Measurements design and phenomena discrimination

The construction of measurements suitable for discriminating signal components produced by phenomena of different types is considered. The required measurements should be capable of cancelling out those signal components which are to be ignored when focusing on a phenomenon of interest. Under the hypothesis that the subspaces hosting the signal components produced by each phenomenon are complementary, their discrimination is accomplished by measurements giving rise to the appropriate oblique projector operator. The subspace onto which the operator should project is selected by nonlinear techniques in line with adaptive pursuit strategies

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Compact conversion and cyclostationary noise modelling of pn junction diodes in low-injection - Part I: Model derivation

Starting from the well known low-injection approximation, a closed form, analytical compact model is derived for the small-signal (SS) and forced quasi-periodic operation of junction diodes. The model determines the small-signal and conversion admittance matrix of the device as a function of the applied (dc or periodic-time varying) bias. Noise characteristics, in both the stationary (SS) and cyclostationary cases, are also evaluated by means of a Green's function approach

Signals on Graphs: Uncertainty Principle and Sampling

In many applications, the observations can be represented as a signal defined over the vertices of a graph. The analysis of such signals requires the extension of standard signal processing tools. In this work, first, we provide a class of graph signals that are maximally concentrated on the graph domain and on its dual. Then, building on this framework, we derive an uncertainty principle for graph signals and illustrate the conditions for the recovery of band-limited signals from a subset of samples. We show an interesting link between uncertainty principle and sampling and propose alternative signal recovery algorithms, including a generalization to frame-based reconstruction methods. After showing that the performance of signal recovery algorithms is significantly affected by the location of samples, we suggest and compare a few alternative sampling strategies. Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to vertex-frequency localization properties.Comment: This article is the revised version submitted to the IEEE Transactions on Signal Processing on May, 2016; first revision was submitted on January, 2016; original manuscript was submitted on July, 2015. The work includes 16 pages, 8 figure

Using Giant Pulses to Measure the Impulse Response of the Interstellar Medium

Giant pulses emitted by PSR B1937+21 are bright, intrinsically impulsive bursts. Thus, the observed signal from a giant pulse is a noisy but direct measurement of the impulse response from the ionized interstellar medium. We use this fact to detect 13,025 giant pulses directly in the baseband data of two observations of PSR B1937+21. Using the giant pulse signals, we model the time-varying impulse response with a sparse approximation method, in which the time dependence at each delay is decomposed in Fourier components, thus constructing a wavefield as a function of delay and differential Doppler shift. We find that the resulting wavefield has the expected parabolic shape, with several diffuse structures within it, suggesting the presence of multiple scattering locations along the line of sight. We also detect an echo at a delay of about 2.4 ms, over 1.5 times the rotation period of the pulsar, which between the two observations moves along the trajectory expected from geometry. The structures in the wavefield are insufficiently sparse to produce a complete model of the system, and hence the model is not predictive across gaps larger than about the scintillation time. Nevertheless, within its range, it reproduces about 75% of the power of the impulse response, a fraction limited mostly by the signal-to-noise ratio of the observations. Furthermore, we show that by deconvolution, using the model impulse response, we can successfully recover the intrinsic pulsar emission from the observed signal.Comment: 14 pages, 8 figures, Accepted for publication in Ap

Fuzzy metrics and fuzzy logic for colour image filtering

El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido. En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente. En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879Palanci

Neural mechanisms of affective instability and cognitive control in substance use

Objective: We explored the impact of affect on cognitive control as this relates to individual differences in affective instability and substance use. Toward this end, we examined how different dimensions of affective instability interact to predict substance misuse and the effect of this on two event-related potential components, the reward positivity and the late positive potential, which are said to reflect the neural mechanisms of reward and emotion processing, respectively. Methods: We recorded the ongoing electroencephalogram from undergraduate students as they navigated two T-maze tasks in search of rewards. One of the tasks included neutral, pleasant, and unpleasant pictures from the International Affective Picture System. Participants also completed several questionnaires pertaining to substance use and personality. Results: A principal components analysis revealed a factor related to affective instability, which we named reactivity. This factor significantly predicted increased substance use. Individuals reporting higher levels of affective reactivity also displayed a larger reward positivity following stimuli with emotional content. Conclusion: The current study uncovered a group of high-risk substance users who were characterized by greater levels of affective reactivity and context-specific increased sensitivity to rewards. Significance: These results help to elucidate the complex factors underlying substance use and may facilitate the creation of individually-tailored treatment programs for those struggling with substance use disorders

A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise