Tesis sobre el tema "Linear prediction"
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Stevens, D. A. "Non-linear prediction for speech processing". Thesis, Swansea University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639110.
Texto completoPottorff, Robert Thomas. "Video Prediction with Invertible Linear Embeddings". BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7577.
Texto completoKalantzis, Eugenia. "Prediction of soil corrosivity using linear polarization". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq29604.pdf.
Texto completoAlbanis, George T. "Financial prediction using non linear classification techniques". Thesis, City University London, 2001. http://openaccess.city.ac.uk/8289/.
Texto completoZhang, Lei. "Code excited linear prediction with multi-pulse codebooks". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24279.pdf.
Texto completoIslam, Tamanna. "Interpolation of linear prediction coefficients for speech coding". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0034/MQ64229.pdf.
Texto completoHawkes, Richard Nathanael. "Linear state models for volatility estimation and prediction". Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/7138.
Texto completoFreij, G. J. "Enhanced sequential adaptive linear prediction for speech encoding". Thesis, University of Liverpool, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356268.
Texto completoEatwell, Karen Anne. "Remediation of instability in Best Linear Unbiased Prediction". Thesis, University of Pretoria, 2013. http://hdl.handle.net/2263/40245.
Texto completoThesis (PhD)--University of Pretoria, 2013.
gm2014
Genetics
unrestricted
Mahmood, Arshad. "Rainfall prediction in Australia : Clusterwise linear regression approach". Thesis, Federation University Australia, 2017. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251.
Texto completoDoctor of Philosophy
Sundberg, Jesper. "Anomaly Detection in Diagnostics Data with Natural Fluctuations". Thesis, KTH, Optimeringslära och systemteori, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170237.
Texto completoI den här rapporten kommer det glödheta området anomalidetektering studeras, vilket tillhör ämnet Machine Learning. Företaget där arbetet utfördes på heter Procera Networks och jobbar med IT-lösningar inom bredband till andra företag. Procera önskar att kunna upptäcka fel hos kunderna i dessa system automatiskt. I det här projektet kommer olika metoder för att hitta intressanta företeelser i datatraffiken att genomföras och forskas kring. De mest intressanta företeelserna är framfärallt snabba avvikelser (avvikande punkt) och färändringar äver tid (trender) men också andra oväntade mänster. Tre modeller har analyserats, nämligen Linear Predictive Coding, Sparse Linear Prediction och Wavelet Transform. Det slutgiltiga resultatet från modellerna är grundat på en speciell träskel som är skapad fär att ge ett så bra resultat som mäjligt till den undersäkta modellen..
Acton, Matthew Nicholas Frederick. "Response Prediction and Detection in Non-linear Clamped Panels". Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504772.
Texto completoWoodard, Jason Paul. "Digital coding of speech using Code Excited Linear Prediction". Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387898.
Texto completoOkumu, Emmanuel Latim. "Non-linear prediction in the presence of macroeconomic regimes". Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297222.
Texto completoAsterios, Geroukis. "Prediction of Linear Models: Application of Jackknife Model Averaging". Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297671.
Texto completoda, Costa Joel. "Online Non-linear Prediction of Financial Time Series Patterns". Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32221.
Texto completoOuzienko, Vladimir. "Log Linear Models for Prediction and Analysis of Networks". Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214763.
Texto completoPh.D.
