Study of L0-norm constraint normalized subband adaptive filtering algorithm

Limited by fixed step-size and sparsity penalty factor, the conventional sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms suffer from trade-off requirements of high filtering accurateness and quicker convergence behavior. To deal with this problem, this paper proposes variable step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs) for sparse system identification. We first analyze mean-square-deviation (MSD) statistics behavior of the L0-NSAF algorithm innovatively in according to a novel recursion form and arrive at corresponding expressions for the cases that background noise variance is available and unavailable, where correlation degree of system input is indicated by scaling parameter r. Based on derivations, we develop an effective variable step-size scheme through minimizing the upper bounds of the MSD under some reasonable assumptions and lemma. To realize performance improvement, an effective reset strategy is incorporated into presented algorithms to tackle with non-stationary situations. Finally, numerical simulations corroborate that the proposed algorithms achieve better performance in terms of estimation accurateness and tracking capability in comparison with existing related algorithms in sparse system identification and adaptive echo cancellation circumstances.Comment: 15 pages,15 figure

A kernel-based embedding framework for high-dimensional data analysis

The world is essentially multidimensional, e.g., neurons, computer networks, Internet traffic, and financial markets. The challenge is to discover and extract information that lies hidden in these high-dimensional datasets to support classification, regression, clustering, and visualization tasks. As a result, dimensionality reduction aims to provide a faithful representation of data in a low-dimensional space. This removes noise and redundant features, which is useful to understand and visualize the structure of complex datasets. The focus of this work is the analysis of high-dimensional data to support regression tasks and exploratory data analysis in real-world scenarios. Firstly, we propose an online framework to predict longterm future behavior of time-series. Secondly, we propose a new dimensionality reduction method to preserve the significant structure of high-dimensional data in a low-dimensional space. Lastly, we propose an sparsification strategy based on dimensionality reduction to avoid overfitting and reduce computational complexity in online applicationsEl mundo es esencialmente multidimensional, por ejemplo, neuronas, redes computacionales, tráfico de internet y los mercados financieros. El desafío es descubrir y extraer información que permanece oculta en estos conjuntos de datos de alta dimensión para apoyar tareas de clasificación, regresión, agrupamiento y visualización. Como resultado de ello, los métodos de reducción de dimensión pretenden suministrar una fiel representación de los datos en un espacio de baja dimensión. Esto permite eliminar ruido y características redundantes, lo que es útil para entender y visualizar la estructura de conjuntos de datos complejos. Este trabajo se enfoca en el análisis de datos de alta dimensión para apoyar tareas de regresión y el análisis exploratorio de datos en escenarios del mundo real. En primer lugar, proponemos un marco para la predicción del comportamiento a largo plazo de series de tiempo. En segundo lugar, se propone un nuevo método de reducción de dimensión para preservar la estructura significativa de datos de alta dimensión en un espacio de baja dimensión. Finalmente, proponemos una estrategia de esparsificacion que utiliza reducción de dimensional dad para evitar sobre ajuste y reducir la complejidad computacional de aplicaciones en líneaDoctorad

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Physically inspired methods and development of data-driven predictive systems.

Traditionally building of predictive models is perceived as a combination of both science and art. Although the designer of a predictive system effectively follows a prescribed procedure, his domain knowledge as well as expertise and intuition in the field of machine learning are often irreplaceable. However, in many practical situations it is possible to build well–performing predictive systems by following a rigorous methodology and offsetting not only the lack of domain knowledge but also partial lack of expertise and intuition, by computational power. The generalised predictive model development cycle discussed in this thesis is an example of such methodology, which despite being computationally expensive, has been successfully applied to real–world problems. The proposed predictive system design cycle is a purely data–driven approach. The quality of data used to build the system is thus of crucial importance. In practice however, the data is rarely perfect. Common problems include missing values, high dimensionality or very limited amount of labelled exemplars. In order to address these issues, this work investigated and exploited inspirations coming from physics. The novel use of well–established physical models in the form of potential fields, has resulted in derivation of a comprehensive Electrostatic Field Classification Framework for supervised and semi–supervised learning from incomplete data. Although the computational power constantly becomes cheaper and more accessible, it is not infinite. Therefore efficient techniques able to exploit finite amount of predictive information content of the data and limit the computational requirements of the resource–hungry predictive system design procedure are very desirable. In designing such techniques this work once again investigated and exploited inspirations coming from physics. By using an analogy with a set of interacting particles and the resulting Information Theoretic Learning framework, the Density Preserving Sampling technique has been derived. This technique acts as a computationally efficient alternative for cross–validation, which fits well within the proposed methodology. All methods derived in this thesis have been thoroughly tested on a number of benchmark datasets. The proposed generalised predictive model design cycle has been successfully applied to two real–world environmental problems, in which a comparative study of Density Preserving Sampling and cross–validation has also been performed confirming great potential of the proposed methods

