Sparse Signal Recovery and Detection Utilizing Side Information

In this dissertation, we investigate the signal recovery and detection task for compressive sensing and wireless spectrum sensing.First, we investigate the compressive sensing task for the difference frames of videos.Exploiting the clustered property, we design an effective structural aware reconstruction technique that is capable of eliminating isolated nonzero noisy pixels, and promoting undiscovered signal coefficients.Further, we develop a novel optimization based method for the compressive sensing of binary sparse signals. We formulate the reconstruction task as a least square minimization procedure, and propose a novel regularization term based on the weighted sum of ell_1 norm and ell_infty norm.Moreover, we study the compressive sensing for asymmetrical signals.We devise an efficient algorithm that greatly improves the reconstruction quality of asymmetrical sparse signals.Further, we investigate sparse reconstruction of clustered sparse signals with asymmetrical features.We develop a powerful technique that is capable of taking inference of the signal, estimating the mixture density, and exploiting the clustered features.Finally, we investigate the spectrum sensing task for cognitive radio.We develop an eigenvalue based technique that notably improve the primary user detection performance under finite number of sensors and samples.Electrical Engineerin

A Bayesian approach on asymmetric heavy tailed mixture of factor analyzer

A Mixture of factor analyzer (MFA) model is a powerful tool to reduce the number of free parameters in high-dimensional data through the factor-analyzer technique based on the covariance matrices. This model also prepares an efficient methodology to determine latent groups in data. In this paper, we use an MFA model with a rich and flexible class of distributions called hidden truncation hyperbolic (HTH) distribution and a Bayesian structure with several computational benefits. The MFA based on the HTH family allows the factor scores and the error component can be skewed and heavy-tailed. Therefore, using the HTH family leads to the robustness of the MFA in modeling asymmetrical datasets with/without outliers. Furthermore, the HTH family, because of several desired properties, including analytical flexibility, provides steps in the estimation of parameters that are computationally tractable. In the present study, the advantages of MFA based on the HTH family have been discussed and the suitable efficiency of the introduced MFA model has been demonstrated by using real data examples and simulation

Basin stability of single machine infinite bus power systems with Levy type load fluctuations

10th International Conference on Electrical and Electronics Engineering, ELECO 2017; Bursa; Turkey; 29 November 2017 through 2 December 2017In this paper, the basin stability of single machine infinite bus power systems with alpha-stable Levy type load fluctuations are investigated over the parameter space of mechanical power and damping parameter. The probabilities of returning to the stable equilibrium point are calculated for different characteristic exponent and skewness parameters of alpha-stable Levy noise to see the effect of impulsive and asymmetric load fluctuations

Carbon Nano Tubes (CNTS) for the development of high-performance and smart composites.

