Relevant bibliographies by topics / Adaptive Filters

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Adaptive Filters'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Adaptive Filters"

1

Pichardo, Eduardo, Ángel Vázquez, Esteban R. Anides, Juan C. Sánchez, Hector Perez, Juan G. Avalos, and Giovanny Sánchez. "A Dual Adaptive Filter Spike-Based Hardware Architecture for Implementation of a New Active Noise Control Structure." Electronics 10, no. 16 (August 12, 2021): 1945. http://dx.doi.org/10.3390/electronics10161945.

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Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.
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Brewster, R. L. "Adaptive Filters." Electronics and Power 32, no. 7 (1986): 545. http://dx.doi.org/10.1049/ep.1986.0315.

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Cowan, Colin F. N., and Peter M. Grant. "Adaptive filters." Signal Processing 9, no. 2 (September 1985): 138. http://dx.doi.org/10.1016/0165-1684(85)90050-7.

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Fettweis, A., and A. Zalnieriunas. "Adaptive filters." Signal Processing 13, no. 3 (October 1987): 335–36. http://dx.doi.org/10.1016/0165-1684(87)90136-8.

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Cowan, C. F. N. "Adaptive filters." IEE Proceedings F Communications, Radar and Signal Processing 134, no. 3 (1987): 209. http://dx.doi.org/10.1049/ip-f-1.1987.0043.

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Harteneck, M., J. M. Pàez-Borrallo, and R. W. Stewart. "An oversampled subband adaptive filter without cross adaptive filters." Signal Processing 64, no. 1 (January 1998): 93–101. http://dx.doi.org/10.1016/s0165-1684(97)00179-5.

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Jain, Deepanjali. "A Novel LMS Algorithm Applied to Adaptive Noise Cancellation with Varying Parameters." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 47–51. http://dx.doi.org/10.22214/ijraset.2022.40179.

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Abstract: Adaptive filters have become active area of research in the field of communication system. This paper explores the novel concept of adaptive noise cancellation (ANC) using least-mean-square (LMS) adaptive filters. The model of the LMS-ANC is designed and simulated in MATLAB environment. The proposed algorithm utilizes adaptive filters to evaluate gradients accurately which results in good adaptation, stability and performance. The objective of this investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters. The results are obtained with the help of adaptive algorithm with variable step size and filter order in order to deliver high convergence speed and stability of the error signal. Keywords: Adaptive Noise cancellation, LMS algorithm, MATLAB, Filter order, Step size.
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M. Africa, Aaron Don, John Arvin Mercado, and Joshua Kenichi Sim. "Development of an adaptive finite impulse response filter optimization algorithm using rough set theory." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (July 1, 2023): 238. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp238-247.

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Signal processing is crucial that as one sends information, there is a corresponding process to encode, decode, and clean the signal of unwanted noise and disruptions via use of filters. Due to the environment and how unpredictable it can be and how noise can come from almost anywhere, the typical filter to be used are adaptive filters. Adaptive filters are non-linear filters and have been used regularly regarding adaptive signal processing, this means that the filter changes accordingly and adapts to the environmental noise surrounding it. The world today has numerous applications for adaptive filters such as channel equalization and acoustic noise cancellation. This incentivizes the further development of this specific technology and the constant research that is ongoing. The main component when it comes to adaptive filtering is the algorithms used for the filter. This research compares the least mean square (LMS) and recursive least square (RLS) algorithms concerning their effectiveness in filtering out unwanted acoustic noises. The paper will cover the design and implementation of an optimized rough set based adaptive finite impulse response (FIR) filter for acoustic noise cancellation. The microstrip and bowtie antenna were used to relay the data. The software MATLAB was used for the simulation.
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Li, Song, Roman Schlieper, and Jürgen Peissig. "Semi-adaptive active noise cancellation headphones." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4717–23. http://dx.doi.org/10.3397/in-2021-2808.

