Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance

Implementación de Algoritmos de Procesado de Señal sobre FPGA: Especificación, Reutilización y Exploración del Espacio de Diseño

Esta Tesis aborda el diseño e implementación de aplicaciones en el campo de procesado de señal, utilizando como plataforma los dispositivos reconfigurables FPGA. Esta plataforma muestra una alta capacidad de lógica, e incorpora elementos orientados al procesado de señal, que unido a su relativamente bajo coste, la hacen ideal para el desarrollo de aplicaciones de procesado de señal cuando se requiere realizar un procesado intensivo y se buscan unas altas prestaciones. Sin embargo, el coste asociado al desarrollo en estas plataformas es elevado. Mientras que el aumento en la capacidad lógica de los dispositivos FPGA permite el desarrollo de sistemas completos, los requisitos de altas prestaciones obligan a que en muchas ocasiones se deban optimizar operadores a muy bajo nivel. Además de las restricciones temporales que imponen este tipo de aplicaciones, también tienen asociadas restricciones de área asociadas al dispositivo, lo que obliga a evaluar y verificar entre diferentes alternativas de implementación. El ciclo de diseño e implementación para estas aplicaciones se puede prolongar tanto, que es normal que aparezcan nuevos modelos de FPGA, con mayor capacidad y mayor velocidad, antes de completar el sistema, y que hagan a las restricciones utilizadas para el diseño del sistema inútiles. Para mejorar la productividad en el desarrollo de estas aplicaciones, y con ello acortar su ciclo de diseño, se pueden encontrar diferentes métodos. Esta Tesis se centra en la reutilización de componentes hardware previamente diseñados y verificados. Aunque los lenguajes HDL convencionales permiten reutilizar componentes ya definidos, se pueden realizar mejoras en la especificación que simplifiquen el proceso de incorporar componentes a nuevos diseños. Así, una primera parte de la Tesis se orientará a la especificación de diseños basada en componentes predefinidos. Esta especificación no sólo busca mejorar y simplificar el proceso de añadir componentes a una descripción, sino que también busca mejorar la calidad del diseño especificado, ofreciendo una mayor posibilidad de configuración e incluso la posibilidad de informar de características de la propia descripción. Reutilizar una componente ya descrito depende en gran medida de la información que se ofrezca para su integración en un sistema. En este sentido los HDLs convencionales únicamente proporcionan junto con la descripción del componente la interfaz de entrada/ salida y un conjunto de parámetros para su configuración, mientras que el resto de información requerida normalmente se acompaña mediante documentación externa. En la segunda parte de la Tesis se propondrán un conjunto de encapsulados cuya finalidad es incorporar junto con la propia descripción del componente, información que puede resultar útil para su integración en otros diseños. Incluyendo información de la implementación, ayuda a la configuración del componente, e incluso información de cómo configurar y conectar al componente para realizar una función. Finalmente se elegirá una aplicación clásica en el campo de procesado de señal, la transformada rápida de Fourier (FFT), y se utilizará como ejemplo de uso y aplicación, tanto de las posibilidades de especificación como de los encapsulados descritos. El objetivo del diseño realizado no sólo mostrará ejemplos de la especificación propuesta, sino que también se buscará obtener una implementación de calidad comparable con resultados de la literatura. Para ello, el diseño realizado se orientará a su implementación en FPGA, aprovechando tanto los elementos lógicos generalistas como elementos específicos de bajo nivel disponibles en estos dispositivos. Finalmente, la especificación de la FFT obtenida se utilizará para mostrar cómo incorporar en su interfaz información que ayude para su selección y configuración desde fases tempranas del ciclo de diseño. Abstract This PhD. thesis addresses the design and implementation of signal processing applications using reconfigurable FPGA platforms. This kind of platform exhibits high logic capability, incorporates dedicated signal processing elements and provides a low cost solution, which makes it ideal for the development of signal processing applications, where intensive data processing is required in order to obtain high performance. However, the cost associated to the hardware development on these platforms is high. While the increase in logic capacity of FPGA devices allows the development of complete systems, high-performance constraints require the optimization of operators at very low level. In addition to time constraints imposed by these applications, Area constraints are also applied related to the particular device, which force to evaluate and verify a design among different implementation alternatives. The design and implementation cycle for these applications can be tedious and long, being therefore normal that new FPGA models with a greater capacity and higher speed appear before completing the system implementation. Thus, the original constraints which guided the design of the system become useless. Different methods can be used to improve the productivity when developing these applications, and consequently shorten their design cycle. This PhD. Thesis focuses on the reuse of hardware components previously designed and verified. Although conventional HDLs allow the reuse of components already defined, their specification can be improved in order to simplify the process of incorporating new design components. Thus, a first part of the PhD. Thesis will focus on the specification of designs based on predefined components. This specification improves and simplifies the process of adding components to a description, but it also seeks to improve the quality of the design specified with better configuration options and even offering to report on features of the description. Hardware reuse of a component for its integration into a system largely depends on the information it offers. In this sense the conventional HDLs only provide together with the component description, the input/output interface and a set of parameters for its configuration, while other information is usually provided by external documentation. In the second part of the Thesis we will propose a formal way of encapsulation which aims to incorporate with the component description information that can be useful for its integration into other designs. This information will include features of the own implementation, but it will also support component configuration, and even information on how to configure and connect the component to carry out a function. Finally, the fast Fourier transform (FFT) will be chosen as a well-known signal processing application. It will be used as case study to illustrate the possibilities of proposed specification and encapsulation formalisms. The objective of the FFT design is not only to show practical examples of the proposed specification, but also to obtain an implementation of a quality comparable to scientific literature results. The design will focus its implementation on FPGA platforms, using general logic elements as base of the implementation, but also taking advantage of low-level specific elements available on these devices. Last, the specification of the obtained FFT will be used to show how to incorporate in its interface information to assist in the selection and configuration process early in the design cycle.