Los nanotubos de carbono han atraído una enorme atención en los últimos años debido a sus propiedades multifuncionales sobresalientes. Un número cada vez mayor de trabajos de investigación de primera línea centran su interés en la búsqueda de aplicaciones prácticas que den uso de las notables propiedades de los nanotubos de carbono, incluyendo una elevada resistencia mecánica, propiedades piezorestivas, alta conductividad eléctrica, ligereza, excelente estabilidad química y térmica. En concreto, los estudios más recientes plantean dos grandes ramas de aplicación: fabricación de estructuras aligeradas de alta resistencia, y desarrollo de estructuras inteligentes. Con respecto a la primera línea de aplicación, el desarrollo de materiales compuestos ligeros de alta resistencia conecta con la creciente tendencia de la ingeniería estructural a incorporar materiales compuestos innovadores. Ejemplos recientes como el avión comercial Boeing 787, en el que la mitad del peso fue diseñado con materiales compuestos, predicen un futuro auspicioso para los nanotubos de carbono en la ingeniería aeronáutica. Sin embargo, aún resulta más interesante el comportamiento piezorresistivo de los compuestos reforzados con nanotubos de carbono, ya que posibilita la creación de estructuras que no sólo presentan altas capacidades portantes y reducido peso específico, sino que también ofrecen capacidades de auto-detección de deformaciones. Cuando el material se ve sometido a una deformación externa, en virtud de dicha propiedad piezoresistiva, la conductividad eléctrica varía de modo que es posible correlacionar su respuesta eléctrica con el campo deformacional aplicado. Estas propiedades multifuncionales entroncan con el nuevo paradigma de la Vigilancia de la Salud Estructural el cual aboga por el uso de materiales/estructuras inteligentes para resolver el problema de escalabilidad. En este contexto, la estructura o parte de ella presenta capacidades de auto-detección de tal manera que el mantenimiento basado en la condición puede llevarse a cabo sin necesidad de incluir sensores externos. En ambas líneas, la mayoría de las investigaciones han centrado el estudio en la experimentación, siendo mucho menor el número de trabajos que plantean modelos teóricos capaces de simular las propiedades mecánicas, eléctricas y electromecánicas de estos compuestos. Desde un punto de vista mecánico, existen estudios experimentales que informan acerca de los efectos perjudiciales sobre la respuesta macroscópica de aspectos micromecánicos tales como la tendencia a formar aglomerados, así como la curvatura de los nanotubos de carbono. Es por ello esencial desarrollar modelos teóricos que incorporen estos efectos y asistan al diseño de elementos estructurales reforzados con nanotubos de carbono. Respecto al estudio de las propiedades de conductividad y piezoresistividad, es esencial desarrollar formulaciones teóricas capaces de abordar la optimización de las propiedades de autodetección de deformaciones. Asimismo, es crucial comprender los diferentes mecanismos físicos que rigen la conductividad eléctrica de estos compuestos, de modo que sea posible incorporar su efecto diferencial dentro de un marco teórico. Por último, también es fundamental avanzar hacia el dominio del tiempo con el fin de desarrollar aplicaciones de vigilancia de la salud estructural basada en vibraciones. Con todo ello, los esfuerzos de esta tesis se han centrado en el modelado de las propiedades mecánicas, conductivas y electromecánicas de los compuestos reforzados con nanotubos de carbono para el desarrollo de estructuras inteligentes y de alta resistencia. Estas dos aplicaciones, a saber, compuestos de alta resistencia e inteligentes, han sido enmarcadas en el ámbito de los materiales poliméricos y de cemento, respectivamente. La razón de esta distinción se debe a la presunción de que los compuestos poliméricos pueden encontrar aplicaciones directas como paneles de fuselaje para estructuras de aeronaves, así como refuerzos mecánicos sobre estructuras pre-existentes. En cuanto al uso de nanotubos de carbono como inclusiones multifuncionales para compuestos inteligentes, tanto los materiales poliméricos como los de base cemento ofrecen una amplia gama de aplicaciones potenciales. Sin embargo, la similitud entre los compuestos de base cemento y el hormigón estructural convencional sugiere la idea de desarrollar sensores embebidos que ofrezcan una monitorización continua integrada sin comprometer a priori la durabilidad de la estructura huésped. Tanto las propiedades mecánicas como las conductivas han sido estudiadas mediante métodos de homogeneización de campo medio. Aspectos micromecánicos tales como la relación de aspecto, el contenido, la distribución de la orientación, la ondulación o la aglomeración de los nanotubos se han estudiado en detalle e incorporado al análisis de diferentes elementos estructurales. De manera similar, se han estudiado las propiedades de conductividad eléctrica y auto-detección de deformaciones bajo cargas cuasi-estáticas mediante modelos mixtos de homogenización micromecánica de Mori-Tanaka. Los principales mecanismos que gobiernan las propiedades de transporte eléctrico de estos compuestos, a saber, los efectos de túnel cuántico y la formación de canales conductores, se han incorporado por separado en las simulaciones a través de la teoría de percolación de fibras conductoras. Los resultados teóricos han sido validados con éxito mediante experimentos en condiciones de laboratorio. Finalmente, se ha desarrollado un nuevo circuito equivalente piezorresistivo/piezoeléctrico para el modelado electromecánico de materiales de base cemento reforzado con nanotubos de carbono en el dominio del tiempo. Con los experimentos como base de validación, se ha demostrado que el enfoque propuesto proporciona resultados precisos y ofrece un marco teórico apto para aplicaciones de procesamiento de señales y monitorización de la salud estructural. Se espera que el trabajo desarrollado en esta tesis pueda proporcionar herramientas valiosas que permitan profundizar