Relevant bibliographies by topics / Adaptive signal processing – Mathematics

Academic literature on the topic 'Adaptive signal processing – Mathematics'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Adaptive signal processing – Mathematics"

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Perić, Zoran, Vlado Delić, Zoran Stamenković, and David Pokrajac. "Advanced Signal Processing and Adaptive Learning Methods." Computational Intelligence and Neuroscience 2019 (November 3, 2019): 1–2. http://dx.doi.org/10.1155/2019/5428615.

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Zou, Dong Lan. "Research on the Sensor Coarse Signal Processing Model Based on Adaptive Genetic Algorithm." Applied Mechanics and Materials 443 (October 2013): 342–45. http://dx.doi.org/10.4028/www.scientific.net/amm.443.342.

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With the rapid development of electronic information science and network transmission technology, the signal processing technology has been widely applied to various fields, which is the most important component of signal detection and transmission, and the key signal processing technology for processing sensor crude signals. Based on this, the experimental system of sensor coarse signal processing model is established, and in the experimental system, the transformer can carry out signal recognition for voltage and current, the use of PC microcontroller and embedded AD converter carries out analog / digital conversion for sensor crude signal. For the amplification process of sensor coarse signal, the use of adaptive genetic algorithm carries out mathematical modeling, the realization of the signal identification, acquisition and processing functions through software programming control. Finally, the intelligent processing of sensor coarse signal is successfully completed by the experiment system, and the signal processing effect is given as well.
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Uskovas, Gediminas, Algimantas Valinevicius, Mindaugas Zilys, Dangirutis Navikas, Michal Frivaldsky, Michal Prauzek, Jaromir Konecny, and Darius Andriukaitis. "A Novel Seismocardiogram Mathematical Model for Simplified Adjustment of Adaptive Filter." Electronics 11, no. 15 (August 5, 2022): 2444. http://dx.doi.org/10.3390/electronics11152444.

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Nonclinical measurements of a seismocardiogram (SCG) can diagnose cardiovascular disease (CVD) at an early stage, when a critical condition has not been reached, and prevents unplanned hospitalization. However, researchers are restricted when it comes to investigating the benefits of SCG signals for moving patients, because the public database does not contain such SCG signals. The analysis of a mathematical model of the seismocardiogram allows the simulation of the heart with cardiovascular disease. Additionally, the developed mathematical model of SCG does not totally replace the real cardio mechanical vibration of the heart. As a result, a seismocardiogram signal of 60 beats per min (bpm) was generated based on the main values of the main artefacts, their duration and acceleration. The resulting signal was processed by finite impulse response (FIR), infinitive impulse response (IRR), and four adaptive filters to obtain optimal signal processing settings. Meanwhile, the optimal filter settings were used to manage the real SCG signals of slowly moving or resting. Therefore, it is possible to validate measured SCG signals and perform advanced scientific research of seismocardiogram. Furthermore, the proposed mathematical model could enable electronic systems to measure the seismocardiogram with more accurate and reliable signal processing, allowing the extraction of more useful artefacts from the SCG signal during any activity.
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Lee, Kwan-Hyeong. "A Study on Target Detection using Covariance Correlation Matrix of Spatial Adaptive Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 236–42. http://dx.doi.org/10.17762/turcomat.v12i5.884.

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In this paper, we study for direction of arrival estimation of the desired target in spatial adaptive processing system. The interference signal removed by using the optimal weight of the covariance correlation matrix in order to estimate desired target signal. The spatial adaptive processing system updates the weight of the direction of arrival algorithm to estimate the desired signal. The weight update use an adaptive algorithm such as MUSIC. The optimal weight is obtained by Lagrange multiplier and the covariance correlation matrix. The covariance correlation matrix applies signal phase matching and uses the output power spectrum of the direct of arrival algorithm to estimate the desired target direction. We compare the performance of the proposed method with the existing method by computer simulation. The existing method has poor resolution due to phase errors of 5o and -3o in the estimation of three targets [10o, 20o, 30o]. While, the method proposed in this study accurately estimated the desired three targets. This study proved that the proposed method is superior to the existing method as a result simulation result.
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Ghalyan, Najah F., Asok Ray, and William Kenneth Jenkins. "A Concise Tutorial on Functional Analysis for Applications to Signal Processing." Sci 4, no. 4 (October 21, 2022): 40. http://dx.doi.org/10.3390/sci4040040.