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Active noise cancellation (ANC) headphones are becoming increasingly important as they can effectively attenuate perceived ambient noise. Fixed filters are commonly applied in commercially available ANC headphones due to their robustness. However, they are not capable of adapting to changes that occur in dynamic environments, resulting in degraded ANC performance. In contrast, adaptive filters are able to update the ANC filters to compensate for noise in dynamic environments, but large estimation errors can occur due to a sudden change in direction/type of noise or secondary path. Some studies have suggested an ANC system by combining fixed and adaptive filters. Based on this mechanism, we propose a semi-adaptive ANC system in which the fixed and adaptive filters are weighted in real-time. Initially, the weighting for the fixed filter dominates the whole system to ensure the robustness of the ANC system. Then, the residual error provided by the adaptive filter is simulated and compared to the real measured one to determine the relative weighting between the fixed and adaptive filters. In this study, this approach is applied to a feedback ANC system. Simulation results show that our proposed approach achieves high noise attenuation performance while maintaining robustness with time-varying secondary paths.
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Ristau, J. P., and Wooil M. Moon. "Adaptive filtering of random noise in 2-D geophysical data." GEOPHYSICS 66, no. 1 (January 2001): 342–49. http://dx.doi.org/10.1190/1.1444913.

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Random noise is often a problem in geophysical data visualization because it obscures fine details and complicates identification of image features. Adaptive filters have recently been used to suppress speckle (random) noise in synthetic aperture radar (SAR) images. SAR data are similar to seismic reflection data, both in their data acquisition approach and in their final data processed format. The nature of the random noise associated is also very similar, and adaptive filters can be applied to reduce random noise in both types of data sets. In this paper several popular adaptive filters—the Lee filter, the Frost filter, and the Kuan filter, which have been used frequently for speckle reduction in SAR data—are tested on Lithoprobe (AGT) deep seismic reflection data and on one set of oil industry shallow seismic reflection data. In addition, a standard band‐pass filter, which is common in many seismic data processing packages, is tested with the oil industry test data. Performance of the adaptive filters is also tested on Radarsat SAR data. The random (speckle) noise in both the Lithoprobe and the Radarsat (SAR) data sets is statistically very similar, and the adaptive filters tested successfully suppressed random noise while minimizing blurring. Among the tested filters the enhanced Lee filter performed best, closely followed by the enhanced Frost filter and the Kuan filter. The background noise in the oil industry seismic data is statistically quite different from the above two data sets; the results obtained were less than satifactory, although they were still encouraging. With the oil industry data, the enhanced Frost and Kuan filters performed better than the enhanced Lee filter. The commonly used band‐pass filter successfully removed the background (random) noise, but it also suppressed the reflection events, making the final result less desirable.
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Dissertations / Theses on the topic "Adaptive Filters"

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Ryan, James Gregory Carleton University Dissertation Engineering Electrical. "Subband adaptive filters." Ottawa, 1992.

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Eshghi, Mohammad. "Highly parallel transversal adaptive filter." Ohio : Ohio University, 1988. http://www.ohiolink.edu/etd/view.cgi?ohiou1182785469.

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Suhasini, Subba Rao Tata. "Wavelets and adaptive filters." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364840.

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Chamon, Luiz Fernando de Oliveira. "Combinations of adaptive filters." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-14072016-143633/.