Physically inspired methods and development of data-driven predictive systems

Traditionally building of predictive models is perceived as a combination of both science and art. Although the designer of a predictive system effectively follows a prescribed procedure, his domain knowledge as well as expertise and intuition in the field of machine learning are often irreplaceable. However, in many practical situations it is possible to build well–performing predictive systems by following a rigorous methodology and offsetting not only the lack of domain knowledge but also partial lack of expertise and intuition, by computational power. The generalised predictive model development cycle discussed in this thesis is an example of such methodology, which despite being computationally expensive, has been successfully applied to real–world problems. The proposed predictive system design cycle is a purely data–driven approach. The quality of data used to build the system is thus of crucial importance. In practice however, the data is rarely perfect. Common problems include missing values, high dimensionality or very limited amount of labelled exemplars. In order to address these issues, this work investigated and exploited inspirations coming from physics. The novel use of well–established physical models in the form of potential fields, has resulted in derivation of a comprehensive Electrostatic Field Classification Framework for supervised and semi–supervised learning from incomplete data. Although the computational power constantly becomes cheaper and more accessible, it is not infinite. Therefore efficient techniques able to exploit finite amount of predictive information content of the data and limit the computational requirements of the resource–hungry predictive system design procedure are very desirable. In designing such techniques this work once again investigated and exploited inspirations coming from physics. By using an analogy with a set of interacting particles and the resulting Information Theoretic Learning framework, the Density Preserving Sampling technique has been derived. This technique acts as a computationally efficient alternative for cross–validation, which fits well within the proposed methodology. All methods derived in this thesis have been thoroughly tested on a number of benchmark datasets. The proposed generalised predictive model design cycle has been successfully applied to two real–world environmental problems, in which a comparative study of Density Preserving Sampling and cross–validation has also been performed confirming great potential of the proposed methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

An Automated Pipeline for Variability Detection and Classification for the Small Telescopes Installed at the Liverpool Telescope

