Monte Carlo limit cycle characterization

The fixed point implementation of IIR digital filters usually leads to the appearance of zero-input limit cycles, which degrade the performance of the system. In this paper, we develop an efficient Monte Carlo algorithm to detect and characterize limit cycles in fixed-point IIR digital filters. The proposed approach considers filters formulated in the state space and is valid for any fixed point representation and quantization function. Numerical simulations on several high-order filters, where an exhaustive search is unfeasible, show the effectiveness of the proposed approach.

Independent doubly Adaptive Rejection Metropolis Sampling

Adaptive Rejection Metropolis Sampling (ARMS) is a wellknown MCMC scheme for generating samples from onedimensional target distributions. ARMS is widely used within Gibbs sampling, where automatic and fast samplers are often needed to draw from univariate full-conditional densities. In this work, we propose an alternative adaptive algorithm (IA2RMS) that overcomes the main drawback of ARMS (an uncomplete adaptation of the proposal in some cases), speeding up the convergence of the chain to the target. Numerical results show that IA2RMS outperforms the standard ARMS, providing a correlation among samples close to zero.

An adaptive population importance sampler

Monte Carlo (MC) methods are widely used in signal processing, machine learning and communications for statistical inference and stochastic optimization. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this work, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples generated. The cloud of proposals is adapted by learning from a subset of previously generated samples, in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error and robustness to initialization.

Integration of embedded data processing algorithms inside PAMELA devices

PAMELA (Phased Array Monitoring for Enhanced Life Assessment) SHMTM System is an integrated embedded ultrasonic guided waves based system consisting of several electronic devices and one system manager controller. The data collected by all PAMELA devices in the system must be transmitted to the controller, who will be responsible for carrying out the advanced signal processing to obtain SHM maps. PAMELA devices consist of hardware based on a Virtex 5 FPGA with a PowerPC 440 running an embedded Linux distribution. Therefore, PAMELA devices, in addition to the capability of performing tests and transmitting the collected data to the controller, have the capability of perform local data processing or pre-processing (reduction, normalization, pattern recognition, feature extraction, etc.). Local data processing decreases the data traffic over the network and allows CPU load of the external computer to be reduced. Even it is possible that PAMELA devices are running autonomously performing scheduled tests, and only communicates with the controller in case of detection of structural damages or when programmed. Each PAMELA device integrates a software management application (SMA) that allows to the developer downloading his own algorithm code and adding the new data processing algorithm to the device. The development of the SMA is done in a virtual machine with an Ubuntu Linux distribution including all necessary software tools to perform the entire cycle of development. Eclipse IDE (Integrated Development Environment) is used to develop the SMA project and to write the code of each data processing algorithm. This paper presents the developed software architecture and describes the necessary steps to add new data processing algorithms to SMA in order to increase the processing capabilities of PAMELA devices.An example of basic damage index estimation using delay and sum algorithm is provided.

Extended phone log-likelihood ratio features and acoustic-based I-vectors for language recognition

This paper presents new techniques with relevant improvements added to the primary system presented by our group to the Albayzin 2012 LRE competition, where the use of any additional corpora for training or optimizing the models was forbidden. In this work, we present the incorporation of an additional phonotactic subsystem based on the use of phone log-likelihood ratio features (PLLR) extracted from different phonotactic recognizers that contributes to improve the accuracy of the system in a 21.4% in terms of Cavg (we also present results for the official metric during the evaluation, Fact). We will present how using these features at the phone state level provides significant improvements, when used together with dimensionality reduction techniques, especially PCA. We have also experimented with applying alternative SDC-like configurations on these PLLR features with additional improvements. Also, we will describe some modifications to the MFCC-based acoustic i-vector system which have also contributed to additional improvements. The final fused system outperformed the baseline in 27.4% in Cavg.