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Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated and developed through collaborative efforts of engineers and mathematicians who used results from Hilbert spaces, Hardy spaces, weak topology, and other topics of functional analysis to establish essential analytical structures for many subfields in signal processing. This paper presents a concise tutorial for understanding the theoretical concepts of the essential elements in functional analysis, which form a mathematical framework and backbone for central topics in signal processing, specifically statistical and adaptive signal processing. The applications of these concepts for formulating and analyzing signal processing problems may often be difficult for researchers in applied sciences and engineering, who are not adequately familiar with the terminology and concepts of functional analysis. Moreover, these concepts are not often explained in sufficient details in the signal processing literature; on the other hand, they are well-studied in textbooks on functional analysis, yet without emphasizing the perspectives of signal processing applications. Therefore, the process of assimilating the ensemble of pertinent information on functional analysis and explaining their relevance to signal processing applications should have significant importance and utility to the professional communities of applied sciences and engineering. The information, presented in this paper, is intended to provide an adequate mathematical background with a unifying concept for apparently diverse topics in signal processing. The main objectives of this paper from the above perspectives are summarized below: (1) Assimilation of the essential information from different sources of functional analysis literature, which are relevant to developing the theory and applications of signal processing. (2) Description of the underlying concepts in a way that is accessible to non-specialists in functional analysis (e.g., those with bachelor-level or first-year graduate-level training in signal processing and mathematics). (3) Signal-processing-based interpretation of functional-analytic concepts and their concise presentation in a tutorial format.
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KIM, KEONWOOK, and ALAN D. GEORGE. "PARALLEL SUBSPACE PROJECTION BEAMFORMING FOR AUTONOMOUS, PASSIVE SONAR SIGNAL PROCESSING." Journal of Computational Acoustics 11, no. 01 (March 2003): 55–74. http://dx.doi.org/10.1142/s0218396x0300181x.

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Adaptive techniques can be applied to improve performance of a beamformer in a cluttered environment. The sequential implementation of an adaptive beamformer, for many sensors and over a wide band of frequencies, presents a serious computational challenge. By coupling each transducer node with a microprocessor, in-situ parallel processing applied to an adaptive beamformer on a distributed system can glean advantages in execution speed, fault tolerance, scalability, and cost. In this paper, parallel algorithms for Subspace Projection Beamforming (SPB), using QR decomposition on distributed systems, are introduced for in-situ signal processing. Performance results from parallel and sequential algorithms are presented using a distributed system testbed comprised of a cluster of computers connected by a network. The execution times, parallel efficiencies, and memory requirements of each parallel algorithm are presented and analyzed. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of future advanced beamforming algorithms in a scalable fashion.
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Ni, Chenqiang, He Xue, Shuai Wang, Xiurong Fang, and Hongliang Yang. "Crack Growth Signal Processing Approach Combining Wavelet Threshold Denoising and Variable Amplitude DCPD Technique." Mathematical Problems in Engineering 2021 (October 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5510361.

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The direct current potential drop (DCPD) method is widely used in laboratory environments to monitor the crack initiation and propagation of specimens. In this study, an anti-interference signal processing approach, combining wavelet threshold denoising and a variable current amplitude DCPD signal synthesis technique, was proposed. Adaptive wavelet threshold denoising using Stein’s unbiased risk estimate was applied to the main potential drop signal and the reference potential signal under two different current amplitudes to reduce the interference caused by noise. Thereafter, noise-reduced signals were synthesized to eliminate the time-varying thermal electromotive force. The multiplicative interference signal was eliminated by normalizing the main potential drop signal and the reference potential drop signal. This signal processing approach was applied to the crack growth monitoring data of 316 L stainless steel compact tension specimens in a laboratory environment, and the signal processing results of static cracks and propagation cracks under different load conditions were analyzed. The results showed that the proposed approach can significantly improve the signal-to-noise ratio as well as the accuracy and resolution of the crack growth measurement.
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Tkachuk, O. V. "OPTIMAL IMAGE SIGNALS PROCESSING ON THE NOISE BACKGROUND IN THE INFORMATION SYSTEM WITH ADAPTIVE ANTENNA ARRAY." Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì -, no. 2(17) (2020): 29–36. http://dx.doi.org/10.32684/2412-5288-2020-2-17-29-36.

