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Monday, July 13, 2020 | History

2 edition of Nonstationary lattice-filter modeling found in the catalog.

Nonstationary lattice-filter modeling

Hanoch Lev-Ari

Nonstationary lattice-filter modeling

a dissertation submitted to the Department of Electrical Engineering and the Committee on Graduate Studies of Stanford University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

by Hanoch Lev-Ari

  • 139 Want to read
  • 32 Currently reading

Published by Department of Electrical Engineering, Stanford University in Stanford, Calif .
Written in English


Edition Notes

Thesis (Ph.D.) - Stanford University, 1983.

Statementby Hanoch Lev-Ari.
ID Numbers
Open LibraryOL13902052M

  () Synthesis of Long-Period Fiber Gratings With the Inverted Erbium Gain Spectrum Using the Multiport Lattice Filter Model. Journal of Lightwave Technology , () Inverse scattering algorithm for reconstructing lossy fiber Bragg gratings. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more.

The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over figures and illustrations. Wide range of filtering techniques presented to address various applications mathematical expressions and equations Practical questions, . Computer Modeling Of Optical Trackers. R. C. Anderson, P. R. Callary. Proc. SPIE , Digital Processing of Aerial Images, pg (4 September ); doi: / Read Abstract + DOWNLOAD PDF SAVE TO MY LIBRARY.

A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG. Time-Frequency Distribution and Nonstationary Signals. Transient detection in the presence of strong narrowband interference via Wigner-Ville spectrum. Nenad M. Marinovic, Leonid M. Roytman. Proc. SPIE , Advanced Signal Processing Algorithms, Architectures, and Implementations, pg (1 November ); doi: /


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Nonstationary lattice-filter modeling by Hanoch Lev-Ari Download PDF EPUB FB2

The focus of study includes nonlinear and nonstationary time series estimation, forecasting and changepoint modeling, nonlinear signal processing in econometrics and financial time series. Lev-Ari and T. Kailath, ``Lattice-Filter Parametrization and Modeling of Nonstationary Processes,'' IEEE Transactions on Information Theory, Vol.

IT, pp.January H. Lev-Ari, T. Kailath and J. Cioffi, ``Least-Squares Adaptive Lattice and Transversal Filters: A Unified Geometric Theory,'' IEEE Transactions on Information Theory. Niedźwiecki, M., Meller, M., Chojnacki, D.: Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation.

In: Proceedings 56th IEEE Conference on Decision and Control, Melbourne, Australia, pp. – () Google ScholarAuthor: Maciej Niedźwiecki, Damian Chojnacki. The paper presents the modification of lattice RLS adaptive filtering algorithms for the case of nonstationary signal processing.

The modification includes the using of sliding window and dynamic. entitled "Nonstationary Lattice-Filter Modeling" and the thesis of J. Cioffi enti-tled "Fast Transversal Filters for Communications Applications". As stated in the introduction to Lev-Ari's thesis, for more than four decades, the notion of stationarity has dominated the theory and practice of numerous.

Chapter 3 on "Spectral Estimation with Applications" by S. Lawrence Maple, Jr. gives a summary of several modern spectral estimation methods. Most of the methods are explained in the context of time series modeling. A few methods involve /87/$ OElsevier Science Publishers B.V.

(North-Holland) Book Reviews nonparametric. Part of the Operator Theory: Advances and Applications book series (OT, volume 18) Abstract These include speech analysis and synthesis, inverse scattering, decoding of error-correcting codes, synthesis of digital filters, modeling of random signals, Padé approximation for.

Statistical Digital Signal Processing and Modeling Monson H. Hayes The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering.

Autoregressive Modeling of a Stationary Stochastic Process. Cholesky Factorization. Lattice Predictors. All-Pole, All-Pass Lattice Filter. Joint-Process Estimation. Predictive Modeling of Speech. Summary and Discussion. Problems.

Bibliography. S. KungefoZ. (), S. Kung et al. Time-Variant Ladders Instead of using a time-invariant (except for the need for switching in a new section at each time instant) lattice filter with vector reflection coefficients, we could use a filter with scalar but time-variant reflection coefficients (timeinvariant in the stationary case).

Autoregressive Modeling of a Stationary Stochastic Process Cholesky Factorization Lattice Predictors All-Pole, All-Pass Lattice Filter Joint-Process Estimation Predictive Modeling of Speech Summary and Discussion Problems Bibliography Chapter 4 Method of Steepest Descent.

() Lattice filter parameterization and modeling of nonstationary processes. IEEE Transactions on Information Theory() Transmutation and linear stochastic estimation. The emphasis of this book is on the theoretical aspects of (). An array pro cessor design methodology for hard real-time systems," ().

An illustration of a methodology for the construction of efficient systolic architectures in VLSI," Nonstationary lattice filter modeling. A Priori Error-Feedback Lattice Filter A Posteriori Error-Feedback Lattice Filter Normalized Lattice Filter Array Lattice Filter Summary of Main Results Bibliographic Notes Problems Computer Project A Modeling with Orthonormal Basis Functions Price: $ The papers (in three volumes) are organized under 7 themes, containing the following topics: 1.

Theory of Signals and Systems: a) Detection, b) Estimation, c) Filtering, d)Spectral estimation, e) Adaptive systems, f) Modeling, g) Digital transforms, h) Digital filtering. This banner text can have markup. web; books; video; audio; software; images; Toggle navigation. Abstract.

Stable autoregressive (AR) and autoregressive moving average (ARMA) processes belong to the class of stationary linear time series. A linear time series {} is Gaussian if the distribution of the independent innovations {ε(t)} is ng that Eε(t) = 0, some of the third‐order cumulants c xxx =Ex(t)x(t+m)x(t+n) will be non‐zero if the ε(t) are not normal and Eε 3 (t)≠O.

Shareable Link. Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. Silence suppression in speech synthesis systems is achieved by detecting and processing only segments of voice activity.

A segment is classified as "speech" if the energy of the signal is greater than an adaptively adjusted threshold.

The adaptively adjusted threshold is preferably defined as the maximum of scaled values of two separate envelope parameters, which both track the variation in. A Priori Error-Feedback Lattice Filter A Posteriori Error-Feedback Lattice Filter Normalized Lattice Filter Array Lattice Filter Summary of Main Results Bibliographic Notes Problems Computer Project A Modeling with Orthonormal Basis Functions.

Autoregressive Modeling of a Stationary Stochastic Process. Cholesky Factorization. Lattice Predictors. All-Pole, All-Pass Lattice Filter. Joint-Process Estimation. Predictive Modeling of Speech. Summary and Discussion. Problems. Bibliography. Chapter 4 Method of Steepest Descent. Basic Idea of the Steepest.The chapter begins with the derivation of the FIR lattice filter, and then proceeds to develop other lattice filter structures, which include the all-pole and allpass lattice filters, lattice filters that have both poles and zeros, and the split lattice filter.

Then, we look at lattice methods for all-pole signal modeling. Autoregressive Modeling of a Stationary Stochastic Process. Cholesky Factorization. Lattice Predictors. All-Pole, All-Pass Lattice Filter. Joint-Process Estimation. Predictive Modeling of Speech. Summary and Discussion. Problems.

Bibliography.