Dissertations / Theses on the topic 'Linear prediction'
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1
Stevens, D. A. "Non-linear prediction for speech processing." Thesis, Swansea University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639110.
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For over 20 years linear prediction has been one of the most widely used methods for analysing speech signals. Linear predictors have been used to model the vocal tract in all areas of speech processing from speech recognition to speech synthesis. However, Teager showed as early as 1980 by measuring the flow within the vocal tract during the pronunciation of a vowel sound, that the vocal tract is a non-linear system. As such the standard linear predictors are unable to model all the vocal tract information available in the speech signal. This work looks at replacing or complementing the standard linear models with non-linear ones in order to improve the modelling of the vocal tract. Several different methods of both generating and implementing non-linear models of the vocal tract are assessed to see how much improvement in prediction can be achieved by using non-linear models, either in place of, or complementing, the standard linear models. Two basic approaches to non-linear prediction have been used. The first of these is to configure a multi-layered perceptron (MLP) as a non-linear predictor and then to train the MLP to predict the speech signal. The second method is known as a split function approach as it effectively splits the overall predictor function into smaller sub-functions each of which requires a less complex predictor function than the whole. This second method uses a classification stage to determine what type of speech is present and then uses a separate predictor for each of the classifications. Initial results using a single MLP predictor proved ineffective, returning gains of 0.1 to 0.3 dB in excess of the standard LPC. This is thought to be due to an inability of the networks used to model the full dynamic complexity of the speech signal. However with the split function predictors it is shown that relatively high prediction gains can be achieved using a few simple sub-functions. With four linear sub-functions gains of 2.1 dB have been achieved over the standard LPC.
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Pottorff, Robert Thomas. "Video Prediction with Invertible Linear Embeddings." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7577.
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Using recently popularized invertible neural network We predict future video frames from complex dynamic scenes. Our invertible linear embedding (ILE) demonstrates successful learning, prediction and latent state inference. In contrast to other approaches, ILE does not use any explicit reconstruction loss or simplistic pixel-space assumptions. Instead, it leverages invertibility to optimize the likelihood of image sequences exactly, albeit indirectly.Experiments and comparisons against state of the art methods over synthetic and natural image sequences demonstrate the robustness of our approach, and a discussion of future work explores the opportunities our method might provide to other fields in which the accurate analysis and forecasting of non-linear dynamic systems is essential.
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Kalantzis, 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.
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Albanis, George T. "Financial prediction using non linear classification techniques." Thesis, City University London, 2001. http://openaccess.city.ac.uk/8289/.
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In this thesis, we explore the ability of statistical classification methods to predict financial events in the bond and stock markets. Our classification methods include conventional Linear Dicriminant Analysis (LDA), and a number of less familiar non-linear techniques such as Probabilistic Neural Network (PNN), Learning Vector Quanization (LVQ), Oblique Classifer (OCI), and Ripper Rule Induction (RRI).
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Zhang, 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.
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Islam, 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.
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Hawkes, Richard Nathanael. "Linear state models for volatility estimation and prediction." Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/7138.
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This thesis concerns the calibration and estimation of linear state models for forecasting stock return volatility. In the first two chapters I present aspects of financial modelling theory and practice that are of particular relevance to the theme of this present work. In addition to this I review the literature concerning these aspects with a particular emphasis on the area of dynamic volatility models. These chapters set the scene and lay the foundations for subsequent empirical work and are a contribution in themselves. The structure of the models employed in the application chapters 4,5 and 6 is the state-space structure, or alternatively the models are known as unobserved components models. In the literature these models have been applied in the estimation of volatility, both for high frequency and low frequency data. As opposed to what has been carried out in the literature I propose the use of these models with Gaussian components. I suggest the implementation of these for high frequency data for short and medium term forecasting. I then demonstrate the calibration of these models and compare medium term forecasting performance for different forecasting methods and model variations as well as that of GARCH and constant volatility models. I then introduce implied volatility measurements leading to two-state models and verify whether this derivative-based information improves forecasting performance. In chapter 6I compare different unobserved components models' specification and forecasting performance. The appendices contain the extensive workings of the parameter estimates' standard error calculations.