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Adaptive filtering has grown to become a fundamental topic in signal processing, increasingly attracting attention from the community. Important factors in this popularization were their low computational complexity and model-free nature, adapting even to nonstationary characteristics of the systems and/or signals under study. Nevertheless, many adaptive algorithms introduce trade-offs, for instance, between convergence rate, nonstationary signals tracking, and steady-state error, which can hinder their use in practical applications. Furthermore, some adaptive filters can become unstable when word length is reduced and/or the input data are highly correlated. Recently, combination of adaptive filters was put forward as a solution for such issues. This approach consists in combining a pool of filters by means of a supervisor that attempts to make the overall system at least as good (usually in the mean-square sense) as the best filter in the set. Examples of these structures have been shown to successfully solve this problem, although well-known limitations remain to be addressed. Moreover, due to the relative novelty of this topic, developments in combination of adaptive filters are difficult to accommodate into a common theoretical framework. This work studies combination of adaptive filters and addresses the aforementioned issue by (i) classifying the existing combinations and proposing a taxonomy that exposes the similarities and differences in their forms; (ii) proposing new combinations; (iii) devising a general framework for studying combinations of adaptive filters and using such framework in performance analyses.
Filtragem adaptativa vem ganhando destaque desde seu surgimento tornando-se um tópico de estudo fundamental em processamento de sinais. A versatilidade de dispensarem total conhecimento das propriedades estatísticas dos sinais, aliada à simplicidade computacional de seus métodos, foram importantes fatores em sua consagração. Apesar disto, muitos filtros adaptativos apresentam compromissos envolvendo, por exemplo, velocidade de convergência, rastreamento de sinais não-estacionários e erro em regime, que podem dificultar sua aplicação na prática. Ademais, alguns algoritmos adaptativos são instáveis quando suas entradas são altamente correlacionados e/ou a precisão dos cálculos é reduzida. Uma solução recente para estes problemas é o uso de combinações de filtros adaptativos. Esta abordagem baseia-se em combinar um conjunto de filtros por meio de um supervisor que procura fazer com que o sistema global seja pelo menos tão bom (em geral no sentido quadrático médio) quanto o melhor filtro do conjunto. Exemplos destas estruturas já mostraram a eficácia deste método, apesar de ainda existirem reconhecida limitações. Além disso, em se tratando de um tópico relativamente recente, os desenvolvimentos na área de combinação de filtros adaptativos não possuem uma estrutura teórica unificada. Este trabalho propõe abordar estas questões (i) classificando as combinações existentes e criando uma taxonomia que explicite semelhanças e diferenças entre elas; (ii) introduzindo novas combinações; e (iii) desenvolvendo uma forma unificada de descrever combinações de filtros adaptativos e usando-a em análises de desempenho.
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Lopes, Wilder Bezerra. "Geometric-algebra adaptive filters." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-22092016-143525/.

