Relevant bibliographies by topics / Minimum norm estimation

Academic literature on the topic 'Minimum norm estimation'

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Published: 14 April 2023

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Journal articles on the topic "Minimum norm estimation"

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Stein, Michael L. "Minimum Norm Quadratic Estimation of Spatial Variograms." Journal of the American Statistical Association 82, no. 399 (September 1987): 765–72. http://dx.doi.org/10.1080/01621459.1987.10478497.

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Kim, June Sic, Joo Man Han, Kwang Suk Park, and Chun Kee Chung. "Distribution-based minimum-norm estimation with multiple trials." Computers in Biology and Medicine 38, no. 11-12 (November 2008): 1203–10. http://dx.doi.org/10.1016/j.compbiomed.2008.09.004.

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Iwaki, S., and S. Ueno. "Minimum-Norm Source Estimation in MEG with Temporal Information." Journal of the Magnetics Society of Japan 22, no. 4_2 (1998): 773–76. http://dx.doi.org/10.3379/jmsjmag.22.773.

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Ermolaev, V. T., and A. B. Gershman. "Fast algorithm for minimum-norm direction-of-arrival estimation." IEEE Transactions on Signal Processing 42, no. 9 (1994): 2389–94. http://dx.doi.org/10.1109/78.317860.

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Xu, Peiliang. "The value of minimum norm estimation of geopotential fields." Geophysical Journal International 111, no. 1 (October 1992): 170–78. http://dx.doi.org/10.1111/j.1365-246x.1992.tb00563.x.

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Nov, Yuval, and Ori Davidov. "Minimum-norm estimation for a bi-exponential survival model." Statistics & Probability Letters 80, no. 7-8 (April 2010): 648–53. http://dx.doi.org/10.1016/j.spl.2009.12.022.

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Nenonen, J. T., M. S. Hämäläinen, and R. J. Iimoniemi. "Minimum-norm estimation in a boundary-element torso model." Medical & Biological Engineering & Computing 32, no. 1 (January 1994): 43–48. http://dx.doi.org/10.1007/bf02512477.

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Willis, A. J., R. de Mello Koch, and J. Nicolson. "Minimum norm target estimation algorithm for phased array radar." Electronics Letters 28, no. 4 (1992): 358. http://dx.doi.org/10.1049/el:19920224.

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Marshall, R. J., and K. V. Mardia. "Minimum norm quadratic estimation of components of spatial covariance." Journal of the International Association for Mathematical Geology 17, no. 5 (July 1985): 517–25. http://dx.doi.org/10.1007/bf01032106.

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Mardia, K. V. "Minimum norm quadratic estimation of components of spatial covariance." Mathematical Geology 18, no. 2 (February 1986): 267. http://dx.doi.org/10.1007/bf00898290.

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Dissertations / Theses on the topic "Minimum norm estimation"

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Colares, Ricardo Fialho. "Aprimorando metodo para detecção de frequencias baseado na norma-minima." [s.n.], 1997. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261954.

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Orientador: Amauri Lopes
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-07-22T21:47:16Z (GMT). No. of bitstreams: 1 Colares_RicardoFialho_M.pdf: 6743255 bytes, checksum: 80941de30b4a94d8e4a67978ab3eff8f (MD5) Previous issue date: 1997
Resumo: : Este trabalho aborda a aplicação da predição linear na análise de sinais discretos. Examinaremos a sua utilização no problema de estimação de freqüências de um sinal sujeito à ruído. O critério baseado nos mínimos-quadrados será utilizado para a obtenção do preditor ótimo nas formas forward, backward e forward-backward. A ferramenta de decomposição em valores singulares (DVS) será utilizada para a obtenção da solução de norma-mínima para esses preditores. Tal solução particular será detalhadamente estudada para investigar o comportamento dos zeros do polinômio associado ao filtro de erro de predição. A análise desenvolvida trouxe contribuições ao conhecimento dos métodos baseados na norma-mínima, as quais propiciaram a elaboração de uma proposta de um novo método para detecção de freqüências baseado no método FBLP Modificado. Os resultados de simulações obtidos para o método proposto mostram uma SNR de limiar inferior àquela obtida para o método FBLP Modificado. Os resultados também indicam que o novo método é capaz de estimar freqüências em SNR's inferiores àquelas obtidas mesmo para o método da Máxima Verossimilhança
Abstract: This work is concerned with the application of the linear prediction on the discrete signals analysis, aiming the frequency estimation at low signal to noise ratios. The linear prediction is optimized using least squares criterion and each of the following forms: forward, backward, forward-backward. For each one of this cases, a minimum norm solution is obtained through singular value decomposition and a polynomial is formed using the optimized coefficients. The study of the behavior of their zeroes led to new insight into the minimum norm based frequency detection methods, allowing the proposal of a new method, based on the Modified FBLP. The simulation results have shown that this new method is able to detect frequencies at lower signal to noise ratios, even when compared with the Maximum Likelihood method
Mestrado
Mestre em Engenharia Elétrica
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Baumgarten, Daniel [Verfasser]. "Computational and experimental investigations on the imaging of magnetic nanoparticles based on magnetorelaxometry and minimum norm estimations for biomedical applications / Daniel Baumgarten." Aachen : Shaker, 2011. http://d-nb.info/1070152749/34.