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Freij, 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.
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Eatwell, Karen Anne. "Remediation of instability in Best Linear Unbiased Prediction." Thesis, University of Pretoria, 2013. http://hdl.handle.net/2263/40245.
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In most breeding programmes breeders use phenotypic data obtained in breeding trials
to rank the performance of the parents or progeny on pre-selected performance criteria.
Through this ranking the best candidates are identified and selected for breeding or
production purposes. Best Linear Unbiased Prediction (BLUP), is an efficient selection
method to use, combining information into a single index. Unbalanced or messy data is
frequently found in tree breeding trial data. Trial individuals are related and a degree of
correlation is expected between individuals over sites, which can lead to collinearity in
the data which may lead to instability in certain selection models. A high degree of
collinearity may cause problems and adversely affect the prediction of the breeding
values in a BLUP selection index. Simulation studies have highlighted that instability is
a concern and needs to be investigated in experimental data. The occurrence of
instability, relating to collinearity, in BLUP of tree breeding data and possible methods
to deal with it were investigated in this study. Case study data from 39 forestry
breeding trials (three generations) of Eucalyptus grandis and 20 trials of Pinus patula
(two generations) were used. A series of BLUP predictions (rankings) using three
selection traits and 10 economic weighting sets were made. Backward and forward
prediction models with three different matrix inversion techniques (singular value
decomposition, Gaussian elimination - partial and full pivoting) and an adapted ridge
regression technique were used in calculating BLUP indices. A Delphi and Clipper
version of the same BLUP programme which run with different computational numerical precision were used and compared. Predicted breeding values (forward
prediction) were determined in the F1 and F2 E. grandis trials and F1 P. patula trials and
realised breeding performance (backward prediction) was determined in the F2 and F3 E.
grandis trials and F2 P. patula trials. The accuracy (correlation between the predicted
breeding values and realised breeding performance) was estimated in order to assess the
efficiency of the predictions and evaluate the different matrix inversion methods. The
magnitude of the accuracy (correlations) was found to mostly be of acceptable
magnitude when compared to the heritability of the compound weighted trait in the F1F2
E. grandis scenarios. Realised genetic gains were also calculated for each method used.
Instability was observed in both E. grandis and P. patula breeding data in the study, and
this may cause a significant loss in realised genetic gains. Instability can be identified by examining the matrix calculated from the product of the phenotypic covariance
matrix with its inverse, for deviations from the expected identity pattern. Results of this
study indicate that it may not always be optimal to use a higher numerical precision
programme when there is collinearity in the data and instability in the matrix
calculations. In some cases, where there is a large amount of collinearity, the use of a
higher precision programme for BLUP calculations can significantly increase or
decrease the accuracy of the rankings. The different matrix inversion techniques
particularly SVD and adapted ridge regression did not perform much better than the full
pivoting technique. The study found that it is beneficial to use the full pivoting
Gaussian elimination matrix inversion technique in preference to the partial pivoting
Gaussian elimination matrix inversion technique for both high and lower numerical
precision programmes.Thesis (PhD)--University of Pretoria, 2013.
gm2014
Genetics
unrestricted
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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.
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Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.Doctor of Philosophy
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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.
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In this thesis, the red hot topic anomaly detection is studied, which is a subtopic in machine learning. The company, Procera Networks, supports several broadband companies with IT-solutions and would like to detected errors in these systems automatically. This thesis investigates and devises methods and algorithms for detecting interesting events in diagnostics data. Events of interest include: short-term deviations (a deviating point), long-term deviations (a distinct trend) and other unexpected deviations. Three models are analyzed, namely Linear Predictive Coding, Sparse Linear Prediction and Wavelet Transformation. The final outcome is determined by the gap to certain thresholds. These thresholds are customized to fit the model as well as possible.I 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..
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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.