The heightened research activity in the interdisciplinary field of network science can be attributed to the emergence of the social network computer applications. Researchers understood early on that data describing how entities interconnect is highly valuable and that it offers a deeper understanding about the entities themselves. This is why there were so many studies done about various kinds of networks in the last 10-15 years. The study of the networks from the perspective of computer science usually has two objectives. The first objective is to develop statistical mechanisms capable of accurately describing and modeling observed real-world networks. A good fit of such mechanism suggests the correctness of the model's assumptions and leads to better understanding of the network. A second goal is more practical, a well performing model can be used to predict what will happen to the network in the future. Also, such model can be leveraged to use the information gleaned from network to predict what will happen to the networks entities. One important leitmotif of network research and analysis is wide adaptation of log linear models. In this work we apply this philosophy for study and evaluation of log-linear statistical models in various types of networks. We begin with proposal of the new Temporal Exponential Random Graph Model (tERGM) for the analysis and predictions in the binary temporal social networks. We then extended the model for applications in partially observed networks that change over time. Lastly, we generalize the tERGM model to predict the real-valued weighted links in the temporal non-social networks. The log-linear models are not limited to networks that change over time but can also be applied to networks that are static. One such static network is a social network composed of patients undergoing hemodialysis. Hemodialysis is prescribed to people suffering from the end stage renal disease; the treatment necessitates the attendance, on non-changing schedule, of the hemodialysis clinic for a prolonged time period and this is how the social ties are formed. The new log-linear Social Latent Vectors (SLV) model was applied to study such static social networks. The results obtained from SLV experiments suggest that social relationships formed by patients bear influence on individual patients clinical outcome. The study demonstrates how social network analysis can be applied to better understand the network constituents.
Temple University--Theses
Koestoer, Nanda Prasetiyo y npkoestoer@yahoo com au. "Robust Linear Prediction Analysis for Low Bit-Rate Speech Coding". Griffith University. School of Microelectronic Engineering, 2002. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030407.142552.
Texto completoKoestoer, Nanda Prasetiyo. "Robust Linear Prediction Analysis for Low Bit-Rate Speech Coding". Thesis, Griffith University, 2002. http://hdl.handle.net/10072/366614.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
Full Text
Bhattacharjee, Sushanta Kumar. "Influence of variables in Bayesian prediction". Thesis, University of Sheffield, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360463.
Texto completoSchwerk, Thomas. "NELION: a non-linear stock prediction and portfolio management system". [S.l. : s.n.], 2001. http://www.diss.fu-berlin.de/2001/85/index.html.
Texto completoPawate, Basavaraj I. "TMS 320 based dual tone multifrequency receiver using linear prediction". Thesis, University of Ottawa (Canada), 1985. http://hdl.handle.net/10393/4656.
Texto completoAdistambha, Kevin. "Embedded lossless audio coding using linear prediction and cascade coding". Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060724.122433/index.html.
Texto completoIakymchuk, Roman [Verfasser]. "Performance modeling and prediction for linear algebra algorithms / Roman Iakymchuk". Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1026308690/34.
Texto completoDas, Subhro. "Distributed Linear Filtering and Prediction of Time-varying Random Fields". Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/765.
Texto completoIrving, Benjamin. "Radiation dose measurement and prediction for linear slit scanning radiography". Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/3251.
Texto completoIncludes bibliographical references (leaves 112-117).
This study describes dose measurements made for linear slit scanning radiography (LSSR) and a dose prediction model that was developed for LSSR. The measurement and calculation methods used for determining entrance dose and effective dose (E) in conventional X-ray imaging systems were verified for use with LSSR. Entrance dose and E were obtained for LSSR and compared to dose measurements on conventional radiography units. Entrance dose measurements were made using an ionisation chamber and dosemeter; E was calculated from these entrance dose measurements using a Monte Carlo simulator. Comparisons with data from around the world showed that for most examinations the doses obtained for LSSR were considerably lower than those of conventional radiography units for the same image quality. Reasons for the low dose obtained with LSSR include scatter reduction and the beam geometry of LSSR. These results have been published as two papers in international peer reviewed journals. A new method to calculate entrance dose and effective dose for LSSR is described in the second part of this report. This method generates the energy spectrum for a particular set of technique factors, simulates a filter through which the beam is attenuated and then calculates entrance dose directly from this energy spectrum. The energy spectrum is then combined with previously generated organ energy absorption data for a standard sized patient to calculate effective dose to a standard sized patient.Energy imparted for different patient thicknesses can then be used to adjust the effective dose to a patient of any size. This method is performed for a large number of slit beams moving across the body in order to more effectively simulate LSSR. This also allows examinations with technique factors that vary for different parts of the anatomy to be simulated. This method was tested against measured data and Monte Carlo simulations. This model was shown to be accurate, while being specifically suited to LSSR and being considerably faster than Monte Carlo simulations.
Li, Huilin. "Small area estimation an empirical best linear unbiased prediction approach /". College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7600.
Texto completoThesis research directed by: Mathematical Statistics Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Erives, Anchondo Ruben. "Validation of non-linear time marching and time-linearised CFD solvers used for flutter prediction". Thesis, KTH, Kraft- och värmeteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175542.
Texto completoKinnas, Spyridon Athanasios. "Non-linear corrections to the linear theory for the prediction of the cavitating flow around hydrofoils". Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/15257.
Texto completoMICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Bibliography: leaves 116-120.
by Spyridon Athanasios Kinnas.
Ph.D.
Oleksandra, Shovkun. "Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors". Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34398.
Texto completoAl-Naimi, Khaldoon Taha. "Advanced speech processing and coding techniques". Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843488/.
Texto completoHaywood, John. "A frequency domain investigation of model based prediction". Thesis, Lancaster University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386424.
Texto completoRahman, Azizur. "Bayesian prediction distributions for some linear models under student-t errors". University of Southern Queensland, Faculty of Sciences, 2007. http://eprints.usq.edu.au/archive/00003581/.
Texto completoQin, Qin. "Linear Prediction Approach for Blind Multiuser Detection in Multicarrier CDMA Systems". University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1034355893.
Texto completoRaval, Kunal M. "Linear prediction models for landmine detection using handheld ground penetrating radar /". free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426099.
Texto completoTrabelsi, Chokri. "RS codes and linear prediction techniques for land mobile satellite channels". Thesis, University of Ottawa (Canada), 1990. http://hdl.handle.net/10393/5763.
Texto completoKhan, Naeem. "Linear prediction approaches to compensation of missing measurement in Kalman filtering". Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10122.
Texto completoJukić, Ante [Verfasser]. "Sparse Multi-Channel Linear Prediction for Blind Speech Dereverberation / Ante Jukić". München : Verlag Dr. Hut, 2017. http://d-nb.info/1149580399/34.
Texto completoAl-Hitmi, Mohammed Abdulla E. "Non-linear data analysis and neural networks for time series prediction". Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370084.
Texto completoLawless, Amos S. "Development of linear models for data assimilation in numerical weather prediction". Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365423.
Texto completoPhadnis, Akash. "Uncertainty quantification and prediction for non-autonomous linear and nonlinear systems". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85476.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 189-197).
The science of uncertainty quantification has gained a lot of attention over recent years. This is because models of real processes always contain some elements of uncertainty, and also because real systems can be better described using stochastic components. Stochastic models can therefore be utilized to provide a most informative prediction of possible future states of the system. In light of the multiple scales, nonlinearities and uncertainties in ocean dynamics, stochastic models can be most useful to describe ocean systems. Uncertainty quantification schemes developed in recent years include order reduction methods (e.g. proper orthogonal decomposition (POD)), error subspace statistical estimation (ESSE), polynomial chaos (PC) schemes and dynamically orthogonal (DO) field equations. In this thesis, we focus our attention on DO and various PC schemes for quantifying and predicting uncertainty in systems with external stochastic forcing. We develop and implement these schemes in a generic stochastic solver for a class of non-autonomous linear and nonlinear dynamical systems. This class of systems encapsulates most systems encountered in classic nonlinear dynamics and ocean modeling, including flows modeled by Navier-Stokes equations. We first study systems with uncertainty in input parameters (e.g. stochastic decay models and Kraichnan-Orszag system) and then with external stochastic forcing (autonomous and non-autonomous self-engineered nonlinear systems). For time-integration of system dynamics, stochastic numerical schemes of varied order are employed and compared. Using our generic stochastic solver, the Monte Carlo, DO and polynomial chaos schemes are inter-compared in terms of accuracy of solution and computational cost. To allow accurate time-integration of uncertainty due to external stochastic forcing, we also derive two novel PC schemes, namely, the reduced space KLgPC scheme and the modified TDgPC (MTDgPC) scheme. We utilize a set of numerical examples to show that the two new PC schemes and the DO scheme can integrate both additive and multiplicative stochastic forcing over significant time intervals. For the final example, we consider shallow water ocean surface waves and the modeling of these waves by deterministic dynamics and stochastic forcing components. Specifically, we time-integrate the Korteweg-de Vries (KdV) equation with external stochastic forcing, comparing the performance of the DO and Monte Carlo schemes. We find that the DO scheme is computationally efficient to integrate uncertainty in such systems with external stochastic forcing.
