Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Use of the First Derivative of Spectral Reflectance to Detect Mold on Tomatoes

The first derivative of spectral reflectance of tomatoes was studied to see whether it can detect molds and sunscald damage on the fruit. The results indicate that a quality index based on the derivative values of reflectance at 590- and 710-nm wavelengths can be used to separate good tomates from those with black mold, gray mold, and sunscald.

A low-cost alternative scheme to detect a 100 Gbps PM-DQPSK signal

We propose and demonstrate a low-cost alternative scheme of direct-detection to detect a 100Gbps polarization-multiplexed differential quadrature phase-shift keying (PM-DQPSK) signal. The proposed scheme is based on a delay line and a polarization rotator; the phase-shift keying signal is first converted into a polarization shift keying signal. Then, this signal is converted into an intensity modulated signal by a polarization beam splitter. Finally, the intensity-modulated signal is detected by balanced photodetectors. In order to demonstrate that our proposed receiver is suitable for using as a PM-DQPSK demodulator, a set of simulations have been performed. In addition to testing the sensitivity, the performance under various impairments, including narrow optical filtering, polarization mode dispersion, chromatic dispersion and polarization sensitivity, is analyzed. The simulation results show that our performance receiver is as good as a conventional receiver based on four delay interferometers. Moreover, in comparison with the typical receiver, fewer components are used in our receiver. Hence, implementation is easier, and total cost is reduced. In addition, our receiver can be easily improved to a bit-rate tunable receiver.

Star dust memories, or how to detect allergen traces in powder foods

Allergies and food intolerances are at the forefront of institutional interest (European Regulation No 1169/2011) for their impact on consumer health. Allergies to peanuts and other nuts and gluten intolerance, makes production processes involving mixtures of powders a great concern for the industry, given the need to indicate the existence of traces of any of them. The food industry requires non-destructive and non-invasive methods of quantification that meet sensitivity requirements but also specificity levels. Optical methods such as NIR spectrophotometry or hyper-spectral image are currently some of the technologies that show potential success. This is the context of this paper that evaluates how to use NIR spectroscopy (900-1600nm) to detect traces of 15 different kinds of nuts and 20 other flours.