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Aircraft panels may often be subjected to high levels ofacoustic pressure loading in flight that may lead to fatigue damage. In some cases, the in-plane restraint provided by the panel boundaries will introduce a geometric stiffening non-linearity when the panel deforms. This Thesis is split into two main areas in the field of non-linear dynamics. Firstly the prediction ofthe non-linear response ofa structure to acoustic excitation for use in sonic fatigue life calculations is examined. The second area of consideration is the detection ofthe presence of non-linearities in a structure when there may more than one present. The Non-Linear Modal method (NLMOD) previously developed at the University of Manchester, allows the prediction ofthe kinematic and stress response of such panels via a reduced order approach that converts Finite Element results into a model based in linear modal space, with additional terms included to represent the non-linear behavior. In this Thesis, the development ofan experimental clamped panel structure is presented, together with several Finite Element models ofthe same structure. The panel is tested for its modal and static characteristics and exposed to fairly high-level acoustic excitation in a Progressive Wave Tube (PWT). The resulting non-linear strain responses are compared to those predicted using the identified non-linear modal model. Discrepancies are initially found between test and prediction. Reasons for the discrepancies are discussed and the model modified accordingly. Good final agreement with test results is found and a number of areas for further investigation are highlighted. The second part of the Thesis develops a novel, simple method for detecting the presence of non-linear stiffness and non-linear damping, when both may be present, using higher order statistics ofthe time domain response ofa structure. The method is developed using simulation of a single degree offreedom system and is then validated using a clamped panel. In order to ensure the presence of non-linear damping, a novel discrete, non-linear damper is developed making use of a passive shaker with the terminals shorted.
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Woodard, 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.
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Okumu, 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.
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This paper studies the predictive performance and in-sample dynamics of three regime switching models for Swedish macroeconomic time series. The models discussed are threshold autoregressive (TAR), Markov switching autoregressive (MSM-AR), and smooth-transition autoregressive (STAR) regime switching models. We perform recursive out-of-sample forecasting to study the predictive performance of the models. We also assess the in-sample dynamics correspondence to the forecast performance and find that there is not always a relationship. Furthermore, we seek to explore if these unrestricted models yield interpretable results regarding the regimes from an macroeconomic standpoint. We assess GDP-growth, the unemployment rate, and government bond yields and find evidence of Teräsvirta's claims that even when the data has non-linear dynamics, non-linear models might not improve the forecast performance of linear models when the forecast window is linear.
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Asterios, 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.
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When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesian Information Criterion and Mallows’s Cp. The results show that the Jackknife Model Averaging technique gives less prediction errors compared to the other methods of model prediction. This study concludes that the Jackknife Model Averaging technique might be a useful choice when predicting data.
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da, Costa Joel. "Online Non-linear Prediction of Financial Time Series Patterns." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32221.
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We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learning, and then both batch and online supervised learning are carried out on Feedforward Neural Networks (FNNs) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with restricted Boltzmann machine pretraining, and variance based initializations. The validity of the FNN backtest results are shown under a rigorous assessment of backtest overfitting using both Combinatorially Symmetrical Cross Validation and Probabilistic and Deflated Sharpe Ratios. Results are further used to develop a view on the phenomenology of financial markets and the value of complex historical data under unstable dynamics.
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Ouzienko, 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.
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Computer and Information SciencePh.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
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Koestoer, Nanda Prasetiyo, and 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.
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Speech coding is a very important area of research in digital signal processing. It is a fundamental element of digital communications and has progressed at a fast pace in parallel to the increase of demands in telecommunication services and capabilities. Most of the speech coders reported in the literature are based on linear prediction (LP) analysis. Code Excited Linear Predictive (CELP) coder is a typical and popular example of this class of coders. This coder performs LP analysis of speech for extracting LP coefficients and employs an analysis-by-synthesis procedure to search a stochastic codebook to compute the excitation signal. The method used for performing LP analysis plays an important role in the design of a CELP coder. The autocorrelation method is conventionally used for LP analysis. Though this works reasonably well for noise-free (clean) speech, its performance goes down when signal is corrupted by noise. Spectral analysis of speech signals in noisy environments is an aspect of speech coding that deserves more attention. This dissertation studies the application of recently proposed robust LP analysis methods for estimating the power spectrum envelope of speech signals. These methods are the moving average, moving maximum and average threshold methods. The proposed methods will be compared to the more commonly used methods of LP analysis, such as the conventional autocorrelation method and the Spectral Envelope Estimation Vocoder (SEEVOC) method. The Linear Predictive Coding (LPC) spectrum calculated from these proposed methods are shown to be more robust. These methods work as well as the conventional methods when the speech signal is clean or has high signal-to-noise ratio. Also, these robust methods give less quantisation distortion than the conventional methods. The application of these robust methods for speech compression using the CELP coder provides better speech quality when compared to the conventional LP analysis methods.