by Akash Phadnis.
S.M.
Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction". Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.
Texto completoPeters, Richard Alan II. "A LINEAR PREDICTION CODING MODEL OF SPEECH (SYNTHESIS, LPC, COMPUTER, ELECTRONIC)". Thesis, The University of Arizona, 1985. http://hdl.handle.net/10150/291240.
Texto completoSapankevych, Nicholas. "Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction". Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5569.
Texto completoChen, Xinyu. "Inference in Constrained Linear Regression". Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/405.
Texto completoTaga, Marcel Frederico de Lima. "Regressão linear com medidas censuradas". Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05122008-005901/.
Texto completoWe consider a simple linear regression model in which both variables are interval censored. To motivate the problem we use data from an audiometric study designed to evaluate the possibility of prediction of behavioral thresholds from physiological thresholds. We develop prediction intervals for the response variable, obtain the maximum likelihood estimators of the proposed model and compare their performance with that of estimators obtained under ordinary linear regression models.
Wall, Ian. "New simulation methods for the prediction of binding free energies". Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313217.
Texto completoMarshall, John Graham. "Prediction of turbomachinery aeroelasticity effects using a 3D non-linear integrated method". Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244501.
Texto completoBasha, Elizabeth (Elizabeth Ann). "In-situ prediction on sensor networks using distributed multiple linear regression models". Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60096.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 199-208).
Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation where complex models are replaced by in-situ sensing and communication. These prediction models utilize low-computation, low-communication, and distributed algorithms suited to autonomous operation and multiple applications. We achieve this through development of new algorithms that enable distributed computation of the pseudo inverse of a matrix on a sensor network, thereby enabling a wide range of prediction methods. We apply these models to three different application areas: (1) river flooding for early warning, (2) solar recharging current for power management, and (3) job congestion prediction on multi-function device networks for achieving quality of service. Additionally, we use these applications to explore other aspects of sensor networks: river flooding to design a predictive environmental monitoring sensor network, solar current to develop a dynamic version of the model for better fault tolerance, and job congestion to explore modeling multi-function device networks. For each, we comprehensively tested the full solutions. We implemented the river flood prediction and solar current prediction solutions on two different sensor network platforms with full field deployments; we had a final test of over 5 weeks operation for both. Overall, we achieve the following contributions: (1) distributed algorithms for computing a matrix pseudoinverse and multiple linear regression model on a sensor network, (2) three applications of these algorithms with associated field experiments demonstrating their versatility, (3) a sensor network architecture and implementation for river flood prediction as well as other applications requiring real-time data and a low node count to geographic area ratio, and (4) a MFD simulator predicting and resolving congestion.
by Elizabeth Ann Basha.
Ph.D.
Yap, Xiu Huan. "Multi-label classification on locally-linear data: Application to chemical toxicity prediction". Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright162901936395651.
Texto completo