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This document introduces a new class of adaptive filters, namely Geometric- Algebra Adaptive Filters (GAAFs). Those are generated by formulating the underlying minimization problem (a least-squares cost function) from the perspective of Geometric Algebra (GA), a comprehensive mathematical language well-suited for the description of geometric transformations. Also, differently from the usual linear algebra approach, Geometric Calculus (the extension of Geometric Algebra to differential calculus) allows to apply the same derivation techniques regardless of the type (subalgebra) of the data, i.e., real, complex-numbers, quaternions etc. Exploiting those characteristics, among others, a general leastsquares cost function is posed, from which two types of GAAFs are designed. The first one, called standard, provides a generalization of regular adaptive filters for any subalgebra of GA. From the obtained update rule, it is shown how to recover the following least-mean squares (LMS) adaptive filter variants: real-entries LMS, complex LMS, and quaternions LMS. Mean-square analysis and simulations in a system identification scenario are provided, showing almost perfect agreement for different levels of measurement noise. The second type, called pose estimation, is designed to estimate rigid transformations { rotation and translation - in n-dimensional spaces. The GA-LMS performance is assessed in a 3-dimensional registration problem, in which it is able to estimate the rigid transformation that aligns two point clouds that share common parts.
Este documento introduz uma nova classe de filtros adaptativos, entitulados Geometric-Algebra Adaptive Filters (GAAFs). Eles s~ao projetados via formulação do problema de minimização (uma função custo de mínimos quadrados) do ponto de vista de álgebra geométrica (GA), uma abrangente linguagem matemática apropriada para a descrição de transformações geométricas. Adicionalmente, diferente do que ocorre na formulação com álgebra linear, cálculo geométrico (a extensão de álgebra geométrica que possibilita o uso de cálculo diferencial) permite aplicar as mesmas técnicas de derivação independentemente do tipo de dados (subálgebra), isto é, números reais, números complexos, quaternions etc. Usando essas e outras características, uma função custo geral de mínimos quadrados é proposta, da qual dois tipos de GAAFs são gerados. O primeiro, chamado standard, generaliza filtros adaptativos da literatura concebidos sob a perspectiva de subálgebras de GA. As seguintes variantes do filtro least-mean squares (LMS) s~ao obtidas como casos particulares: LMS real, LMS complexo e LMS quaternions. Uma análise mean-square é desenvolvida e corroborada por simulações para diferentes níveis de ruído de medição em um cenário de identificação de sistemas. O segundo tipo, chamado pose estimation, é projetado para estimar transformações rígidas - rotação e translação { em espaços n-dimensionais. A performance do filtro GA-LMS é avaliada em uma aplicação de alinhamento tridimensional na qual ele estima a tranformação rígida que alinha duas nuvens de pontos com partes em comum.
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FACEROLI, SILVANA TEREZINHA. "STABILIZATION OF ADAPTIVE IIR FILTERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8628@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
De modo geral, as análises de estabilidade em filtragem digital adaptativa referem-se a possíveis instabilidades nos algoritmos de adaptação. Entretanto, a estabilização do algoritmo é apenas parte do problema, visto que também deve ser considerada a estabilização das estruturas recursivas onde operam estes algoritmos. Este trabalho discute a estabilização das estruturas recursivas de filtragem digital utilizadas em filtros IIR adaptativos, apresentando soluções para seu emprego em tempo real. A estabilização da estrutura, somada a do algoritmo, tem o objetivo de estabilizar a operação do filtro como um todo. O estudo inicia pela análise dos principais algoritmos para filtros IIR adaptativos, enfatizando o aspecto estabilidade. Desta forma, pode-se atribuir eventuais instabilidades ocorridas na seqüência do trabalho apenas à estrutura, evitando confundi-las com problemas do algoritmo. Na seqüência, é proposto um novo algoritmo que pretende estabilizar as estruturas recursivas dos filtros IIR digitais através de um modelo com estrutura adaptável. O método é baseado na variação percentual da energia do erro, calculada em tempo real. Finalmente, são apresentados alguns exemplos, indicando a viabilidade do método proposto. Eles ainda mostram benefícios adicionais no seu uso no aspecto velocidade de convergência e redução de eventuais polarizações de parâmetros do processo.
The stability analysis in adaptative digital filters is generally referred to possible instabilities in the adaptation algorthms. However, the algorithm stabilization is only part of the problem, because the stabilization of the recursive structures, where this algorithms operate, should be also considered. This work discusses the stabilization of digital filtering recursive structures that are used in adaptative IIR filters, presenting solutions to its use real time. The stabilization of the structure, added to the stabilization of the algorithm, has the objective of stabilizing the whole filter operation. The study begins with the analysis of the main algorithms for adaptive IIR filters, emphasizing the stability aspect. In this way, instabilities wventually occurred in the sequence of the work could be atributed only to the structure, avoiding any confusion with algorithm´s errors. After that, a new algorithm, that intend to stabilize the recursive structures of the digital IIR filters with a adaptable structure model, is proposed. The method is based on the error energy percentage variation, with real time calculation. Finally, some examples are presented, indicating the viability of the proposed method, showing additional benefits in the convergence speed and reduction of the polarization of the process parameters.
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Che, Ujang Che Ahmad Bukhari Bin. "Quaternion-valued nonlinear adaptive filters." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9601.

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Advances in vector sensor technology have created a need for adaptive nonlinear signal processing in the quaternion domain. The main concern of this thesis lies in the issue of analyticity of quaternion-valued nonlinear functions. The Cauchy-Riemann-Fueter (CRF) conditions determine the analyticity in the quaternion domain which proved too strict to be of any practical use. In order to circumvent this problem, split-quaternion nonlinear functions which are analytic componentwise are commonly employed. However, these functions do not fully capture the correlations between dimensions and are not suitable for real-world applications. To address this, the use of fully quaternion nonlinear functions in the derivation of a completely new class of algorithms which takes into consideration the non-commutative aspect of quaternion product is proposed. These fully quaternion functions satisfy the local analyticity condition (LAC) that guarantees the first-order differentiability of the function. This provides a unifying framework for the derivation of gradient based learning algorithms in the quaternion domain which are shown to have the same generic form as their real- and complex-valued counterparts. Unlike existing approaches, this new class of algorithms derived is suitable for the processing of signals with strong component correlations and is further extended to the recurrent neural network (RNN) architecture. Novel algorithms are also derived to improve the computational complexity of quaternion-valued adaptive filters which could be easily extended to incorporate nonlinear functions. A rigorous mathematical analysis provides a basis for the understanding of the convergence and steady-state performance of the proposed algorithms. Simulations over a range of synthetic and real-world signals support the approach taken in the thesis.
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Druckmüllerová, Hana. "Application of Adaptive Filters in Processing of Solar Corona Images." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-234232.

