementation. Simulation results show that the proposed DFEs perform close to the RLS CE based DFE, where the CE is obtained using the classical RLS adaptive filter and the equalizer coefficients are computed according to the MMSE criterion. Localization is an important problem for many underwater communication systems, such as underwater sensor networks. Due to the characteristics of the underwater acoustic channel, localization of underwater acoustic sources is challenging and needs to be accurate and computationally efficient. The matched-phase coherent broadband matched-field (MF) processor has been previously proposed and shown to outperform other advanced broadband MF processors for underwater acoustic source localization. It has been previously proposed to search the matched phases using the simulated annealing, which is well known for its ability for solving global optimization problems while having high computational complexity. This prevents simultaneous processing of many frequencies, and thus, limits the processor performance. In this thesis, we introduce a novel iterative technique based on coordinate descent optimization, the phase descent search (PDS), for searching the matched phases. We show that the PDS algorithm obtains matched phases similar to that obtained by the simulated annealing, and has significantly lower complexity. Therefore, it enables to search phases for a large number of frequencies and significantly improves the processor performance. The proposed processor is applied to experimental data for locating a moving acoustic source and shown to provide accurate localization of the source well matched to GPS measurements.
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Ranganathan, Raghuram. "Novel complex adaptive signal processing techniques employing optimally derived time-varying convergence factors with applications in digital signal processing and wireless communications." Orlando, Fla. : University of Central Florida, 2008. http://purl.fcla.edu/fcla/etd/CFE0002431.
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Schoenig, Gregory Neumann. "Contributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radar." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26972.
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Classical adaptive signal processors typically utilize assumptions in their derivation. The
presence of adequate Gaussian and independent and identically distributed (i.i.d.) input
data are central among such assumptions. However, classical processors have a tendency
to suffer a degradation in performance when assumptions like these are violated. Worse
yet, such degradation is not guaranteed to be proportional to the level of deviation from
the assumptions. This dissertation proposes new signal processing algorithms based on
aspects of modern robustness theory, including methods to enable adaptivity of presently
non-adaptive robust approaches. The contributions presented are the result of research
performed jointly in two disciplines, namely robustness theory and adaptive signal process-
ing. This joint consideration of robustness and adaptivity enables improved performance in
assumption-violating scenarios â scenarios in which classical adaptive signal processors fail.
Three contributions are central to this dissertation. First, a new adaptive diagnostic tool for
high-dimension data is developed and shown robust in problematic contamination. Second,
a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally,
a new suppression-based robust estimator is developed for use with complex-valued adaptive
signal processing data. To exercise the proposals and compare their performance to state-
of-the art methods, data sets commonly used in statistics as well as Space-Time Adaptive
Processing (STAP) radar data, both real and simulated, are processed, and performance is
subsequently computed and displayed. The new algorithms are shown to outperform their
state-of-the-art counterparts from both a signal-to-interference plus noise ratio (SINR) conver-
gence rate and target detection perspective.Ph. D.
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Spalding, Scott A. Jr. "Adaptive OFDM Radar Signal Design." Miami University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=miami1335728143.
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Howe, G. S. "A real-time adaptive beamformer for underwater telemetry." Thesis, University of Newcastle Upon Tyne, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307825.
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Lampl, Tanja. "Implementation of adaptive filtering algorithms for noise cancellation." Thesis, Högskolan i Gävle, Avdelningen för elektroteknik, matematik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-33277.
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This paper deals with the implementation and performance evaluation of adaptive filtering algorithms for noise cancellation without reference signal. Noise cancellation is a technique of estimating a desired signal from a noise-corrupted observation. If the signal and noise characteristics are unknown or change continuously over time, the need of adaptive filter arises. In contrast to the conventional digital filter design techniques, adaptive filters do not have constant filter parameters, they have the capability to continuously adjust their coefficients to their operating environment. To design an adaptive filter, that produces an optimum estimate of the desired signal from the noisy environment, different adaptive filtering algorithms are implemented and compared to each other. The Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm are investigated. Three performance criteria are used in the study of these algorithms: the rate of convergence, the error performance and the signal-to-noise ratio SNR. The implementation results show that the adaptive noise cancellation application benefits more from the use of the NLMS algorithm instead of the LMS or RLS algorithm.
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Perumalla, Calvin A. "Machine Learning and Adaptive Signal Processing Methods for Electrocardiography Applications." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6926.
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This dissertation is directed towards improving the state of art cardiac monitoring methods and automatic diagnosis of cardiac anomalies through modern engineering approaches such as adaptive signal processing, and machine learning methods. The dissertation will describe the invention and associated methods of a cardiac rhythm monitor dubbed the Integrated Vectorcardiogram (iVCG). In addition, novel machine learning approaches are discussed to improve diagnoses and prediction accuracy of cardiac diseases.
It is estimated that around 17 million people in the world die from cardiac related events each year. It has also been shown that many of such deaths can be averted with long-term continuous monitoring and actuation. Hence, there is a growing need for better cardiac monitoring solutions. Leveraging the improvements in computational power, communication bandwidth, energy efficiency and electronic chip size in recent years, the Integrated Vectorcardiogram (iVCG) was invented as an answer to this problem. The iVCG is a miniaturized, integrated version of the Vectorcardiogram that was invented in the 1930s. The Vectorcardiogram provides full diagnostic quality cardiac information equivalent to that of the gold standard, 12-lead ECG, which is restricted to in-office use due to its bulky, obtrusive form. With the iVCG, it is possible to provide continuous, long-term, full diagnostic quality information, while being portable and unobtrusive to the patient. Moreover, it is possible to leverage this ‘Big Data’ and create machine learning algorithms to deliver better patient outcomes in the form of patient specific machine diagnosis and timely alerts.
First, we present a proof-of-concept investigation for a miniaturized vectorcardiogram, the iVCG system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes such that the signals are distinguishable from the noise. The target dimensions for our prototype iVCG are 3x3x2 cm and based on our experimental results we show that it is possible to achieve these dimensions.
Following this, we present a solution to the problem of transforming the three VCG component signals to the familiar 12-lead ECG for the convenience of cardiologists. The least squares (LS) method is employed on the VCG signals and the reference (training) 12-lead ECG to obtain a 12x3 transformation matrix to generate the real-time ECG signals from the VCG signals.
The iVCG is portable and worn on the chest of the patient and although a physician or trained technician will initially install it in the appropriate position, it is prone to subsequent rotation and displacement errors introduced by the patient placement of the device. We characterize these errors and present a software solution to correct the effect of the errors on the iVCG signals.
We also describe the design of machine learning methods to improve automatic diagnosis and prediction of various heart conditions. Methods very similar to the ones described in this dissertation can be used on the long term, full diagnostic quality ‘Big Data’ such that the iVCG will be able to provide further insights into the health of patients.
The iVCG system is potentially breakthrough and disruptive technology allowing long term and continuous remote monitoring of patient’s electrical heart activity. The implications are profound and include 1) providing a less expensive device compared to the 12-lead ECG system (the “gold standard”); 2) providing continuous, remote tele-monitoring of patients; 3) the replacement of current Holter shortterm monitoring system; 4) Improved and economic ICU cardiac monitoring; 5) The ability for patients to be sent home earlier from a hospital since physicians will have continuous remote monitoring of the patients.
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Tanrikulu, Oguz. "Adaptive signal processing algorithms with accelerated convergence and noise immunity." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7877.
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Lin, Lu. "Adaptive signal processing in subbands using sigma-delta modulation technique." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6532.
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In this thesis, the use of subbanding and sigma-delta modulation in interference/noise cancellation is intensively studied and a sigma-delta modulated subbanded adaptive interference/noise cancellation system is proposed. The filter bank is fully sigma-delta modulated. The output signal from the filter bank is then used to produce the input to the adaptive filter. The adaptive filter is partially sigma-delta modulated. The output is demodulated at the final stage. Maintaining the sigma-delta modulated signal representation throughout the system results in considerable savings in complexity. The performance of the proposed system is studied and compared to the regular non sigma-delta modulated case regarding complexity, convergence speed and steady state error. The effect of the oversampling rate used in the sigma-delta modulation as well as the quality of the demodulator is also considered. It is shown that in the case of interference cancellation a comb filter is sufficient, while in the case of noise canceller a good quality demodulator is essential. The thesis concludes by highlighting the tradeoffs between the hardware complexity reduction and the overall system performance.
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White, Paul Robert. "Adaptive signal processing and its application to infrared detector systems." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316442.
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Scott, Iain. "Partially adaptive array signal processing with application to airborne radar." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/12912.
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An adaptive array is a signal processor used in conjunction with a set of antennae to provide a versatile form of spatial filtering. The processor combines spatial samples of a propagating field with a variable set of weights, typically chosen to reject interfering signals and noise. In radar, the spatial filtering capability of the array facilitates cancellation of hostile jamming signals and aids in the suppression of clutter. In many applications, the practical usefulness of an adaptive array is limited by the complexity associated with computing the adaptive weights. In a partially adaptive beamformer only a subset of the available degrees of freedom are used adaptively, where adaptive degree of freedom denotes the number of unconstrained or free weights that must be computed. The principal benefits associated with reducing the number of adaptive degrees of freedom are reduced computational burden and improved adaptive convergence rate. The computational cost of adaptive algorithms is generally either directly proportional to the number of adaptive weights or to the square or cube of the number of adaptive weights. In radar it is often mandatory that the number of adaptive weights be reduced with large antenna arrays because of the algorithms computational requirement. The number of data vectors needed for the adaptive weights to converge to their optimal values is also proportional to the number of adaptive weights. Thus, in some applications, adaptive response requirements dictate reductions in the number of adaptive weights. Both of these aspects are investigated in this thesis. The primary disadvantage of reducing the number of adaptive weights is a degradation in the steady-state interference cancellation capability. This degradation is a function of which adaptive degrees of freedom are utilised and is the motivation for the partially adaptive design techniques detailed in this thesis. A new technique for selecting adaptive degrees of freedom is proposed. This algorithm sequentially selects adaptive weights based on an output mean square error criterion. It is demonstrated through simulation that for a given partially adaptive dimension this approach leads to improved steady-state performance, in mean square error terms, over popular eigenstructure approaches. Additionally, the adaptive structure which results from this design method is computationally efficient, yielding a reduction of around 80% in the number of both complex multiplications and additions.
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Chambers, Jonathon Arthur. "Digital signal processing algorithms and structures for adaptive line enhancing." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/47797.
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Chen, Jen Mei. "Multistage adaptive filtering in a multirate digital signal processing system." Thesis, Massachusetts Institute of Technology, 1993. https://hdl.handle.net/1721.1/127935.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.Includes bibliographical references (leaves 101-104).
by Jen Mei Chen.
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.