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Koestoer, Nanda Prasetiyo. "Robust Linear Prediction Analysis for Low Bit-Rate Speech Coding." Thesis, Griffith University, 2002. http://hdl.handle.net/10072/366614.
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Speech coding is a very important area of research in digital signal processing. It is a fundamental element of digital communications and has progressed at a fast pace in parallel to the increase of demands in telecommunication services and capabilities. Most of the speech coders reported in the literature are based on linear prediction (LP) analysis. Code Excited Linear Predictive (CELP) coder is a typical and popular example of this class of coders. This coder performs LP analysis of speech for extracting LP coefficients and employs an analysis-by-synthesis procedure to search a stochastic codebook to compute the excitation signal. The method used for performing LP analysis plays an important role in the design of a CELP coder. The autocorrelation method is conventionally used for LP analysis. Though this works reasonably well for noise-free (clean) speech, its performance goes down when signal is corrupted by noise. Spectral analysis of speech signals in noisy environments is an aspect of speech coding that deserves more attention. This dissertation studies the application of recently proposed robust LP analysis methods for estimating the power spectrum envelope of speech signals. These methods are the moving average, moving maximum and average threshold methods. The proposed methods will be compared to the more commonly used methods of LP analysis, such as the conventional autocorrelation method and the Spectral Envelope Estimation Vocoder (SEEVOC) method. The Linear Predictive Coding (LPC) spectrum calculated from these proposed methods are shown to be more robust. These methods work as well as the conventional methods when the speech signal is clean or has high signal-to-noise ratio. Also, these robust methods give less quantisation distortion than the conventional methods. The application of these robust methods for speech compression using the CELP coder provides better speech quality when compared to the conventional LP analysis methods.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
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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.
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Schwerk, 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.
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Pawate, Basavaraj I. "TMS 320 based dual tone multifrequency receiver using linear prediction." Thesis, University of Ottawa (Canada), 1985. http://hdl.handle.net/10393/4656.
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Adistambha, 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.
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Iakymchuk, 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.
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Das, Subhro. "Distributed Linear Filtering and Prediction of Time-varying Random Fields." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/765.
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We study distributed estimation of dynamic random fields observed by a sparsely connected network of agents/sensors. The sensors are inexpensive, low power, and they communicate locally and perform computation tasks. In the era of large-scale systems and big data, distributed estimators, yielding robust and reliable field estimates, are capable of significantly reducing the large computation and communication load required by centralized estimators, by running local parallel inference algorithms. The distributed estimators have applications in estimation, for example, of temperature, rainfall or wind-speed over a large geographical area; dynamic states of a power grid; location of a group of cooperating vehicles; or beliefs in social networks. The thesis develops distributed estimators where each sensor reconstructs the estimate of the entire field. Since the local estimators have direct access to only local innovations, local observations or a local state, the agents need a consensus-type step to construct locally an estimate of their global versions. This is akin to what we refer to as distributed dynamic averaging. Dynamic averaged quantities, which we call pseudo-quantities, are then used by the distributed local estimators to yield at each sensor an estimate of the whole field. Using terminology from the literature, we refer to the distributed estimators presented in this thesis as Consensus+Innovations-type Kalman filters. We propose three distinct types of distributed estimators according to the quantity that is dynamically averaged: (1) Pseudo-Innovations Kalman Filter (PIKF), (2) Distributed Information Kalman Filter (DIKF), and (3) Consensus+Innovations Kalman Filter (CIKF). The thesis proves that under minimal assumptions the distributed estimators, PIKF, DIKF and CIKF converge to unbiased and bounded mean-squared error (MSE) distributed estimates of the field. These distributed algorithms exhibit a Network Tracking Capacity (NTC) behavior – the MSE is bounded if the degree of instability of the field dynamics is below a threshold. We derive the threshold for each of the filters. The thesis establishes trade-offs between these three distributed estimators. The NTC of the PIKF depends on the network connectivity only, while the NTC of the DIKF and of the CIKF depend also on the observation models. On the other hand, when all the three estimators converge, numerical simulations show that the DIKF improves 2dB over the PIKF. Since the DIKF uses scalar gains, it is simpler to implement than the CIKF. Of the three estimators, the CIKF provides the best MSE performance using optimized gain matrices, yielding an improvement of 3dB over the DIKF. Keywords: Kalman filter, distributed state estimation, multi-agent networks, sensor networks, distributed algorithms, consensus, innovation, asymptotic convergence, mean-squared error, dynamic averaging, Riccati equation, Lyapunov iterations, distributed signal processing, random dynamical systems.
