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.

Técnicas de procesado digital de señal aplicadas a acelerómetros

En este proyecto se estudian y analizan las diferentes técnicas de procesado digital de señal aplicadas a acelerómetros. Se hace uso de una tarjeta de prototipado, basada en DSP, para realizar las diferentes pruebas. El proyecto se basa, principalmente, en realizar filtrado digital en señales provenientes de un acelerómetro en concreto, el 1201F, cuyo campo de aplicación es básicamente la automoción. Una vez estudiadas la teoría de procesado y las características de los filtros, diseñamos una aplicación basándonos sobre todo en el entorno en el que se desarrollaría una aplicación de este tipo. A lo largo del diseño, se explican las diferentes fases: diseño por ordenador (Matlab), diseño de los filtros en el DSP (C), pruebas sobre el DSP sin el acelerómetro, calibración del acelerómetro, pruebas finales sobre el acelerómetro... Las herramientas utilizadas son: la plataforma Kit de evaluación 21-161N de Analog Devices (equipado con el entorno de desarrollo Visual DSP 4.5++), el acelerómetro 1201F, el sistema de calibración de acelerómetros CS-18-LF de Spektra y los programas software MATLAB 7.5 y CoolEditPRO 2.0. Se realizan únicamente filtros IIR de 2º orden, de todos los tipos (Butterworth, Chebyshev I y II y Elípticos). Realizamos filtros de banda estrecha, paso-banda y banda eliminada, de varios tipos, dentro del fondo de escala que permite el acelerómetro. Una vez realizadas todas las pruebas, tanto simulaciones como físicas, se seleccionan los filtros que presentan un mejor funcionamiento y se analizan para obtener conclusiones. Como se dispone de un entorno adecuado para ello, se combinan los filtros entre sí de varias maneras, para obtener filtros de mayor orden (estructura paralelo). De esta forma, a partir de filtros paso-banda, podemos obtener otras configuraciones que nos darán mayor flexibilidad. El objetivo de este proyecto no se basa sólo en obtener buenos resultados en el filtrado, sino también de aprovechar las facilidades del entorno y las herramientas de las que disponemos para realizar el diseño más eficiente posible. In this project, we study and analize digital signal processing in order to design an accelerometer-based application. We use a hardware card of evaluation, based on DSP, to make different tests. This project is based in design digital filters for an automotion application. The accelerometer type is 1201F. First, we study digital processing theory and main parameters of real filters, to make a design based on the application environment. Along the application, we comment all the different steps: computer design (Matlab), filter design on the DSP (C language), simulation test on the DSP without the accelerometer, accelerometer calibration, final tests on the accelerometer... Hardware and software tools used are: Kit of Evaluation 21-161-N, based on DSP, of Analog Devices (equiped with software development tool Visual DSP 4.5++), 1201-F accelerometer, CS-18-LF calibration system of SPEKTRA and software tools MATLAB 7.5 and CoolEditPRO 2.0. We only perform 2nd orden IIR filters, all-type : Butterworth, Chebyshev I and II and Ellyptics. We perform bandpass and stopband filters, with very narrow band, taking advantage of the accelerometer's full scale. Once all the evidence, both simulations and physical, are finished, filters having better performance and analyzed and selected to draw conclusions. As there is a suitable environment for it, the filters are combined together in different ways to obtain higher order filters (parallel structure). Thus, from band-pass filters, we can obtain many configurations that will give us greater flexibility. The purpose of this project is not only based on good results in filtering, but also to exploit the facilities of the environment and the available tools to make the most efficient design possible.

Estudio de calidad de codificación utilizando los perfiles 10 Bits del estándar H.264

Desde los inicios de la codificación de vídeo digital hasta hoy, tanto la señal de video sin comprimir de entrada al codificador como la señal de salida descomprimida del decodificador, independientemente de su resolución, uso de submuestreo en los planos de diferencia de color, etc. han tenido siempre la característica común de utilizar 8 bits para representar cada una de las muestras. De la misma manera, los estándares de codificación de vídeo imponen trabajar internamente con estos 8 bits de precisión interna al realizar operaciones con las muestras cuando aún no se han transformado al dominio de la frecuencia. Sin embargo, el estándar H.264, en gran auge hoy en día, permite en algunos de sus perfiles orientados al mundo profesional codificar vídeo con más de 8 bits por muestra. Cuando se utilizan estos perfiles, las operaciones efectuadas sobre las muestras todavía sin transformar se realizan con la misma precisión que el número de bits del vídeo de entrada al codificador. Este aumento de precisión interna tiene el potencial de permitir unas predicciones más precisas, reduciendo el residuo a codificar y aumentando la eficiencia de codificación para una tasa binaria dada. El objetivo de este Proyecto Fin de Carrera es estudiar, utilizando las medidas de calidad visual objetiva PSNR (Peak Signal to Noise Ratio, relación señal ruido de pico) y SSIM (Structural Similarity, similaridad estructural), el efecto sobre la eficiencia de codificación y el rendimiento al trabajar con una cadena de codificación/descodificación H.264 de 10 bits en comparación con una cadena tradicional de 8 bits. Para ello se utiliza el codificador de código abierto x264, capaz de codificar video de 8 y 10 bits por muestra utilizando los perfiles High, High 10, High 4:2:2 y High 4:4:4 Predictive del estándar H.264. Debido a la ausencia de herramientas adecuadas para calcular las medidas PSNR y SSIM de vídeo con más de 8 bits por muestra y un tipo de submuestreo de planos de diferencia de color distinto al 4:2:0, como parte de este proyecto se desarrolla también una aplicación de análisis en lenguaje de programación C capaz de calcular dichas medidas a partir de dos archivos de vídeo sin comprimir en formato YUV o Y4M. ABSTRACT Since the beginning of digital video compression, the uncompressed video source used as input stream to the encoder and the uncompressed decoded output stream have both used 8 bits for representing each sample, independent of resolution, chroma subsampling scheme used, etc. In the same way, video coding standards force encoders to work internally with 8 bits of internal precision when working with samples before being transformed to the frequency domain. However, the H.264 standard allows coding video with more than 8 bits per sample in some of its professionally oriented profiles. When using these profiles, all work on samples still in the spatial domain is done with the same precision the input video has. This increase in internal precision has the potential of allowing more precise predictions, reducing the residual to be encoded, and thus increasing coding efficiency for a given bitrate. The goal of this Project is to study, using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity) objective video quality metrics, the effects on coding efficiency and performance caused by using an H.264 10 bit coding/decoding chain compared to a traditional 8 bit chain. In order to achieve this goal the open source x264 encoder is used, which allows encoding video with 8 and 10 bits per sample using the H.264 High, High 10, High 4:2:2 and High 4:4:4 Predictive profiles. Given that no proper tools exist for computing PSNR and SSIM values of video with more than 8 bits per sample and chroma subsampling schemes other than 4:2:0, an analysis application written in the C programming language is developed as part of this Project. This application is able to compute both metrics from two uncompressed video files in the YUV or Y4M format.

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.

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

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.

Lifetime maximization for beamforming applications in wireless sensor networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If data is to be sent to a far-away base station, collaborative beamforming by the sensors may help to dis- tribute the load among the nodes and reduce fast battery depletion. However, collaborative beamforming techniques are far from opti- mality and in many cases may be wasting more power than required. In this contribution we consider the issue of energy efficiency in beamforming applications. Using a convex optimization framework, we propose the design of a virtual beamformer that maximizes the network's lifetime while satisfying a pre-specified Quality of Service (QoS) requirement. A distributed consensus-based algorithm for the computation of the optimal beamformer is also provided

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.