Análisis, modelado e igualación no lineal de arrays paramétricos acústicos

La presente Tesis analiza y desarrolla metodología específica que permite la caracterización de sistemas de transmisión acústicos basados en el fenómeno del array paramétrico. Este tipo de estructuras es considerado como uno de los sistemas más representativos de la acústica no lineal con amplias posibilidades tecnológicas. Los arrays paramétricos aprovechan la no linealidad del medio aéreo para obtener en recepción señales en el margen sónico a partir de señales ultrasónicas en emisión. Por desgracia, este procedimiento implica que la señal transmitida y la recibida guardan una relación compleja, que incluye una fuerte ecualización así como una distorsión apreciable por el oyente. Este hecho reduce claramente la posibilidad de obtener sistemas acústicos de gran fidelidad. Hasta ahora, los esfuerzos tecnológicos dirigidos al diseño de sistemas comerciales han tratado de paliar esta falta de fidelidad mediante técnicas de preprocesado fuertemente dependientes de los modelos físicos teóricos. Estos están basados en la ecuación de propagación de onda no lineal. En esta Tesis se propone un nuevo enfoque: la obtención de una representación completa del sistema mediante series de Volterra que permita inferir un sistema de compensación computacionalmente ligero y fiable. La dificultad que entraña la correcta extracción de esta representación obliga a desarrollar una metodología completa de identificación adaptada a este tipo de estructuras. Así, a la hora de aplicar métodos de identificación se hace indispensable la determinación de ciertas características iniciales que favorezcan la parametrización del sistema. En esta Tesis se propone una metodología propia que extrae estas condiciones iniciales. Con estos datos, nos encontramos en disposición de plantear un sistema completo de identificación no lineal basado en señales pseudoaleatorias, que aumenta la fiabilidad de la descripción del sistema, posibilitando tanto la inferencia de la estructura basada en bloques subyacente, como el diseño de mecanismos de compensación adecuados. A su vez, en este escenario concreto en el que intervienen procesos de modulación, factores como el punto de trabajo o las características físicas del transductor, hacen inviables los algoritmos de caracterización habituales. Incluyendo el método de identificación propuesto. Con el fin de eliminar esta problemática se propone una serie de nuevos algoritmos de corrección que permiten la aplicación de la caracterización. Las capacidades de estos nuevos algoritmos se pondrán a prueba sobre un prototipo físico, diseñado a tal efecto. Para ello, se propondrán la metodología y los mecanismos de instrumentación necesarios para llevar a cabo el diseño, la identificación del sistema y su posible corrección, todo ello mediante técnicas de procesado digital previas al sistema de transducción. Los algoritmos se evaluarán en términos de error de modelado a partir de la señal de salida del sistema real frente a la salida sintetizada a partir del modelo estimado. Esta estrategia asegura la posibilidad de aplicar técnicas de compensación ya que éstas son sensibles a errores de estima en módulo y fase. La calidad del sistema final se evaluará en términos de fase, coloración y distorsión no lineal mediante un test propuesto a lo largo de este discurso, como paso previo a una futura evaluación subjetiva. ABSTRACT This Thesis presents a specific methodology for the characterization of acoustic transmission systems based on the parametric array phenomenon. These structures are well-known representatives of the nonlinear acoustics field and display large technological opportunities. Parametric arrays exploit the nonlinear behavior of air to obtain sonic signals at the receptors’side, which were generated within the ultrasonic range. The underlying physical process redunds in a complex relationship between the transmitted and received signals. This includes both a strong equalization and an appreciable distortion for a human listener. High fidelity, acoustic equipment based on this phenomenon is therefore difficult to design. Until recently, efforts devoted to this enterprise have focused in fidelity enhancement based on physically-informed, pre-processing schemes. These derive directly from the nonlinear form of the wave equation. However, online limited enhancement has been achieved. In this Thesis we propose a novel approach: the evaluation of a complete representation of the system through its projection onto the Volterra series, which allows the posterior inference of a computationally light and reliable compensation scheme. The main difficulty in the derivation of such representation strives from the need of a complete identification methodology, suitable for this particular type of structures. As an example, whenever identification techniques are involved, we require preliminary estimates on certain parameters that contribute to the correct parameterization of the system. In this Thesis we propose a methodology to derive such initial values from simple measures. Once these information is made available, a complete identification scheme is required for nonlinear systems based on pseudorandom signals. These contribute to the robustness and fidelity of the resulting model, and facilitate both the inference of the underlying structure, which we subdivide into a simple block-oriented construction, and the design of the corresponding compensation structure. In a scenario such as this where frequency modulations occur, one must control exogenous factors such as devices’ operation point and the physical properties of the transducer. These may conflict with the principia behind the standard identification procedures, as it is the case. With this idea in mind, the Thesis includes a series of novel correction algorithms that facilitate the application of the characterization results onto the system compensation. The proposed algorithms are tested on a prototype that was designed and built for this purpose. The methodology and instrumentation required for its design, the identification of the overall acoustic system and its correction are all based on signal processing techniques, focusing on the system front-end, i.e. prior to transduction. Results are evaluated in terms of input-output modelling error, considering a synthetic construction of the system. This criterion ensures that compensation techniques may actually be introduced, since these are highly sensible to estimation errors both on the envelope and the phase of the signals involved. Finally, the quality of the overall system will be evaluated in terms of phase, spectral color and nonlinear distortion; by means of a test protocol specifically devised for this Thesis, as a prior step for a future, subjective quality evaluation.

