Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Data-driven Signal Processing per Jamming Detection in Sistemi Satellitari

In questo elaborato vengono analizzate differenti tecniche per la detection di jammer attivi e costanti in una comunicazione satellitare in uplink. Osservando un numero limitato di campioni ricevuti si vuole identificare la presenza di un jammer. A tal fine sono stati implementati i seguenti classificatori binari: support vector machine (SVM), multilayer perceptron (MLP), spectrum guarding e autoencoder. Questi algoritmi di apprendimento automatico dipendono dalle features che ricevono in ingresso, per questo motivo è stata posta particolare attenzione alla loro scelta. A tal fine, sono state confrontate le accuratezze ottenute dai detector addestrati utilizzando differenti tipologie di informazione come: i segnali grezzi nel tempo, le statistical features, le trasformate wavelet e lo spettro ciclico. I pattern prodotti dall’estrazione di queste features dai segnali satellitari possono avere dimensioni elevate, quindi, prima della detection, vengono utilizzati i seguenti algoritmi per la riduzione della dimensionalità: principal component analysis (PCA) e linear discriminant analysis (LDA). Lo scopo di tale processo non è quello di eliminare le features meno rilevanti, ma combinarle in modo da preservare al massimo l’informazione, evitando problemi di overfitting e underfitting. Le simulazioni numeriche effettuate hanno evidenziato come lo spettro ciclico sia in grado di fornire le features migliori per la detection producendo però pattern di dimensioni elevate, per questo motivo è stato necessario l’utilizzo di algoritmi di riduzione della dimensionalità. In particolare, l'algoritmo PCA è stato in grado di estrarre delle informazioni migliori rispetto a LDA, le cui accuratezze risentivano troppo del tipo di jammer utilizzato nella fase di addestramento. Infine, l’algoritmo che ha fornito le prestazioni migliori è stato il Multilayer Perceptron che ha richiesto tempi di addestramento contenuti e dei valori di accuratezza elevati.

Spatial filtering for pilot-aided WCDMA systems: a semi-blind subspace approach

This paper proposes a spatial filtering technique forthe reception of pilot-aided multirate multicode direct-sequencecode division multiple access (DS/CDMA) systems such as widebandCDMA (WCDMA). These systems introduce a code-multiplexedpilot sequence that can be used for the estimation of thefilter weights, but the presence of the traffic signal (transmittedat the same time as the pilot sequence) corrupts that estimationand degrades the performance of the filter significantly. This iscaused by the fact that although the traffic and pilot signals areusually designed to be orthogonal, the frequency selectivity of thechannel degrades this orthogonality at hte receiving end. Here,we propose a semi-blind technique that eliminates the self-noisecaused by the code-multiplexing of the pilot. We derive analyticallythe asymptotic performance of both the training-only andthe semi-blind techniques and compare them with the actual simulatedperformance. It is shown, both analytically and via simulation,that high gains can be achieved with respect to training-onlybasedtechniques.

Enhanced MDIR receiver for space-time dispersive channels

A particular property of the matched desiredimpulse response receiver is introduced in this paper, namely,the fact that full exploitation of the diversity is obtained withmultiple beamformers when the channel is spatially and timelydispersive. This particularity makes the receiver specially suitablefor mobile and underwater communications. The new structureprovides better performance than conventional and weightedVRAKE receivers, and a diversity gain with no needs of additionalradio frequency equipment. The baseband hardware neededfor this new receiver may be obtained through reconfigurabilityof the RAKE architectures available at the base station. Theproposed receiver is tested through simulations assuming UTRAfrequency-division-duplexing mode.

Conditional maximum likelihood timing recovery: estimators and bounds

This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical low-SNR unconditional ML (UML) formulationthat is systematically applied in the literature for the derivationof non-data-aided (NDA) timing-error-detectors (TEDs). A new CML TED is derived and proved to be self-noise free, in contrast to the conventional low-SNR-UML TED. In addition, the paper provides a derivation of the conditional Cramér–Rao Bound (CRB ), which is higher (less optimistic) than the modified CRB (MCRB)[which is only reached by decision-directed (DD) methods]. It is shown that the CRB is a lower bound on the asymptotic statisticalaccuracy of the set of consistent estimators that are quadratic with respect to the received signal. Although the obtained boundis not general, it applies to most NDA synchronizers proposed in the literature. A closed-form expression of the conditional CRBis obtained, and numerical results confirm that the CML TED attains the new bound for moderate to high Eg/No.

Robust beamforming for interference rejection in mobile communications

The problem of robust beamformer design for mobile communicationsapplications in the presence of moving co-channel sources isaddressed. A generalization of the optimum beamformer based on a statisticalmodel accounting for source movement is proposed. The new methodis easily implemented and is shown to offer dramatic improvements overconventional optimum beamforming for moving sources under a varietyof operating conditions.

Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoramento da dressagem na retificação através do sinal puro de emissão acústica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.

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.

Fast Transforms for Acoustic Imaging-Part I: Theory

The classical approach for acoustic imaging consists of beamforming, and produces the source distribution of interest convolved with the array point spread function. This convolution smears the image of interest, significantly reducing its effective resolution. Deconvolution methods have been proposed to enhance acoustic images and have produced significant improvements. Other proposals involve covariance fitting techniques, which avoid deconvolution altogether. However, in their traditional presentation, these enhanced reconstruction methods have very high computational costs, mostly because they have no means of efficiently transforming back and forth between a hypothetical image and the measured data. In this paper, we propose the Kronecker Array Transform ( KAT), a fast separable transform for array imaging applications. Under the assumption of a separable array, it enables the acceleration of imaging techniques by several orders of magnitude with respect to the fastest previously available methods, and enables the use of state-of-the-art regularized least-squares solvers. Using the KAT, one can reconstruct images with higher resolutions than was previously possible and use more accurate reconstruction techniques, opening new and exciting possibilities for acoustic imaging.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.

Noise reduction in speech signals using a TMS320C31 digital signal processor

This paper describes a speech enhancement system (SES) based on a TMS320C31 digital signal processor (DSP) for real-time application. The SES algorithm is based on a modified spectral subtraction method and a new speech activity detector (SAD) is used. The system presents a medium computational load and a sampling rate up to 18 kHz can be used. The goal is load and a sampling rate up to 18 kHz can be used. The goal is to use it to reduce noise in an analog telephone line.