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The method to restore image signals against the arbitrary intensity noise background in the information radio engineering system with adaptive antenna array has been developed. In order to use methods developed for processing one-dimensional signals for image recovery in the information system with adaptive antenna array, the transition from the two-dimensional array to vector representation is carried out. Mathematical model of narrowband signal formed at input of antenna array elements in space-time sense is obtained. Correlation matrices of image carrier signals, interference and noise are considered and features of adaptive processing of image signals coming from several sources are observed. An expression was found for the likelihood function if the incoming vector process is a multivariate stationary Gaussian process with a non-zero mean. According to the maximum likelihood criterion, the expression for the system of optimal independent parametric weight vectors necessary for image signals restoring against the arbitrary intensity noise background coming from several independent sources in the information system with adaptive antenna array is obtained. In accordance to this system, a signal processing algorithm is built in the adaptive processor of N-dimensional adaptive antenna array. A simulation model of image signal restores coming from one source in the information system with adaptive antenna array against the arbitrary intensity noise background coming from several independent sources is built. It is shown that the use of weight coefficients calculated on the basis of the correlation matrix of observations, due to its properties, does not allow dividing the set of correlated image signals. The direction of further development of the obtained results in the class of invariant methods is determined.
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Gong, Tianzhuo, and Sibing Sun. "Feature Extraction of Music Signal Based on Adaptive Wave Equation Inversion." Advances in Mathematical Physics 2021 (October 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/8678853.

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The digitization, analysis, and processing technology of music signals are the core of digital music technology. There is generally a preprocessing process before the music signal processing. The preprocessing process usually includes antialiasing filtering, digitization, preemphasis, windowing, and framing. Songs in the popular wav format and MP3 format on the Internet are all songs that have been processed by digital technology and do not need to be digitalized. Preprocessing can affect the effectiveness and reliability of the feature parameter extraction of music signals. Since the music signal is a kind of voice signal, the processing of the voice is also applicable to the music signal. In the study of adaptive wave equation inversion, the traditional full-wave equation inversion uses the minimum mean square error between real data and simulated data as the objective function. The gradient direction is determined by the cross-correlation of the back propagation residual wave field and the forward simulation wave field with respect to the second derivative of time. When there is a big gap between the initial model and the formal model, the phenomenon of cycle jumping will inevitably appear. In this paper, adaptive wave equation inversion is used. This method adopts the idea of penalty function and introduces the Wiener filter to establish a dual objective function for the phase difference that appears in the inversion. This article discusses the calculation formulas of the accompanying source, gradient, and iteration step length and uses the conjugate gradient method to iteratively reduce the phase difference. In the test function group and the recorded music signal library, a large number of simulation experiments and comparative analysis of the music signal recognition experiment were performed on the extracted features, which verified the time-frequency analysis performance of the wave equation inversion and the improvement of the decomposition algorithm. The features extracted by the wave equation inversion have a higher recognition rate than the features extracted based on the standard decomposition algorithm, which verifies that the wave equation inversion has a better decomposition ability.
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Zhen, Jingran. "Rotating Machinery Fault Diagnosis Based on Adaptive Vibration Signal Processing under Safety Environment Conditions." Mathematical Problems in Engineering 2022 (May 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/1543625.

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At present, the degree of industrialization in China is deepening, and various types of production equipment appear. However, during the startup and operation of mechanical equipment, fracture and wear will occur due to various factors. Therefore, once the mechanical equipment fails, it must be diagnosed as soon as possible to avoid serious economic losses and casualties. Rotating machinery is an important power device, so it is necessary to regularly detect and monitor equipment signals to avoid the consequences of wrong control methods. In this study, the fault diagnosis of rotating machine based on adaptive vibration signal processing is studied under the safe environmental conditions. The fault diagnosis process of rotating machinery is to first collect vibration signals, then process signal noise reduction, and then extract fault characteristic signals to further identify and classify fault status and diagnose fault degree. This study briefly introduces several rotating machinery vibration signal processing methods and identifies the fault state of the rotating machine based on the high-order cumulant. By building a DDS fault diagnosis test bench, the chaotic particle swarm parameter optimization algorithm is used to calculate the accurate stochastic resonance parameters. After noise processing, the high-frequency part is significantly reduced. The results show that, after stochastic resonance wavelet decomposition and denoising processing, the number of intrinsic functions can be significantly reduced, the fault frequency can be increased, the high-frequency noise can be reduced, and the fault analysis accuracy can be improved. We identify the fault state of rotating machinery based on the high-order cumulant, train the four states of the bearing, and compare the four types of faults, no fault, inner ring fault, rolling element fault, and outer ring fault through the comparison of the actual test set and the predicted test set. It is concluded that the rotating machinery fault belongs to the rolling element fault and the identification accuracy rate is 95%. Finally, based on the LMD morphological filtering, the rotating machinery fault diagnosis is carried out, and the feature extraction is carried out based on the LMD algorithm to decompose the bearing fault signal. Finally, the result after the morphological filtering and LMD decomposition and extraction can avoid noise interference.
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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 reconstruction
With 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&gt. "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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Books on the topic "Adaptive signal processing – Mathematics"

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1934-, Åström Karl J., Goodwin Graham C. 1945-, and Kumar P. R, eds. Adaptive control, filtering, and signal processing. New York: Springer-Verlag, 1995.

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Solo, Victor. Adaptive signal processing algorithms: Stability and performance. Englewood Cliffs, N.J: Prentice Hall, 1995.

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Manolakis, Dimitris G. Statistical and adaptive signal processing: Spectral estimation, signal modeling, adaptive filtering, and array processing. Boston: McGraw-Hill, 2000.

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Adaptive IIR filtering in signal processing and control. New York: M. Dekker, 1995.

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Ifeachor, Emmanuel C. Digital signal processing: A practical approach. 2nd ed. Harlow, England: Prentice Hall, 2002.

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Ifeachor, Emmanuel C. Digital signal processing: A practical approach. Wokingham, England: Addison-Wesley, 1993.

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Ruano, António E. New Advances in Intelligent Signal Processing. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Macchi, Odile. Adaptive processing: The least mean squares approach with applications in transmission. Chichester: John Wiley & Sons, 1995.

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1963-, Lakey Joseph D., ed. Time-frequency and time-scale methods: Adaptive decompositions, uncertainty principles, and sampling. Boston: Birkhauser, 2005.

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Adaptive filtering: Fundamentals of least mean squares with MATLAB. Boca Raton: CRC Press/Taylor & Francis Group, 2014.

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Book chapters on the topic "Adaptive signal processing – Mathematics"

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Bognár, Gergő, Sándor Fridli, Péter Kovács, and Ferenc Schipp. "Adaptive Rational Transformations in Biomedical Signal Processing." In Progress in Industrial Mathematics at ECMI 2018, 239–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27550-1_30.

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Debayle, Johan, and Jean-Charles Pinoli. "General Adaptive Neighborhood Viscous Mathematical Morphology." In Mathematical Morphology and Its Applications to Image and Signal Processing, 224–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21569-8_20.

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Pessoa, Lúcio F. C., and Petros Maragos. "MRL-Filters and Their Adaptive Optimal Design for Image Processing." In Mathematical Morphology and its Applications to Image and Signal Processing, 155–62. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_18.

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De Mendonça Braga Neto, Ulisses. "Alternating Sequential Filters by Adaptive-Neighborhood Structuring Functions." In Mathematical Morphology and its Applications to Image and Signal Processing, 139–46. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_16.

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Dougherty, Edward R., and Yidong Chen. "Optimal and Adaptive Design of Reconstructive Granulometric Filters." In Mathematical Morphology and its Applications to Image and Signal Processing, 253–61. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_29.

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Agam, Gady, and Its’hak Dinstein. "Adaptive Directional Morphology with Application to Document Analysis." In Mathematical Morphology and its Applications to Image and Signal Processing, 401–8. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_47.

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Angulo, Jesús. "Morphological Bilateral Filtering and Spatially-Variant Adaptive Structuring Functions." In Mathematical Morphology and Its Applications to Image and Signal Processing, 212–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21569-8_19.

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Mulgrew, Bernard, Peter Grant, and John Thompson. "Adaptive filters." In Digital Signal Processing, 206–39. London: Macmillan Education UK, 1999. http://dx.doi.org/10.1007/978-1-349-14944-5_8.

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Chonavel, Thierry. "Adaptive Estimation." In Statistical Signal Processing, 231–48. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_16.

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Mulgrew, Bernard. "Adaptive filters." In Digital Signal Processing, 213–45. London: Macmillan Education UK, 2003. http://dx.doi.org/10.1057/978-1-137-44655-8_8.

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Conference papers on the topic "Adaptive signal processing – Mathematics"

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Cao, Pei, Qi Shuai, and Jiong Tang. "Damage Identification Using Piezoelectric Impedance Measurement With Compressed Sensing Technique." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3936.