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Fotografování sluneční koróny patří mezi nejobtížnější úlohy astrofotografie a zároveň je jednou z klíčových metod pro studium koróny. Tato práce přináší ucelený souhrn metod pro pozorování sluneční koróny pomocí snímků. Práce obsahuje nutnou matematickou teorii, postup pro zpracování snímků a souhrn adaptivních filtrů pro vizualizaci koronálních struktur v digitálních obrazech. Dále přináší návrh nových metod určených především pro obrazy s vyšším obsahem šumu, než je běžné u obrazů bílé koróny pořízených během úplných zatmění Slunce, např. pro obrazy pořízené pomocí úzkopásmových filtrů. Fourier normalizing-radial-graded filter, který byl navržen v rámci této práce, je založen na aproximaci hodnot pixelů a jejich variability pomocí trigonometrických polynomů s využitím dalších vlastností obrazu.
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Hevey, Stephen J. "Adaptive Control using IIR Lattice Filters." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36736.

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This work is a study of a hybrid adaptive controller that blends fixed feedback control and adaptive feedback control techniques. This type of adaptive controller removes the requirement that information about the disturbance is known apriori. Additionally, the control structure is implemented in such a way that as long as the adaptive controller is stable during adaptation, the system consisting of the controller and plant remain stable. The objective is to design and implement an adaptive controller that damps the structural vibrations induced in a multi-modal structure. The adaptive controller utilizes an adaptive infinite impulse response lattice filter for improved damping over the fixed feedback controller alone. An adaptive finite impulse response LMS filter is also implemented for comparison of the ability for both algorithms to reject harmonic, narrow bandwidth and wide bandwidth disturbances. It is demonstrated that the lattice filter algorithm performs slightly better than the LMS filter algorithm in all three disturbance cases. The lattice filter also requires less than half the order of the LMS filter to get the same performance.
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Salminen, Daniel. "Adaptive filters applied on radar signals." Thesis, Uppsala universitet, Signaler och System, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-210087.

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This master thesis has been performed at SAAB AB in Järfälla, Sweden.A radar warning receiver must alert the user when someone highlights it with radarsignals. Radar signals used today varies and has a wide frequency band. In order todetect all possible radar signals the radar warning receiver must have a widebandwidth. This results in that the noise power will be high in the radar warningreceiver and weak radar signals will be hard to detect or even undetected.The aim of the thesis work was to investigate the possibility to suppress the noise inthe received radar signals. Unfortunately we do not know the frequency of thereceived radar signals, since the frequency has been decided by the threat radar. Wehave used adaptive filters, which adapts it band-pass to the received radar signal. Theadaptive filters must converge quickly to the state it reduces the noise and passes theradar signals since radar pulses can be very short in the time domain. We also wantto achieve a high SNR gain that is a measurement of how well the adaptive filterreduces the noise.We have investigated two adaptive algorithms, the recursive least square (RLS)algorithm and the least mean square (LMS) algorithm. We found out that the LMSalgorithm was more suitable for noise cancellation in radar applications due to its lowcomplexity and stability compared to RLS algorithm. The LMS algorithm gave SNRgains in the span 14-20 dB for different radar pulses.
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Books on the topic "Adaptive Filters"

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Sayed, Ali H. Adaptive filters. Hoboken, NJ: J. Wiley & Sons, 2008.

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N, Cowan C. F., Grant Peter M, and Adams P. F, eds. Adaptive filters. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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Kovačević, Branko, Zoran Banjac, and Milan Milosavljević. Adaptive Digital Filters. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33561-7.

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Kovačević, Branko. Adaptive Digital Filters. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Maurice, Bellanger, ed. Adaptive digital filters. 2nd ed. New York: Marcel Dekker, 2001.

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London), Colloquium on Adaptive Filters (1989. Colloquium on "Adaptive Filters". London: IEE Electronics Division, 1989.

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Shanbhag, Naresh R., and Keshab K. Parhi. Pipelined Adaptive Digital Filters. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2678-0.

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Mulgrew, Bernard, and Colin F. N. Cowan. Adaptive Filters and Equalisers. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1701-2.

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Zhao, Haiquan, and Badong Chen. Efficient Nonlinear Adaptive Filters. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20818-8.

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N, Cowan C. F., ed. Adaptive filters and equalisers. Boston: Kluwer Academic Publishers, 1988.

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Book chapters on the topic "Adaptive Filters"

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Diniz, Paulo S. R. "Kalman Filters." In Adaptive Filtering, 431–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_14.

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Diniz, Paulo S. R. "Kalman Filters." In Adaptive Filtering, 623–34. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_17.

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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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Mayer-Baese, Uwe. "Adaptive Filters." In Digital Signal Processing with Field Programmable Gate Arrays, 365–422. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-06728-4_8.

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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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Moir, Tom J. "Adaptive Filters." In Rudiments of Signal Processing and Systems, 387–422. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76947-5_14.

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Dickman, Arie. "Adaptive Filters." In Verified Signal Processing Algorithms in Matlab and C, 145–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93363-0_13.

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Kovačević, Branko, Zoran Banjac, and Milan Milosavljević. "Adaptive Filtering." In Adaptive Digital Filters, 31–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33561-7_2.

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Diniz, Paulo Sergio Ramirez. "Adaptive IIR Filters." In Adaptive Filtering, 377–436. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_9.

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Diniz, Paulo S. R. "Adaptive IIR Filters." In Adaptive Filtering, 307–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_10.

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Conference papers on the topic "Adaptive Filters"

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Mayer, Dirk, Sven Herold, and Holger Hanselka. "Application of Kautz Models for Adaptive Vibration Control." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/ad-23725.

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Abstract Both for active noise control (ANC) and active vibration control (AVC) the well known F-X-LMS-algorithm can be used. This approach requires a proper model of the path from the actuator to the error sensor, preferably received with an on-line identification. In the field of ANC adaptive finite impulse response (FIR) filters work well for this task, but for lightly damped mechanical systems with long impulse responses FIR filters with up to several thousand coefficients would have to be used. One alternative are adaptive IIR filters, but these can get unstable while adapting or the adapting process can get stuck in local minima. In this work, adaptive Kautz models are introduced, which need some a priori knowledge about the poles of the system. On the other hand, they represent an infinite impulse response while maintaining the transversal structure of the adaptive filter. This is reached by generalization of the FIR filter, for which the delay operator is substituted by discrete allpass filters, the Kautz filters. The adaptive filter bank is implemented by means of the straightforward LMS algorithm in the Matlab/Simulink environment. As an example, system identification with Kautz models and their usage in AVC for a simple mechanical system will be studied.
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M, Jeba Jenitha, Kani Jesintha D, and Mahalakshmi P. "Noise Adaptive Fuzzy Switching Median Filters for Removing Gaussian Noise." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/ozsc7243/ngcesi23p113.

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Recently, in all image processing systems, image restoration plays a major role and it forms the major part of image processing systems. Medical images such as brain Magnetic Resonance Imaging (MRI), ultrasound images of liver and kidney, retinal images and images of uterus images are often affected by various types of noises such as Gaussian noise and salt and pepper noise. All image restoration techniques attempts to remove various types of noises. This paper deals with various filters namely Mean Filter, Averaging Filter, Median Filter, Adaptive Median Filter, Adaptive Weighted Median Filter, Gabor Filter and Noise Adaptive Fuzzy Switching Median Filter (NAFSM) for removing salt and pepper noise. Among all the filters, NAFSM removes the Gaussian noise better than the other filters and the performance of all the filters are compared using metrics such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), NAE (Normalized Absolute Error), Normalized Cross Correlation (NK), Average Difference (AD), Maximum Difference (MD), SC (Structural Content) and time elapsed to produce the denoised image.
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Ghosh, Anjan K. "Design of fiber optic adaptive filters and neural networks." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.wj6.