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Berro, Soumaya. "Identification of muscle activation schemes by inverse methods applied on HD-sEMG signals." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2708.

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L'identification rapide ou en temps réel de l'activation spatio-temporelle des unités motrices (UM) qui représentent les unités fonctionnelles du système neuromusculaire est fondamentale dans les applications de contrôle des prothèses et en réhabilitation fonctionnelle. Cependant, cette procédure demande un temps de calcul énorme. Par conséquent, le travail de cette thèse a été consacré à fournir un algorithme permettant l'identification en temps réel des stratégies d'activation spatiale et temporelle des UMs en appliquant des méthodes inverses sur les signaux HD-sEMG (électromyogramme de surface à haute densité) à partir d'une grille placée sur le Biceps Brachial (BB). À cette fin, nous proposons une approche innovante, qui implique l'utilisation de la méthode inverse classique de minimisation de norme et une interpolation de courbe en 3D, à savoir l'approche est nommée CFB-MNE. Cette méthode, fondée sur l'identification inverse (estimation de la norme minimale) couplée à un dictionnaire des potentiels d'action des unités motrices simulées (MUAP) d'un modèle récent et testée sur des simulations, a permis la localisation en temps réel des unités motrices individuelles simulées. Une analyse de robustesse (modifications anatomiques, physiologiques et instrumentales) a ensuite été effectuée pour vérifier l'efficacité de l'algorithme proposé. Enfin, l'algorithme proposé a été testé sur des UMs avec des schémas de recrutement réalistes donnant des résultats prometteurs et encourageants en identification spatiale et temporelle sur trois scenarios. Pour conclure, en perspectives, les résultats prometteurs obtenus suggèrent l'utilisation de l'apprentissage automatique et de l'intelligence artificielle (IA) pour améliorer encore les performances de l'algorithme proposé
Fast or real-time identification of the spatiotemporal activation of Motor Units (MUs), functional units of the neuromuscular system, is fundamental in applications as prosthetic control and rehabilitation guidance but often dictates expensive computational times. Therefore, the thesis work was devoted to providing an algorithm that enables the real-time identification of MU spatial and temporal activation strategies by applying inverse methods on HD-sEMG (high-density surface electromyogram) signals from a grid placed over the Biceps Brachii (BB). For this purpose, we propose an innovative approach, that involves the use of the classical minimum norm inverse method and a 3D fitting curve interpolation, namely CFB-MNE approach. This method, based on inverse identification (minimum norm estimation) coupled to simulated motor unit action potential (MUAP) dictionary from a recent model and tested on simulations, allowed the real time localization of simulated individual motor units. A robustness analysis (anatomical, physiological, and instrumental modifications) was then performed to verify the efficiency of the proposed algorithm. Finally, the proposed algorithm was tested on MUs with realistic recruitment patterns giving promising results in both spatial and temporal identification. To conclude, a door to future perspectives was opened, according to the obtained promising results, suggesting the use of machine learning and artificial intelligence (AI) to further boost the performance of the proposed algorithm
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Bertin, Karine. "Estimation asymptotiquement exacte en norme sup de fonctions multidimensionnelles." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2004. http://tel.archives-ouvertes.fr/tel-00008028.

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On étudie deux modèles statistiques: le modèle de régression à pas aléatoire et le modèle de bruit blanc gaussien. Dans ces modèles, le but est d'estimer en norme sup une fonction f inconnue, à partir des observations, en supposant que f appartient à une classe de Holder. Dans le modèle de régression, pour l'estimation d'une fonction unidimensionnelle, on obtient la constante exacte et un estimateur asymptotiquement exact. Dans le modèle de bruit blanc, on s'intéresse à l'estimation sur deux classes de fonctions multidimensionnelles anisotropes dont une est une classe additive. Pour ces deux classes, on détermine la constante exacte et un estimateur asymptotiquement exact et on met en évidence leur lien avec l'"optimal recovery". La dernière partie donne des résultats d'asymptotique exacte dans un cadre adaptatif dans le modèle de bruit blanc. On détermine la constante exacte adaptative et un estimateur asymptotiquement exact adaptatif pour l'estimation sur des classes anisotropes.
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Kahaei, Mohammad Hossein. "Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36044/7/36044_Digitised_Thesis.pdf.

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The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.
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Delpish, Ayesha Nneka Niu Xu-Feng. "A comparison of estimators in hierarchical linear modeling restricted maximum likelihood versus bootstrap via minimum norm quadratic unbiased estimators /." 2006. http://etd.lib.fsu.edu/theses/available/06262006-100559.

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Thesis (Ph. D.)--Florida State University, 2006.
Advisor: Xu-Feng Niu, Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Sept. 18, 2006). Document formatted into pages; contains ix, 116 pages. Includes bibliographical references.
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Books on the topic "Minimum norm estimation"

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Lau, Chun Kit Edwin. Minimum norm mutual coupling compensation with applications in matrix pencil direction of arrival estimation. Ottawa: National Library of Canada, 2003.