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel–Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although acorrelation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo. Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analyzed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56 ± 6.06 years, intervals 58–82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations

Two way clustering of Microarray Data using a Hybrid Approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Modal contribution and state space order selection in operational modal analysis

The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.

On the use of discrete cosine transforms for multicarrier communications

In this correspondence, the conditions to use any kind of discrete cosine transform (DCT) for multicarrier data transmission are derived. The symmetric convolution-multiplication property of each DCT implies that when symmetric convolution is performed in the time domain, an element-by-element multiplication is performed in the corresponding discrete trigonometric domain. Therefore, appending symmetric redun-dancy (as prefix and suffix) into each data symbol to be transmitted, and also enforcing symmetry for the equivalent channel impulse response, the linear convolution performed in the transmission channel becomes a symmetric convolution in those samples of interest. Furthermore, the channel equalization can be carried out by means of a bank of scalars in the corresponding discrete cosine transform domain. The expressions for obtaining the value of each scalar corresponding to these one-tap per subcarrier equalizers are presented. This study is completed with several computer simulations in mobile broadband wireless communication scenarios, considering the presence of carrier frequency offset (CFO). The obtained results indicate that the proposed systems outperform the standardized ones based on the DFT.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

High Performance FPGA-oriented mersenne twister uniform random number generator

Mersenne Twister (MT) uniform random number generators are key cores for hardware acceleration of Monte Carlo simulations. In this work, two different architectures are studied: besides the classical table-based architecture, a different architecture based on a circular buffer and especially targeting FPGAs is proposed. A 30% performance improvement has been obtained when compared to the fastest previous work. The applicability of the proposed MT architectures has been proven in a high performance Gaussian RNG.

A practical approach for outdoors distributed target localization in wireless sensor networks

Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensusbased implementation of the algorithm is proposed based on an augmented Lagrangian approach and primaldual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

Permutation tests for nonparametric detection

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman?Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the permutation and rank tests (detectors) against the parametric ones in radar applications. First, a brief review of some contributions on nonparametric tests is realized. Then, the optimum permutation and rank tests are derived. Finally, a performance analysis is realized by Monte-Carlo simulations for the corresponding detectors, and the results are shown in curves of detection probability versus signal-to-noise ratio

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Many existing engineering works model the statistical characteristics of the entities under study as normal distributions. These models are eventually used for decision making, requiring in practice the definition of the classification region corresponding to the desired confidence level. Surprisingly enough, however, a great amount of computer vision works using multidimensional normal models leave unspecified or fail to establish correct confidence regions due to misconceptions on the features of Gaussian functions or to wrong analogies with the unidimensional case. The resulting regions incur in deviations that can be unacceptable in high-dimensional models. Here we provide a comprehensive derivation of the optimal confidence regions for multivariate normal distributions of arbitrary dimensionality. To this end, firstly we derive the condition for region optimality of general continuous multidimensional distributions, and then we apply it to the widespread case of the normal probability density function. The obtained results are used to analyze the confidence error incurred by previous works related to vision research, showing that deviations caused by wrong regions may turn into unacceptable as dimensionality increases. To support the theoretical analysis, a quantitative example in the context of moving object detection by means of background modeling is given.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks ({WSNs}) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of {WSNs} facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers ({DCs}). The high economical and environmental impact of the energy consumption in {DCs} requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of {WSNs}: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of {DCs}: energy-optimal workload assignment policies in heterogeneous {DCs}, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

Hybrid Contribution of JPEG200 Video Files for Professional Production Centers Reliability and Non-Reliability Transmission File Based in Image Quality Needed

ATM, SDH or satellite have been used in the last century as the contribution network of Broadcasters. However the attractive price of IP networks is changing the infrastructure of these networks in the last decade. Nowadays, IP networks are widely used, but their characteristics do not offer the level of performance required to carry high quality video under certain circumstances. Data transmission is always subject to errors on line. In the case of streaming, correction is attempted at destination, while on transfer of files, retransmissions of information are conducted and a reliable copy of the file is obtained. In the latter case, reception time is penalized because of the low priority this type of traffic on the networks usually has. While in streaming, image quality is adapted to line speed, and line errors result in a decrease of quality at destination, in the file copy the difference between coding speed vs line speed and errors in transmission are reflected in an increase of transmission time. The way news or audiovisual programs are transferred from a remote office to the production centre depends on the time window and the type of line available; in many cases, it must be done in real time (streaming), with the resulting image degradation. The main purpose of this work is the workflow optimization and the image quality maximization, for that reason a transmission model for multimedia files adapted to JPEG2000, is described based on the combination of advantages of file transmission and those of streaming transmission, putting aside the disadvantages that these models have. The method is based on two patents and consists of the safe transfer of the headers and data considered to be vital for reproduction. Aside, the rest of the data is sent by streaming, being able to carry out recuperation operations and error concealment. Using this model, image quality is maximized according to the time window. In this paper, we will first give a briefest overview of the broadcasters requirements and the solutions with IP networks. We will then focus on a different solution for video file transfer. We will take the example of a broadcast center with mobile units (unidirectional video link) and regional headends (bidirectional link), and we will also present a video file transfer file method that satisfies the broadcaster requirements.