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There is a tremendous need for high speed optical signal processing devices for working with guided lightwave signals. Various adaptive filters and Adaline neural networks can be fabricated by combining an optical transversal filter based on tapped fiber optic delay lines and an analog electronic feedback. Utilizing the high speed of optical device and parallel interconnection properties of light these optoelectronic adaptive systems can be faster and more efficient. The learning rates and accuracy of such optical adaptive filters are affected by optical round-off errors and the speed of operation of the spatial light modulator and electronic components. By analyzing the sensitivity of optical adaptive filters, a set of design guidelines is derived.
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Lloyd-Hart, M., and T. Rhoadarmer. "Wavefront Control Optimization using Adaptive Filters." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athc.14.

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A key component in any close-loop adaptive optics (AO) system is the algorithm which converts signals from the wavefront sensor into commands to the correcting element. Much work has recently been devoted to the study of the optimal implementation of this algorithm, both in the theoretical [1, 2] and practical [3] domains.
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Katz, Alex, and Simon Katz. "Multichannel adaptive filters." In SEG Technical Program Expanded Abstracts 1990. Society of Exploration Geophysicists, 1990. http://dx.doi.org/10.1190/1.1890100.

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"4 - Adaptive Filters." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628571.

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Gong, Y. "Totally adaptive filters." In Irish Signals and Systems Conference 2004. IEE, 2004. http://dx.doi.org/10.1049/cp:20040544.

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Marchant, Ross. "Scale Adaptive Filters." In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2015. http://dx.doi.org/10.1109/dicta.2015.7371304.

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Freeman, William T., Edward H. Adelson, and Eero P. Simoncelli. "Steerable filters for image analysis." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.mjj4.

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We present a method to synthesize filters tuned for spatial frequency and orientation as linear combinations of a set of basis filters, allowing an efficient architecture for filtering and analyzing images with oriented filters. Using this technique, one can design filters which allow adaptive control of both the orientation and phase of a filter simply by changing the linear weightings in a sum of basis filters. The number of basis filters required depends on the polynomial order n of the filter, after modulation by a windowing function. Filter functions of even or odd parity require n + 1 basis filters; functions of mixed parity require 2n + 1 basis filters. Filters similar to those found in the visual cortex form a good basis set for image analysis. Steerable filters have application in the analysis of texture and local orientation; they can also be adapted for shape-from-shading analysis in a variant of Pentland's linear shape-from-shading scheme.
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Arenas-Garcia, Jeronimo, and Miguel Lazaro-Gredilla. "Tracking performance of adaptively biased adaptive filters." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947261.

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Reports on the topic "Adaptive Filters"

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Tadmor, Eitan, and Jared Tanner. Adaptive Filters for Piecewise Smooth Spectral Data. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada448329.

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Etter, Delores M. Analysis of Multirate Adaptive Filters in Subbands. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada310356.

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Parker, Robert, Scott Stanberry, and Raghu Rao. Generalized Variable-Precision Filters for Adaptive Computing Applications. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada402597.

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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Sun, Winston Y. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error. Office of Scientific and Technical Information (OSTI), April 1993. http://dx.doi.org/10.2172/10148667.

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Scharf, Louis. Reduced Rank Wiener Filters in Optimized Coordinates for Partially Adaptive Filtering in Passive and Active Sonar Arrays. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada424321.

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Huang, Yuchen. Adaptive Notch Filter. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6686.

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Rifkin, R., and J. J. Vaccaro. Comparison of Narrowband Adaptive Filter Technologies for GPS. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada458023.

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Blaich, Jonathan David, Daniel R. Kammler, Andrea Ambrosini, William C. Sweatt, Jason C. Verley, Edwin J. Heller, and William Graham Yelton. A tunable electrochromic fabry-perot filter for adaptive optics applications. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/895983.

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Li, Hua. Locally Connected Adaptive Gabor Filter for Real-Time Motion Compensation. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada285726.

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