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Irving, 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.
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Includes abstract.Includes 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.
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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.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.Thesis 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.
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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.
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The turbomachinery related industry relies heavily on numerical tools for the design and development of modern turbomachines. In order to be competitive turbomachines ought to be highly efficient and robust. This has lead engineers to develop more aggressive designs, which often leads to lower margins of structural reliability. One of the strongest threats to turbomachines are high cycle fatigue problems which arise from aeroelastic phenomena such as flutter. According to Kielb R. (2013) many of such problems are detected at developing testing stage. This implies that the prediction capabilities for aeroelastic phenomena are in need of further development and/or tuning. This is especially evident for unsteady flow phenomena at transonic regimes. A very important step for the improvement of unsteady aerodynamic solvers is the validation and comparison of such solvers. The present thesis concerns with the validation and comparison of a non-linear time marching (ANSYS CFX) and the GKN’s in-house linearised solvers used for flutter analysis. The former has recently implemented a new feature called Transient Blade Row TBR, which drastically reduces the simulation domain to a maximum of two blades. In order to be included in the deign process, such tool need to be validated. In the same way, the recently launched in-house code LINNEA needs to be validated in order to be considered as a design tool. Experimental data from the aeroelastic standard configuration 4, and the FUTURE project were used for the validation purposes. The validation process showed that the solvers agreed very well between them for the standard configuration. Such agreement was less clear for the FUTURE compressor; nonetheless, the solutions still sit within the bulk of solutions provided from the different FUTURE partners. The validation showed that these tools provide with similar results as the state of the art tools from different companies. This indicates that they can be used in the design process. At the same time it was observed that there is room for improvement in the solvers, as these still present some considerable differences with the experimental results.
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Kinnas, 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.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1985.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Bibliography: leaves 116-120.
by Spyridon Athanasios Kinnas.
Ph.D.
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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.
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Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion.
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Al-Naimi, Khaldoon Taha. "Advanced speech processing and coding techniques." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843488/.
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Over the past two decades there has been substantial growth in speech communications and new speech related applications. Bandwidth constraints led researchers to investigate ways of compressing speech signals whilst maintaining speech quality and intelligibility so as to increase the possible number of customers for the given bandwidth. Because of this a variety of speech coding techniques have been proposed over this period. At the heart of any proposed speech coding method is quantisation of the speech production model parameters that need to be transmitted to the decoder. Quantisation is a controlling factor for the targeted bit rates and for meeting quality requirements. The objectives of the research presented in this thesis are twofold. The first enabling the development of a very low bit rate speech coder which maintains quality and intelligibility. This includes increasing the robustness to various operating conditions as well as enhancing the estimation and improving the quantisation of speech model parameters. The second objective is to provide a method for enhancing the performance of an existing speech related application. The first objective is tackled with the aid of three techniques. Firstly, various novel estimation techniques are proposed which are such that the resultant estimated speech production model parameters have less redundant information and are highly correlated. This leads to easier quantisation (due to higher correlation) and therefore to bit saving. The second approach is to make use of the joint effect of the quantisation of spectral parameters (i.e. LSF and spectral amplitudes) for their big impact on the overall bit allocation required. Work towards the first objective also includes a third technique which enhances the estimation of a speech model parameter (i.e. the pitch) through a robust statistics-based post-processing (or tracking) method which operates in noise contaminated environments. Work towards the second objective focuses on an application where speech plays an important role, namely echo-canceller and noise-suppressor systems. A novel echo-canceller method is proposed which resolves most of the weaknesses present in existing echo-canceller systems and improves the system performance.