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service), SpringerLink (Online, ed. L1-Norm and L∞-Norm Estimation: An Introduction to the Least Absolute Residuals, the Minimax Absolute Residual and Related Fitting Procedures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Farebrother, Richard. L1-Norm and L-Norm Estimation: An Introduction to the Least Absolute Residuals, the Minimax Absolute Residual and Related Fitting Procedures. Springer, 2013.

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Book chapters on the topic "Minimum norm estimation"

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Monticelli, A. "Least-Squares and Minimum Norm Problems." In State Estimation in Electric Power Systems, 15–37. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4999-4_2.

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Namitha, Ch, V. Uma Mahesh, M. Anusha, S. Koteswara Rao, and T. Vaishnavi Chandra. "Frequency Estimation Using Minimum Norm Algorithm on Seismic Data." In Lecture Notes in Electrical Engineering, 153–63. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7329-8_16.

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Crowley, Christopher W., Richard E. Greenblatt, and Ismail Khalil. "Minimum Norm Estimation of Current Distributions in Realistic Geometries." In Advances in Biomagnetism, 603–6. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0581-1_133.

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Mousas, Abdou, Panagiotis G. Simos, Roozbeh Rezaie, and Andrew C. Papanicolaou. "Estimation of Regional Activation Maps and Interdependencies from Minimum Norm Estimates of Magnetoencephalography (MEG) Data." In Modern Electroencephalographic Assessment Techniques, 267–89. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/7657_2014_72.

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Pinto, B., A. C. Sousa, and C. Quintão. "Depth-Sensitive Algorithm to Localize Sources Using Minimum Norm Estimations." In IFMBE Proceedings, 1726–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_426.

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Conference papers on the topic "Minimum norm estimation"

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Cabrera, S. D., Jan-Ti Yang, and Chao-Hsin Chi. "Estimation of sinusoids by adaptive minimum norm extrapolation." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205541.

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Berro, Soumaya, Ahmad Diab, Mohamad Hajj-Hassan, Mohamad Khalil, Hassan Amoud, and Sofiane Boudaoud. "Identification of motor unit spatial activation by minimum norm estimation." In 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME). IEEE, 2019. http://dx.doi.org/10.1109/icabme47164.2019.8940273.

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Xiaohui Yu, Yaowu Shi, and Yan Ma. "A cross fourth order moment minimum norm method for chirp time delay estimation." In 2009 International Conference on Mechatronics and Automation (ICMA). IEEE, 2009. http://dx.doi.org/10.1109/icma.2009.5246690.

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Zhang, Na, Qianqian Kang, and Yong Hu. "Estimation of orientation distribution function using spherical ridgelet basis with minimum L2 norm." In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/bmei.2011.6098483.

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Berro, Soumaya, Ahmad Diab, Mohamad Hajj-Hassan, Mohamad Khalil, Hassan Amoud, and Sofiane Boudaoud. "Exploration of motor unit activation pattern using high density sEMG minimum norm estimation." In 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME). IEEE, 2020. http://dx.doi.org/10.1109/mecbme47393.2020.9265172.

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Migliore, M. D., B. Fuchs, L. le Coq, and S. Rondineau. "Estimation of the 5G Massive-MIMO Antenna Beams using Drones by Minimum Trace Norm Minimization." In 2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). IEEE, 2021. http://dx.doi.org/10.23919/ursigass51995.2021.9560455.

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Zhigang Su, Yingning Peng, and Renbiao Wu. "Minimum norm singular capon spectral estimator." In 2007 IEEE Antennas and Propagation International Symposium. IEEE, 2007. http://dx.doi.org/10.1109/aps.2007.4395700.

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Fiddy, M. A. "Quantum-limited image restoration." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ft2.

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Imaging at low-light levels relies on intensity measurements that have a signal-to-noise ratio of N , if N photons are detected at a pixel. Usually, a simple time integration to improve the SNR is not possible because the object being imaged or the measurement system is moving; this results in a loss of high spatial frequency information and possible blur. Examples of imaging techniques are described along with the usual techniques for dealing with the photon-limited data, poor SNR, and blur. Correlation based methods (recovery from Fourier magnitude or bispectral data) provide a solution to some of these problems, in principle, and are outlined. Spectral estimation techniques to restore high spatial frequencies or missing phase information can further improve these images but are very noise sensitive in general. New methods are described that are either explicitly or implicitly regularized and permit reliable image restoration; they are based on minimum norm estimators that can incorporate prior knowledge about the object, if available. Both linear and nonlinear estimators can be defined and the role of the prior knowledge is quite different in the two cases.
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Tu, Stephen, Ross Boczar, and Benjamin Recht. "Minimax Lower Bounds for H∞-Norm Estimation." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814311.

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BAUMGARTEN, DANIEL, and JENS HAUEISEN. "IMAGING OF MAGNETIC NANOPARTICLES BASED ON MAGNETORELAXATION AND MINIMUM NORM ESTIMATIONS." In Proceedings of the First International Workshop on Magnetic Particle Imaging. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814324687_0027.

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