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During the last decades, extensive research has been conducted on structural health monitoring (SHM) techniques based on the changes of coupled structure properties, e.g. piezoelectric impedance, which enjoys high detection sensitivity due to high-frequency actuation/sensing nature. However, the actual identification of fault locations and severities remains to be challenging owing to underdetermined underling mathematics. Recently, compressed sensing, a signal processing technique originally developed to recover signals from the compressed measurements, has shown its potential to address some of the mathematical challenges encountered in SHM practices. In this research, we morph the impedance-based SHM problem into a compressed sensing scheme such that the impedance change are used as measured data to reconstruct the damage locations and severities through convex optimization, e.g. l1 optimization. The proposed approach offers practical attractions of only requiring a small number of measurements and a short amount of computational time, and the results are promising if certain properties are fulfilled. Finally, the proposed approach is applied to and validated by several test problems.
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Wang, D., V. Haese-Coat, and A. Bruno. "Adaptive segmentation of textures using mathematical morphology." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150955.

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Ganguly, Biswarup, Anwesa Bhattacharya, Ananya Srivastava, Debangshu Dey, and Sugata Munshi. "Fusion of Mathematical Morphology with Adaptive Gamma Correction for Dehazing and Visibility Enhancement of Images." In 2020 IEEE Applied Signal Processing Conference (ASPCON). IEEE, 2020. http://dx.doi.org/10.1109/aspcon49795.2020.9276734.

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Zhang, Yungang, Bailing Zhang, and Wenjin Lu. "Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647208.

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Schneider, Peter, Andreas Köhler, Sven Reitz, and Roland Jancke. "Behavioral and Network Modeling for Efficient Design of Adaptive Systems." In ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smasis2014-7733.

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Adaptive systems usually implement the entire cycle of measurement and data acquisition, signal conditioning and processing as well as process control. Especially, for the design of adaptive signal processing and control algorithms detailed insight into the interaction between the system components is of crucial importance. System level simulations are a suitable way to gain insight and to support algorithm design and test. However, an adequate mathematical representation of the system behavior is needed to take advantage of this method. In the paper a generic methodology for behavioral modelling is introduced. Important steps of the modelling process are described and illustrated by two examples. For a gyro sensor system the combination of different modeling methods is demonstrated. Network modeling and in particular an approach for the construction of network models for magnetic systems is discussed for an electromagnetic switching device.
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Ricci, R., P. Borghesani, S. Chatterton, and P. Pennacchi. "The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71012.

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Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.
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MALLAT, STÉPHANE. "APPLIED MATHEMATICS MEETS SIGNAL PROCESSING." In Proceedings of the International Conference on Fundamental Sciences: Mathematics and Theoretical Physics. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812811264_0006.

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Yang, Jie, Xiaoming Zhu, Gerald E. Sobelman, and Keshab K. Parhi. "Sparseness-Controlled Adaptive Tap algorithms for partial update adaptive filters." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397686.

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Cai, Z. Q., and Tracey K. M. Lee. "Adaptive switching median filter." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397741.

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"Adaptive antennas, signal processing." In 2005 5th International Conference on Antenna Theory and Techniques. IEEE, 2005. http://dx.doi.org/10.1109/icatt.2005.1496940.

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Reports on the topic "Adaptive signal processing – Mathematics"

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Albert, T. R. Adaptive Signal Processing at NOSC. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250245.

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Tufts, Donald W. Adaptive, Robust, High-Resolution Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada223728.

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Schmitt, Harry A. Advanced Mathematics for Optimizing Missile Seeker Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada398610.

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Brady, David J., Mark A. Neifeld, and Travis Blalock. Adaptive Multiplexed Wavelength and Spatial Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada449523.

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Shamma, Shihab A., and P. S. Krishnaprasad. Signal Processing and Recognition in Adaptive Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada250505.

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Honig, Michael L. Adaptive Signal Processing Techniques for Robust, High Capacity Spread- Spectrum Multiple Access. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada422622.

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Preisig, James C. High-Frequency Acoustic Propagation and Adaptive Signal Processing: An Integrated Research Program. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625506.

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Lesser, Victor R., Hamid Nawab, and Donald Weiner. High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 1. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada300901.

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Casey, Stephen D. New Techniques in Time-Frequency Analysis: Adaptive Band, Ultra-Wide Band and Multi-Rate Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 2016. http://dx.doi.org/10.21236/ad1005007.

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Boyd, Stephen, and Thomas Kailath. SIAM Workshop on Mathematics of Systems and Signal Processing Held in Stanford, California on 31 August-4 September 1987. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada194204.

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