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Haywood, John. "A frequency domain investigation of model based prediction." Thesis, Lancaster University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386424.
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Rahman, 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/.
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[Abstract]: This thesis investigates the prediction distributions of future response(s), conditional on a set of realized responses for some linear models havingstudent-t error distributions by the Bayesian approach under the uniform priors. The models considered in the thesis are the multiple regression modelwith multivariate-t errors and the multivariate simple as well as multiple re-gression models with matrix-T errors. For the multiple regression model, results reveal that the prediction distribution of a single future response anda set of future responses are a univariate and multivariate Student-t distributions respectively with appropriate location, scale and shape parameters.The shape parameter of these prediction distributions depend on the size of the realized responses vector and the dimension of the regression parameters' vector, but do not depend on the degrees of freedom of the error distribu-tion. In the multivariate case, the distribution of a future responses matrix from the future model, conditional on observed responses matrix from the realized model for both the multivariate simple and multiple regression mod-els is matrix-T distribution with appropriate location matrix, scale factors and shape parameter. The results for both of these models indicate that prediction distributions depend on the realized responses only through the sample regression matrix and the sample residual sum of squares and products matrix. The prediction distribution also depends on the design matricesof the realized as well as future models. The shape parameter of the prediction distribution of the future responses matrix depends on size of the realized sample and the number of regression parameters of the multivariatemodel. Furthermore, the prediction distributions are derived by the Bayesian method as multivariate-t and matrix-T are identical to those obtained under normal errors' distribution by the di®erent statistical methods such as the classical, structural distribution and structural relations of the model approaches. This indicates not only the inference robustness with respect todepartures from normal error to Student-t error distributions, but also indicates that the Bayesian approach with a uniform prior is competitive withother statistical methods in the derivation of prediction distribution.
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Qin, 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.
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Raval, 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.
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Trabelsi, Chokri. "RS codes and linear prediction techniques for land mobile satellite channels." Thesis, University of Ottawa (Canada), 1990. http://hdl.handle.net/10393/5763.
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Reed-Solomon codes as nonbinary block codes with multiple burst-error-correcting capability are well suited for error control in fading channels. This thesis studies the effectiveness of Reed Solomon codes and provides a complete characterization of these codes for a class of land mobile satellite communication channels. Earlier studies on the performance of RS codes in mobile satellite channels did not consider shadowing. Furthermore, they considered only the slow fading case. In our simulation the effect of shadowing is taken into consideration and a fast fading assumption is used with system fading bandwidths of 1 to 10 percent of the transmission rate. This range of normalized fading bandwidths corresponds to vehicle speeds from 15 to 60 mph at bit rate of 2400 bps and 4800 bps for L-band carrier frequency. BER performance results show that RS codes can provide large improvement, however there is still 5 to 20 dB discrepancy between the performance in an AWGN channel and LMSAT channel. Therefore to obtain further improvement a novel receiver structure which employs fading prediction in conjunction with RS codes has been introduce and analyzed. With practical predictor lengths, simulated performance results show that the proposed technique greatly reduces the performance degradation caused by the fading channel.
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Khan, Naeem. "Linear prediction approaches to compensation of missing measurement in Kalman filtering." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10122.