en la comprensión de los principales aspectos físicos que controlan las propiedades mecánicas, eléctricas y electromecánicas de los compuestos reforzados con nanotubos de carbono. Además, se espera que los resultados presentados en esta tesis impulsen el desarrollo de materiales compuestos auto-sensibles embebidos para aplicaciones de vigilancia de la salud estructural.Carbon nanotubes have drawn enormous attention in recent years due to their outstanding multifunctional properties. A constantly growing number of works at the front line of research pursue potential applications of their remarkable physical properties, including elevated load-bearing capacity, piezoresistive properties, high electrical conductivity, lightness, and excellent chemical and thermal stability. In particular, most recent works contemplate two different application branches: manufacture of light-weight high-strength structures, and development of smart structures. With regard to the first line of application, the development of high-strength lightweight composites connects with the growing tendency of structural engineering to incorporate advanced composite materials. Recent noticeable examples such as the commercial aircraft Boeing 787, in which half of the total weight was designed with composite materials, predict an auspicious future for carbon nanotubes in aircraft structures. Nonetheless, what is even more interesting is the piezoresistive behavior of carbon nanotube-reinforced composites, which allows us to create structures that are not only high-strength and lightweight but also strain-sensitive. When the composites are subjected to external strain fields, in virtue of such piezoresistive properties, the overall electrical conductivity varies in such a way that it is possible to correlate the electrical response with the deformational state of the material. These multifunctional properties are in line with the new paradigm of Structural Health Monitoring which advocates the use of smart materials/structures to solve the scalability issue. In this context, the structure or part of it presents self-sensing capabilities in such a way that the condition-based maintenance can be conducted without necessitating external off-the-shelf sensors. In both lines, most investigations have focused on experimentation. Conversely, the number of theoretical models capable of simulating the mechanical, electrical, and electromechanical properties of these composites is still scarce. From a mechanical point of view, experiments have reported about the detrimental effects of micromechanical aspects such as agglomeration of fillers and curviness on the macroscopic properties. Hence, it is essential to develop theoretical models that allow us to include these effects and assist the design of composite structural elements. With regard to the study of the conductivity and piezoresistivity of carbon nanotube-reinforced composites, it is essential to develop theoretical formulations capable of tackling the optimization of their strain sensitivity. In addition, it is crucial to understand the different physical mechanisms that govern the electrical conductivity of these composites and include them separately in the theoretical framework. Finally, it is also fundamental to move towards the time domain in order to develop applications for vibration-based structural health monitoring. Overall, all the efforts of this thesis have been put into the modeling of the mechanical, conductive and electromechanical properties of carbon nanotube-reinforced composites for the development of high-strength and smart structures. These two applications, namely high-strength and smart composites, have been framed in the realm of polymeric and cement-based materials, respectively. The reason for this distinction is the idea that polymer composites with high load-bearing capacity can find direct applications as fuselage panels for aircraft structures, as well as mechanical reinforcements attached to pre-existing structures. With regard to the use of carbon nanotubes as fillers for smart composites, both polymer and cement-based materials offer an enormous range of potential applications. Nonetheless, the similarity between cement-based composites and regular structural concrete suggests the idea of developing continuous embedded monitoring systems without compromising the durability of the hosting structure a priori. Both mechanical and conductive properties have been studied by means of mean-field homogenization methods. Micromechanical aspects such as filler aspect ratio, content, orientation distribution, waviness or agglomeration have been studied in detail and incorporated to the analysis of different structural elements. Similarly, the electrical conductivity and strain-sensing properties of these composites under quasi-static loadings have been studied by means of mixed Mori-Tanaka micromechanics models. The main mechanisms that underlie the electrical conduction of these composites, namely quantum tunneling effects and conductive networks, have been distinguished by a percolative-type behavior. The theoretical results have been successfully validated by means of experiments under laboratory conditions. Finally, a novel piezoresistive/piezoelectric equivalent lumped circuit has been developed for the electromechanical modeling of carbon nanotube-reinforced cement-based materials in the time domain. With experiments as validating basis, the proposed approach has been shown to provide accurate results and offers a theoretical framework readily applicable to signal processing applications and structural health monitoring. The work developed in this thesis is envisaged to provide valuable tools to further the understanding of the main physical aspects that control the mechanical, electrical and electromechanical properties of composites doped with carbon nanotubes. Furthermore, it is expected to boost the development of embedded self-sensing carbon nanotube-reinforced composites for structural health monitoring applications.Premio Extraordinario de Doctorado U

Compressive learning: new models and applications

Today’s world is fuelled by data. From self-driving cars through to agriculture, massive amounts of data are used to fit learning models to provide valuable insights and predictions. Such insights come at a significant price as many traditional learning procedures have both memory and computational costs that scale with the size of the data. This quickly becomes prohibitive, even when substantial resources are available. A new way of learning is therefore needed to allow for efficient model fitting in the 21st century. The birth of compressive learning in recent years has provided a novel solution to the bottleneck of learning from big data. Situated at the core of the compressive learning framework is the construction of a so-called sketch. The sketch is a compact representation of the data that provides sufficient information for specific learning tasks. In this thesis we develop the compressive learning framework to a host of new models and applications. In the first part of the thesis, we consider the group of semi-parametric models and demonstrate the unique advantages and challenges associated with creating a compressive learning paradigm for these particular models. Concentrating on the independent component analysis model, we develop a framework of algorithms and theory enabling magnitudes of compression with respect to memory complexity compared to existing methods. In the second part of the thesis, we develop a compressive learning framework to the emerging technology of single-photon counting lidar. We demonstrate that forming a sketch of the time-of-flight data circumvents the inherent data-transfer bottleneck of existing lidar techniques. Finally, we extend the compressive lidar technology by developing both an efficient sketch-based detection algorithm that can detect the presence of a surface solely from the sketch and a sketched plug and play framework that can integrate existing powerful denoisers that are robust to noisy lidar scenes with low photon counts

Composite cure assessment using spectral analysis (via an embedded optical fibre sensor).

Available from British Library Document Supply Centre-DSC:DXN051258 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Employing data fusion & diversity in the applications of adaptive signal processing

The paradigm of adaptive signal processing is a simple yet powerful method for the class of system identification problems. The classical approaches consider standard one-dimensional signals whereby the model can be formulated by flat-view matrix/vector framework. Nevertheless, the rapidly increasing availability of large-scale multisensor/multinode measurement technology has render no longer sufficient the traditional way of representing the data. To this end, the author, who from this point onward shall be referred to as `we', `us', and `our' to signify the author myself and other supporting contributors i.e. my supervisor, my colleagues and other overseas academics specializing in the specific pieces of research endeavor throughout this thesis, has applied the adaptive filtering framework to problems that employ the techniques of data diversity and fusion which includes quaternions, tensors and graphs. At the first glance, all these structures share one common important feature: invertible isomorphism. In other words, they are algebraically one-to-one related in real vector space. Furthermore, it is our continual course of research that affords a segue of all these three data types. Firstly, we proposed novel quaternion-valued adaptive algorithms named the n-moment widely linear quaternion least mean squares (WL-QLMS) and c-moment WL-LMS. Both are as fast as the recursive-least-squares method but more numerically robust thanks to the lack of matrix inversion. Secondly, the adaptive filtering method is applied to a more complex task: the online tensor dictionary learning named online multilinear dictionary learning (OMDL). The OMDL is partly inspired by the derivation of the c-moment WL-LMS due to its parsimonious formulae. In addition, the sequential higher-order compressed sensing (HO-CS) is also developed to couple with the OMDL to maximally utilize the learned dictionary for the best possible compression. Lastly, we consider graph random processes which actually are multivariate random processes with spatiotemporal (or vertex-time) relationship. Similar to tensor dictionary, one of the main challenges in graph signal processing is sparsity constraint in the graph topology, a challenging issue for online methods. We introduced a novel splitting gradient projection into this adaptive graph filtering to successfully achieve sparse topology. Extensive experiments were conducted to support the analysis of all the algorithms proposed in this thesis, as well as pointing out potentials, limitations and as-yet-unaddressed issues in these research endeavor.Open Acces

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens