On the reliability of visual communication in vertebrate-dispersed fruits

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Harmonics filtering and detection of disturbances using wavelets

Traditional mathematical tools, like Fourier Analysis, have proven to be efficient when analyzing steady-state distortions; however, the growing utilization of electronically controlled loads and the generation of a new dynamics in industrial environments signals have suggested the need of a powerful tool to perform the analysis of non-stationary distortions, overcoming limitations of frequency techniques. Wavelet Theory provides a new approach to harmonic analysis, focusing the decomposition of a signal into non-sinusoidal components, which are translated and scaled in time, generating a time-frequency basis. The correct choice of the waveshape to be used in decomposition is very important and discussed in this work. A brief theoretical introduction on Wavelet Transform is presented and some cases (practical and simulated) are discussed. Distortions commonly found in industrial environments, such as the current waveform of a Switched-Mode Power Supply and the input phase voltage waveform of motor fed by inverter are analyzed using Wavelet Theory. Applications such as extracting the fundamental frequency of a non-sinusoidal current signal, or using the ability of compact representation to detect non-repetitive disturbances are presented.

Prediseño de modelos a escala para el estudio de fenómenos dinámicos de interacción suelo-estructura en aerogeneradores off-shore sobre monopilotes

Máster Universitario en Eficiencia Energética (SIANI)

Generating Realistic Camera Shake for Virtual Scenes

When depicting both virtual and physical worlds, the viewer's impression of presence in these worlds is strongly linked to camera motion. Plausible and artist-controlled camera movement can substantially increase scene immersion. While physical camera motion exhibits subtle details of position, rotation, and acceleration, these details are often missing for virtual camera motion. In this work, we analyze camera movement using signal theory. Our system allows us to stylize a smooth user-defined virtual base camera motion by enriching it with plausible details. A key component of our system is a database of videos filmed by physical cameras. These videos are analyzed with a camera-motion estimation algorithm (structure-from-motion) and labeled manually with a specific style. By considering spectral properties of location, orientation and acceleration, our solution learns camera motion details. Consequently, an arbitrary virtual base motion, defined in any conventional animation package, can be automatically modified according to a user-selected style. In an animation package the camera motion base path is typically defined by the user via function curves. Another possibility is to obtain the camera path by using a mixed reality camera in motion capturing studio. As shown in our experiments, the resulting shots are still fully artist-controlled, but appear richer and more physically plausible.

Increased availability and scalability for clustered services via the wait time calculation, trust based filtering and redirection of TCP connection requests

The paper describes two new transport layer (TCP) options and an expanded transport layer queuing strategy that facilitate three functions that are fundamental to the dispatching-based clustered service. A transport layer option has been developed to facilitate. the use of client wait time data within the service request processing of the cluster. A second transport layer option has been developed to facilitate the redirection of service requests by the cluster dispatcher to the cluster processing member. An expanded transport layer service request queuing strategy facilitates the trust based filtering of incoming service requests so that a graceful degradation of service delivery may be achieved during periods of overload - most dramatically evidenced by distributed denial of service attacks against the clustered service. We describe how these new options and queues have been implemented and successfully tested within the transport layer of the Linux kernel.

Confidence measurement in the light of signal detection theory

We compare three alternative methods for eliciting retrospective confidence in the context of a simple perceptual task: the Simple Confidence Rating (a direct report on a numerical scale), the Quadratic Scoring Rule (a post-wagering procedure), and the Matching Probability (MP; a generalization of the no-loss gambling method). We systematically compare the results obtained with these three rules to the theoretical confidence levels that can be inferred from performance in the perceptual task using Signal Detection Theory (SDT). We find that the MP provides better results in that respect. We conclude that MP is particularly well suited for studies of confidence that use SDT as a theoretical framework.

Theory and generalization of Monte Carlo models of the BOLD signal source

The dependency of the blood oxygenation level dependent (BOLD) signal on underlying hemodynamics is not well understood. Building a forward biophysical model of this relationship is important for the quantitative estimation of the hemodynamic changes and neural activity underlying functional magnetic resonance imaging (fMRI) signals. We have developed a general model of the BOLD signal which can model both intra- and extravascular signals for an arbitrary tissue model across a wide range of imaging parameters. The model of the BOLD signal was instantiated as a look-up-table (LuT), and was verified against concurrent fMRI and optical imaging measurements of activation induced hemodynamics. Magn Reson Med, 2008. © 2008 Wiley-Liss, Inc.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

Signal or Noise? Information Theory and the Novel

In the context of a hostile funding environment, universities are increasingly asked to justify their output in narrowly defined economic terms, and this can be difficult in Humanities or Arts faculties where productivity is rarely reducible to a simple financial indicator. This can lead to a number of immediate consequences that I have no need to rehearse here, but can also result in some interesting tensions within the academic community itself. First is that which has become known as the ‘Science Wars’: the increasingly acrimonious exchanges between scientists and scientific academics and cultural critics or theorists about who has the right to describe the world. Much has already been said—and much remains to be said—about this issue, but it is not my intention to discuss it here. Rather, I will look at a second area of contestation: the incorporation of scientific theory into literary or cultural criticism. Much of this work comes from a genuine commitment to interdisciplinarity, and an appreciation of insights that a fresh perspective can bring to a familiar object. However, some can be seen as cynical attempts to lend literary studies the sort of empirical legitimacy of the sciences. In particular, I want to look at a number of critics who have applied information theory to the literary work. In this paper, I will examine several instances of this sort of criticism, and then, through an analysis of a novel by American author Richard Powers, Three Farmers on Their Way to a Dance, show how this sort of criticism merely reduces the meaningful analysis of a complex literary text.

Signal detection theory in applied tasks: The case of hazard perception in driving

Fuzzy signal detection analysis can be a useful complementary technique to traditional signal detection theory analysis methods, particularly in applied settings. For example, traffic situations are better conceived as being on a continuum from no potential for hazard to high potential, rather than either having potential or not having potential. This study examined the relative contribution of sensitivity and response bias to explaining differences in the hazard perception performance of novices and experienced drivers, and the effect of a training manipulation. Novice drivers and experienced drivers were compared (N = 64). Half the novices received training, while the experienced drivers and half the novices remained untrained. Participants completed a hazard perception test and rated potential for hazard in occluded scenes. The response latency of participants to the hazard perception test replicated previous findings of experienced/novice differences and trained/untrained differences. Fuzzy signal detection analysis of both the hazard perception task and the occluded rating task suggested that response bias may be more central to hazard perception test performance than sensitivity, with trained and experienced drivers responding faster and with a more liberal bias than untrained novices. Implications for driver training and the hazard perception test are discussed.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.