Um modelo matemático baseado em wavelets para análise do método térmico de recuperação de óleo pesado aplicando irradiação eletromagnética

This work proposes a model to investigate the use of a cylindrical antenna used in the thermal method of recovering through electromagnetic radiation of high-viscosity oil. The antenna has a simple geometry, adapted dipole type, and it can be modelled by using Maxwell s equation. The wavelet transforms are used as basis functions and applied in conjunction with the method of moments to obtain the current distribution in the antenna. The electric field, power and temperature distribution are carefully calculated for the analysis of the antenna as electromagnetic heating. The energy performance is analyzed based on thermo-fluid dynamic simulations at field scale, and through the adaptation in the Steam Thermal and Advanced Processes Reservoir Simulator (STARS) by Computer Modelling Group (CMG). The model proposed and the numerical results obtained are stable and presented good agreement with the results reported in the specialized literature

Análisis predictivo para clasificar dígitos escritos a mano utilizando la base de datos MNIST

En el presente trabajo de fin de máster se realiza una investigación sobre las técnicas de preproceso del dataset de entrenamiento y la aplicación de un modelo de predicción que realice una clasificación de dı́gitos escritos a mano. El conjunto de dataset de train y test son proporcionado en la competencia de Kaggle: Digit Recognizer y provienen de la base de datos de dı́gitos manuscritos MNIST. Por tratarse de imágenes las técnicas de preproceso se concentran en obtener una imagen lo más nı́tida posible y la reducción de tamaño de la misma, objetivos que se logran con técnicas de umbralización por el método de Otsu, transformada de Wavelet de Haar y el análisis de sus componentes principales. Se utiliza Deep Learning como modelo predictivo por ajustarse a este tipo de datos, se emplean además librerı́as de código abierto implementadas en el lenguaje estádisto R. Por último se obtiene una predicción con las técnicas y herramientas mencio- nadas para ser evaluada en la competencia de Kaggle, midiendo y comparando los resultados obtenidos con el resto de participantes.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

A mobile image enhancement technology for visually impaired

In this thesis, an image enhancement application is developed for low-vision patients when they use iPhones to see images/watch videos. The thesis has two contributions. The first contribution is the new image enhancement algorithm which combines human vision features. The new image enhancement algorithm is modified from a wavelet transform based image enhancement algorithm developed by Dr. Jinshan Tang. Different from the original algorithm, the new image enhancement algorithm combines human visual feature into the algorithm and thus can make the new algorithm more effective. Experimental simulation results show that the proposed algorithm has better visual results than the algorithm without combining visual features. The second contribution of this thesis is the development of a mobile image enhancement application. In this application, users with low-vision can see clearer images on an iPhone which is installed with the application I have developed.

The Nature of Microvariability in Blazar 0716+714

We organized an international campaign to observe the blazar 0716+714 in the optical band. The observations took place from February 24, 2009 to February 26, 2009. The global campaign was carried out by observers from more that sixteen countries and resulted in an extended light curve nearly seventy-eight hours long. The analysis and the modeling of this light curve form the main work of this dissertation project. In the first part of this work, we present the time series and noise analyses of the data. The time series analysis utilizes discrete Fourier transform and wavelet analysis routines to search for periods in the light curve. We then present results of the noise analysis which is based on the idea that each microvariability curve is the realization of the same underlying stochastic noise processes in the blazar jet. Neither reoccuring periods nor random noise can successfully explain the observed optical fluctuations. Hence in the second part, we propose and develop a new model to account for the microvariability we see in blazar 0716+714. We propose that the microvariability is due to the emission from turbulent regions in the jet that are energized by the passage of relativistic shocks. Emission from each turbulent cell forms a pulse of emission, and when convolved with other pulses, yields the observed light curve. We use the model to obtain estimates of the physical parameters of the emission regions in the jet.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

Singularity and multifractal characterization of signals with wavelets. Application to clay soil images

In the field of multiscale analysis of signals, including images, the wavelet transform is one of the most attractive and powerful tool due to its ability to focus on signals structures at different scales. Wavelet Transform at different scales is successfully applied to image characterization (which can be applied to a watermarking scheme) and multiscale singularity detection and processing. In this work we show further research of computation of multifractals properties such as the multifractal spectrum (D(alpha)) applied to dye stained images of natural terrain. This can be useful for statically describing preferential flow path geometry.

Long-period oscillations in GPS Up time series. Study over the European/Mediterranean area

The surface of the Earth is subjected to vertical deformations caused by geophysical and geological processes which can be monitored by Global Positioning System (GPS) observations. The purpose of this work is to investigate GPS height time series to identify interannual signals affecting the Earth’s surface over the European and Mediterranean area, during the period 2001-2019. Thirty-six homogeneously distributed GPS stations were selected from the online dataset made available by the Nevada Geodetic Laboratory (NGL) on the basis of the length and quality of the data series. The Principal Component Analysis (PCA) is the technique applied to extract the main patterns of the space and time variability of the GPS Up coordinate. The time series were studied by means of a frequency analysis using a periodogram and the real-valued Morlet wavelet. The periodogram is used to identify the dominant frequencies and the spectral density of the investigated signals; the second one is applied to identify the signals in the time domain and the relevant periodicities. This study has identified, over European and Mediterranean area, the presence of interannual non-linear signals with a period of 2-to-4 years, possibly related to atmospheric and hydrological loading displacements and to climate phenomena, such as El Niño Southern Oscillation (ENSO). A clear signal with a period of about six years is present in the vertical component of the GPS time series, likely explainable by the gravitational coupling between the Earth’s mantle and the inner core. Moreover, signals with a period in the order of 8-9 years, might be explained by mantle-inner core gravity coupling and the cycle of the lunar perigee, and a signal of 18.6 years, likely associated to lunar nodal cycle, were identified through the wavelet spectrum. However, these last two signals need further confirmation because the present length of the GPS time series is still too short when compared to the periods involved.

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.

“Event-Related Spectral Perturbations” nel segnale EEG durante un compito di reaching

Questo elaborato ha analizzato le Event Related Spectral Perturbation (ERSP), cioè le variazioni nella potenza del segnale in corrispondenza di una frequenza o una banda di frequenze, suddivisi in Event Related Synchronization (ERS) nel caso di incremento di potenza e Event Related Desynchronization (ERD) nel caso di decremento di potenza, relative a movimenti di reaching eseguiti con l’arto superiore. In particolare, sono state prese in considerazione le potenze nel ritmo alpha (8-12 Hz) e ritmo beta (12-30 Hz), in quanto ritmi associati alla preparazione ed esecuzione del movimento. I segnali EEG analizzati (60 canali) sono relativi a 14 soggetti a cui è stato chiesto di compiere movimenti di reaching verso una posizione target, secondo una tempistica definita dall’accensione di uno stimolo visivo informativo (CUE, corrispondente al led nella posizione target) e un secondo stimolo visivo imperativo (GO) che dà il via libera all’esecuzione del movimento verso il target. Sono codificate 5 posizioni discrete, a 0°, 45°, 90°, 135° e 180°, su una semicirconferenza il cui centro rappresenta il punto di partenza e ritorno, chiamato base. I segnali preventivamente preprocessati, sono stati decomposti tramite trasformata wavelet ed elaborati con la tecnica dell’averaging, con lo scopo di evidenziare ERS e ERD in banda alpha e beta associati a eventi visivi e motori. È stata presentata l’evoluzione nel tempo della distribuzione topologica delle ERSP a livello dello scalpo e sono state svolte analisi suddividendo i dati secondo la direzione (destra, centro, sinistra) del target e secondo la profondità (vicino, medio, lontano) del target. I risultati mostrano una desincronizzazione (ERD) nella fase di preparazione del movimento (immediatamente prima dello stimolo GO), sia in banda alpha che beta e suggeriscono una modulazione dell’ERD in funzione della direzione/profondità del target.