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Kalrnan filter relies heavily on perfect knowledge of sensor readings, used to compute the minimum mean square error estimate of the system state. However in reality, unavailability of output data might occur due to factors including sensor faults and failures, confined memory spaces of buffer registers and congestion of communication channels. Therefore investigations on the effectiveness of Kalman filtering in the case of imperfect data have, since the last decade, been an interesting yet challenging research topic. The prevailed methodology employed in the state estimation for imperfect data is the open loop estimation wherein the measurement update step is skipped during data loss time. This method has several shortcomings such as high divergence rate, not regaining its steady states after the data is resumed, etc. This thesis proposes a novel approach, which is found efficient for both stationary and non- stationary processes, for the above scenario, based on linear prediction schemes. Utilising the concept of linear prediction, the missing data (output signal) is reconstructed through modified linear prediction schemes. This signal is then employed in Kalman filtering at the measure- ment update step. To reduce the computational cost in the large matrix inversions, a modified Levinson-Durbin algorithm is employed. It is shown that the proposed scheme offers promising results in the event of loss of observations and exhibits the general properties of conventional Kalman filters. To demonstrate the effectiveness of the proposed scheme, a rigid body spacecraft case study subject to measurement loss has been considered.
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Jukić, Ante [Verfasser]. "Sparse Multi-Channel Linear Prediction for Blind Speech Dereverberation / Ante Jukić." München : Verlag Dr. Hut, 2017. http://d-nb.info/1149580399/34.
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Al-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.
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Lawless, 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.
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Phadnis, 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.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged 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.
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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.
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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictions calculated with stochastic methods such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA). By contrast the traditional approach was instead to use raw data as inputs. The proposed methods show superior result in yielding profit: at best 1.1% in the Swedish market and 4.6% in the American market. The neural network yielded more profit than the linear regression model, which is reasonable given its ability to find nonlinear patterns. The historical data was used with different window sizes. This gives a good understanding of the window size impact on the prediction performance.
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Peters, 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.
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Sapankevych, Nicholas. "Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5569.
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Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting, weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms.
One type of time series interpolation and prediction algorithm that has been proven to be effective for these various types of applications is Support Vector Regression (SVR) [1], which is based on the Support Vector Machine (SVM) developed by Vapnik et al. [2, 3]. The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.
As with most time series prediction algorithms, there are typically challenges associated in applying a given heuristic to any general problem. One difficult challenge in using SVR to solve these types of problems is the selection of free parameters associated with the SVR algorithm. There is no given heuristic to select SVR free parameters and the user is left to adjust these parameters in an ad hoc manner.
The focus of this dissertation is to present an alternative to the typical ad hoc approach of tuning SVR for time series prediction problems by using Particle Swarm Optimization (PSO) to assist in the SVR free parameter selection process. Developed by Kennedy and Eberhart [4-8], PSO is a technique that emulates the process living creatures (such as birds or insects) use to discover food resources at a given geographic location. PSO has been proven to be an effective technique for many different kinds of optimization problems [9-11].
The focus of this dissertation is to present an alternative to the typical ad hoc approach of tuning SVR for time series prediction problems by using Particle Swarm Optimization (PSO) to assist in the SVR free parameter selection process. Developed by Kennedy and Eberhart [4-8], PSO is a technique that emulates the process living creatures (such as birds or insects) use to discover food resources at a given geographic location. PSO has been proven to be an effective technique for many different kinds of optimization problems [9-11].
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Chen, Xinyu. "Inference in Constrained Linear Regression." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/405.
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Regression analyses constitutes an important part of the statistical inference and has great applications in many areas. In some applications, we strongly believe that the regression function changes monotonically with some or all of the predictor variables in a region of interest. Deriving analyses under such constraints will be an enormous task. In this work, the restricted prediction interval for the mean of the regression function is constructed when two predictors are present. I use a modified likelihood ratio test (LRT) to construct prediction intervals.
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Taga, 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/.
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Consideramos um modelo de regressão linear simples, em que tanto a variável resposta como a independente estão sujeitas a censura intervalar. Como motivação utilizamos um estudo em que o objetivo é avaliar a possibilidade de previsão dos resultados de um exame audiológico comportamental a partir dos resultados de um exame audiológico eletrofisiológico. Calculamos intervalos de previsão para a variável resposta, analisamos o comportamento dos estimadores de máxima verossimilhança obtidos sob o modelo proposto e comparamos seu desempenho com aquele de estimadores obtidos de um modelo de regressão linear simples usual, no qual a censura dos dados é desconsiderada.We 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.
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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.
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Marshall, 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.
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Basha, 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.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This 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.
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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.
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