The Small Telescopes at the Liverpool Telescope (STILT) is an almost decade old project to install a number of wide field optical instruments to the Liverpool Telescope, named Skycams, to monitor weather conditions and yield useful photometry on bright astronomical sources. The motivation behind this thesis is the development of algorithms and techniques which can automatically exploit the data generated during the first 1200 days of Skycam operation to catalogue variable sources in the La Palma sky. A previously developed pipeline reduces the Skycam images and produces photometric time-series data named light curves of millions of objects. 590,492 of these objects have 100 or more data points of sufficient quality to attempt a variability analysis. The large volume and relatively high noise of this data necessitated the use of Machine Learning and sophisticated optimisation techniques to successfully extract this information. The Skycam instruments have no control over the orientation and pointing of the Liverpool Telescope and therefore resample areas of the sky highly irregularly. The term used for this resampling in astronomy is ‘cadence’. The unusually irregular Skycam cadence places increased strain on the algorithms designed for the detection of periodicity in light curves. This thesis details the development of a period estimation method based on a novel implementation of a genetic algorithm combined with a generational clustering method. Named GRAPE (Genetic Routine for Astronomical Period Estimation), this algorithm deconstructs the space of possible periods for a light curve into regions in which the genetic population clusters. These regions are then fine-tuned using a k-means clustering algorithm to return a set of independent period candidates which are then analysed using a Vuong closeness test to discriminate between aliased and true periods. This thesis demonstrates the capability of GRAPE on a set of synthetic light curves built using traditional regular cadence sampling and Skycam style cadence for four different shapes of periodic light curve. The performance of GRAPE on these light curves is compared to a more traditional periodogram which returns a set of peaks and is then analysed using Vuong closeness tests. GRAPE obtains similar performance compared to the periodogram on all the light curve shapes but with less computational complexity allowing for more efficient light curve analysis. Automated classification of variable light curves has been explored over the last decade. Multiple features have been engineered to identify patterns in the light curves of different classes of variable star. Within the last few years deep learning has come to prominence as a method of automatically generating informative representations of the data for the solution of a desired problem, such as a classification task. A set of models using Random Forests, Support Vector Machines and Neural Networks were trained using a set of variable Skycam light curves of five classes. Using 16 features engineered from previous methods an Area under the Curve (AUC) of 0.8495 was obtained. Replacing these features with inputs from the pixel intensities from a 100 by 20 pixel image representation, produced an AUC of 0.6348, which improved to 0.7952 when provided with additional context to the dimensionality of the image. Despite the inferior performance, the importance of the different pixels produced relations in the trained models demonstrating that they had produced features based on well-understood patterns in the different classes of light curve. Using features produced by Richards et al. and Kim & Bailer-Jones et al., a set of features to train machine learning classification models was constructed. In addition to this set of features, a semi-supervised set of novel features was designed to describe the shape of light curves phased around the GRAPE candidate period. This thesis investigates the performance of the PolyFit algorithm of Prsa et al., a technique to fit four piecewise polynomials with discontinuous knots capable of connecting across the phase boundary at phases of zero and one. This method was designed to fit eclipsing binary phased light curves however were also described to be fully capable on other variable star types. The optimisation method used by PolyFit is replaced by a novel genetic algorithm optimisation routine to fit the model to Skycam data with substantial improvement in performance. The PolyFit model is applied to the candidate period and twice this period for every classified light curve. This interpolation produces novel features which describe similar statistics to the previously developed methods but which appear significantly more resilient to the Skycam noise and are often preferred by the trained models. In addition, Principal Component Analysis (PCA) is used to investigate a set of 6897 variable light curves and discover that the first ten principal components are sufficient to describe 95\% of the variance of the fitted models. This trained PCA model is retained and used to generate twenty novel shape features. Whilst these features are not dominant in their importance to the learned models, they have above average importance and help distinguish some objects in the light curve classification task. The second principal component in particular is an important feature in the discrimination of short period pulsating and eclipsing variables as it appears to be an automatically learned robust skewness measure. The method described in this thesis produces 112 features of the Skycam light curves, 38 variability indices which are quickly obtainable and 74 which require the computation of a candidate period using GRAPE. A number of machine learning classifiers are investigated to produce high-performance models for the detection and classification of variable light curves from the Skycam dataset. A Random Forest classifier uses a training set of 859 light curves of 12 object classes to produce a classifier with a multi-class F1 score of 0.533. It would be computationally infeasible to produce all the features for every Skycam light curve, therefore an automated pipeline has been developed which combines a Skycam trend removal pipeline, GRAPE and our machine learned classifiers. It initialises with a set of Skycam light curves from objects cross-matched from the American Association of Variable Star Observers (AAVSO) Variable Star Index (VSI), one of the most comprehensive catalogues of variable stars available. The learned models classify the full 112 features generated for these cross-matched light curves and confident matches are selected to produce a training set for a binary variability detection model. This model utilises only the 38 variability indices to identify variable light curves rapidly without the use of GRAPE. This variability model, trained using a random forest classifier, obtains an F1 score of 0.702. Applying this model to the 590,492 Skycam light curves yields 103,790 variable candidates of which 51,129 candidates have been classified and are available for further analysis

STRUCTURED SPARSITY DRIVEN LEARNING: THEORY AND ALGORITHMS

Ph.DDOCTOR OF PHILOSOPH

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio