In this letter, we derive a novel exact expression for the probability of false alarm (PFA) and an approximate closed-form solution for the probability of detection (PD) of a smallest of cell-averaging constant false alarm rate (SOCA-CFAR) detector operating over Weibull-distributed clutter. For the analysis, we consider an exponentially distributed target and allow arbitrary values for the shape parameter of clutter interference. To the best of our knowledge, there are no exact or approximate performance evaluations for an SOCA-CFAR detector considering arbitrary values for the shape parameter of the Weibull interference samples (i.e., different from 1) contained within the CFAR window. Therefore, our analytical derivations generalize previous performance evaluation studies and take a small step toward a better understanding of more realistic SOCA-CFAR detectors. Moreover, we obtain exact formulations for the probability density function (PDF) and the cumulative distribution function (CDF) for a minimum of two sums of independent and identically distributed (i.i.d.) Weibull random variables. Numerical results indicate that the system performance improves as the shape parameter of the Weibull interference increases. The validity of all our expressions is confirmed via Monte Carlo simulations.

An automatic recognizer system based in Artificial Intelligence for thermographic images of the electric power distribution network is proposed in this article. The infrared thermography is usually used to conduct inspections in electrical power distribution lines, assisted by a human operator, which is usually responsible for operating all the equipment, selecting the hottest spots in the image (corresponding to the places needing maintenance), making reports and calling the technical team, which will do the repairs. The proposed automatic diagnosis system aims to replace the manual inspection operation using image processing algorithms. An old method of segmentation for thermal images known as JSEG is implemented and tested and a Deep Learning Neural Network is responsible to recognize the segmented elements. A comparison between the exclusive Deep Learning based image recognition with the same method preceded by the JSEG segmentation algorithm is done in this article, showing better performance with this previous segmentation of the thermographic images.

The non-invasive visualization of retinal microvasculature is a traditional way to diagnose and predict some diseases. The region of foveal avascular zone (FAZ) is an important tool to quantify the macular ischemic with implications in the visual acuity. In this work, we proposed an automatic segmentation of FAZ using high-resolution retinal images captured by a retinography conjugated to Retinal Function Imager (RFI) apparatus. The pixels corresponding to FAZ are automatically detected, segmented and measured (area, perimeter, height and width). The proposed algorithm was tested with 20 images of 10 healthy volunteers, and the results were compared to three manual segmentations. The difference between the software and the manual segmentation was 12.8930% (the mean difference between humans was 7.8288%), and the time of each image segmentation was performed 14.1549 times faster by the computer. The method obtained accuracy of 0.9947, sensitivity of 0.8442, and specificity of 0.9972.

The excellent visual quality and compression rate of fractal image coding have limited applications due to exhaustive inherent encoding time. This paper presents a new fast and efficient image coder that applies the speed of the wavelet transform to the image quality of the fractal compression. Fast fractal encoding using Fisher's domain classification is applied to the lowpass subband of wavelet transformed image and a modified set partitioning in hierarchical trees (SPIHT) coding, on the remaining coefficients. Furthermore, image details and wavelet progressive transmission characteristics are maintained, no blocking effects from fractal techniques are introduced, and the encoding fidelity problem common in fractal-wavelet hybrid coders is solved. The proposed scheme promotes an average of 94% reduction in encoding-decoding time comparing to the pure accelerated Fractal coding results. The simulations also compare the results to the SPIHT wavelet coding. In both cases, the new scheme improves the subjective quality of pictures for high-medium-low bitrates.

The demodulation quality of a Pal-M television receiver can be improved by eliminating interference in the chrominance signal due to the luminance signal. The paper presents a simple technique to achieve that using an additional simple delay filter.>