We use the regularization theory in a Bayesian framework to derive a quadratic cost function for denoising fringe patterns. As prior constraints for the regularization problem, we propose a Markov random field model that includes information about the fringe orientation. In our cost function the regularization term imposes constraints to the solution (i.e., the filtered image) to be smooth only along the fringe’s tangent direction. In this way as the fringe information and noise are conveniently separated in the frequency space, our technique avoids blurring the fringes. The attractiveness of the proposed filtering method is that the minimization of the cost function can be easily implemented using iterative methods. To show the performance of the proposed technique we present some results obtained by processing simulated and real fringe patterns.

Recent studies have demonstrated that the phase recovery from a single fringe pattern with closed fringes can be properly performed if the modulo $2\pi$ fringe orientation is estimated. For example, the fringe pattern in quadrature can be efficiently obtained in terms of the orientational phase spatial operator using fast Fourier transformations and a spiral phase spectral operator in the Fourier space. The computation of the modulo $2\pi$ fringe orientation, however, is by far the most difficult task in the global process of phase recovery. For this reason we propose the demodulation of fringe patterns with closed fringes through the computation of the modulo $2\pi$ fringe orientation using an orientational vector-field-regularized estimator. As we will show, the phase recovery from a single pattern can be performed in an efficient manner using this estimator, provided that it requires one to solve locally in the fringe pattern a simple linear system to optimize a regularized cost function. We present simulated and real experiments applying the proposed methodology.

The effects of pulsed light based-LEDs at eleven frequencies (0.1, 1, 10, 50, 100, 500 Hz, 1, 5, 10, 50 and 100 kHz) programmed at 50 % duty cycle were analyzed, obtaining important parameters of the fluorescence emission of chlorophyll such as: maximum fluorescence (Fm′), minimum fluorescence, the fluorescence emission in steady state, maximum efficiency of PSII (Fv′/Fm′), the fraction of PSII centers that are open, photochemical quenching, nonphotochemical quenching (NPQ), quantum efficiency of photosystem II (ΦPSII), electron transport rate (ETR) and quantum yield of CO₂ assimilation (ϕCO₂). For the study and validation of the results obtained in the experiments, the analysis of variance (ANOVA) was applied 0for each parameter with confidence intervals of 95 %. The results show that the frequencies of pulsed light had positive and negative effects on the fluorescence parameters with respect to the control treatment (continuous light). The frequencies that generated the best performance of Fv′/Fm′, NPQ, ΦPSII, ETR, ϕCO₂ in tomato plants were 0.1, 1, 100 Hz, and 1 kHz. The increase obtained in these parameters can represent an optimal growth and productivity conditions for optimal energy consumption.

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.