Information processing in the human brain: magnetoencephalographic approach.

Rapid progress in effective methods to image brain functions has revolutionized neuroscience. It is now possible to study noninvasively in humans neural processes that were previously only accessible in experimental animals and in brain-injured patients. In this endeavor, positron emission tomography has been the leader, but the superconducting quantum interference device-based magnetoencephalography (MEG) is gaining a firm role, too. With the advent of instruments covering the whole scalp, MEG, typically with 5-mm spatial and 1-ms temporal resolution, allows neuroscientists to track cortical functions accurately in time and space. We present five representative examples of recent MEG studies in our laboratory that demonstrate the usefulness of whole-head magnetoencephalography in investigations of spatiotemporal dynamics of cortical signal processing.

Chemotactic signal integration in bacteria.

Chemotactic signaling in Escherichia coli involves transmission of both negative and positive signals. In order to examine mechanisms of signal processing, behavioral responses to dual inputs have been measured by using photoactivable "caged" compounds, computer video analysis, and chemoreceptor deletion mutants. Signaling from Tar and Tsr, two receptors that sense amino acids and pH, was studied. In a Tar deletion mutant the photoactivated release of protons, a Tsr repellent, and of serine, a Tsr attractant, in separate experiments at pH 7.0 resulted in tumbling (negative) or smooth-swimming (positive) responses in ca. 50 and 140 ms, respectively. Simultaneous photorelease of protons and serine resulted in a single tumbling or smooth-swimming response, depending on the relative amounts of the two effectors. In contrast, in wild-type E. coli, proton release at pH 7.0 resulted in a biphasic response that was attributed to Tsr-mediated tumbling followed by Tar-mediated smooth-swimming. In wild-type E. coli at more alkaline pH values the Tar-mediated signal was stronger than the Tsr signal, resulting in a strong smooth-swimming response preceded by a diminished tumbling response. These observations imply that (i) a single receptor time-averages the binding of different chemotactic ligands generating a single response; (ii) ligand binding to different receptors can result in a nonintegrated response with the tumbling response preceding the smooth-swimming response; (iii) however, chemotactic signals of different intensities derived from different receptors can also result in an apparently integrated response; and (iv) the different chemotactic responses to protons at neutral and alkaline pH may contribute to E. coli migration toward neutrality.

Digital filtering using burst processing techniques /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Nonlinear acoustics, 1954 to 1983.

"Approved for public release, distribution unlimited."

Direct support and general support maintenance manual : translator, signal data CV-3098/G (NSN 5895-00-237-2472) for satellite communication terminal AN/FSC-78(V).

"June 1978."

Proceedings of the Institution of Electrical Engineers.

Vols.1-87,1872-1940 also called no.1-258.

Hydroaccoustic pinging as a diver tool for underwater navigation in surveying Puget Sound Eelgrass (Zostera marina) densities

Senior thesis written for Oceanography 445

The ambient soundscape of inland waters in Seattle, Washington: bridge traffic as a source of urban underwater noise pollution?

Senior thesis written for Oceanography 445

Pitch jump probability measures for the analysis of snoring sounds in apnea

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The gold standard of diagnosis, called polysomnography (PSG), requires a full-night hospital stay connected to over ten channels of measurements requiring physical contact with sensors. PSG is inconvenient, expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. Diagnostic systems intent on using snore-related sounds (SRS) face the tough problem of how to define a snore. In this paper, we present a working definition of a snore, and propose algorithms to segment SRS into classes of pure breathing, silence and voiced/unvoiced snores. We propose a novel feature termed the 'intra-snore-pitch-jump' (ISPJ) to diagnose OSA. Working on clinical data, we show that ISPJ delivers OSA detection sensitivities of 86-100% while holding specificity at 50-80%. These numbers indicate that snore sounds and the ISPJ have the potential to be good candidates for a take-home device for OSA screening. Snore sounds have the significant advantage in that they can be conveniently acquired with low-cost non-contact equipment. The segmentation results presented in this paper have been derived using data from eight patients as the training set and another eight patients as the testing set. ISPJ-based OSA detection results have been derived using training data from 16 subjects and testing data from 29 subjects.