This work releases research progress carried out on carbon quantum dots (CQDs) obtained from agave bagasse with the aim of applying their UV-blue luminescence properties, as a novel way to improve plant growth and protecting them from the solar UV harmful effects. This results in an increasing exposure to blue irradiation, which is avidly absorbed by chlorophyll, thus strengthening plant photosynthesis. The developed green route process is based on the pyrolysis of agave bagasse fibers, a cheap carbon precursor from the tequila’s industries waste. After drying and grinding the fibers, three samples of powdered agave were burned in the open air at 500 °C for 0.5 h, 1.0 h and 2.0 h seeking to synthesize CQDs samples of different sizes. The optical absorption and photoluminescence (PL) response of all samples were similar, that is, a strong UV absorption and a PL peak on the blue region. Furthermore, their highest PL quantum yield of 15.74% was obtained for the 2 h pyrolysis time sample that was applied on acrylic sheets in a greenhouse experiment. The groups of plants with this cover had faster germination rates and better plant growth rates than two control groups without CQDs.

In this work we propose a method for sub-segmentation of images using the PFCM clustering algorithm. The sub-segmentation consists of finding, within the clusters found using the segmentation process, those data less representative, or atypical data, belonging to the clusters. These data represent, in many cases, the zones of interest during image analysis. Two different examples are used in order to show the results, and the advantages of identifying those elements of data forced to belong to a cluster, of which they are the less representative and, therefore may contain information of great interest in particular applications.

This paper presents the design of an artificial vision system prototype for automatic inspection and monitoring of objects over a conveyor belt and using a Smart camera 2D BOA-INS. The prototype consists of a conveyor belt and an embedded system based on an Arduino Mega card for system control, and it has as main peripherals the smart camera, a direct current motor, a photoelectric sensor, LED illumination and LEDs indicating the status (good or defect) of each evaluated object. The application of the prototype is for educational purposes, so that undergraduate, master and diploma students can simulate a continuous production line, controlled by an embedded system, and perform quality control by monitoring through a visual system and a personal computer. This allows implementing the topics of embedded systems, artificial vision, artificial intelligence, pattern recognition, automatic control, as well as automation of real processes.

This paper describes the synthesis and characterization of Si and CdTe quantum dots (QDs) and their use, either on their own or combined, as photoluminescent (PL) down-shifting nanostructured coatings aimed to enhance the photovoltaic efficiency of polycrystalline silicon solar cells. To this end, the front face of a set of silicon cells was coated with different volume ratios of the above-mentioned QDs, or some of its mixtures, dispersed in PMMA layers. Previously, the absorption and the PL (exc = 380 nm) response of the dispersions of the QDs were measured. It was observed that the PL response of the mixtures was strongly affected in location, spread, and intensity of the emission peak according to the volume ratio involved. As compared to the unmixed CdTe samples, a notorious red-shift of the main peak location was obtained for a couple of mixed QDs’ dispersions, which was one of the project objectives given that Si solar cells respond better to photons with wavelengths in the 650–700 nm range. This effect was confirmed in a set of polycrystalline Si solar cells covered with and without nanostructured PMMA/QDs layers tested under AM 1.5G solar simulator conditions. It was found that the use of the proposed mixtures of QDs gave an increase of 1.53% in solar cell power conversion efficiency.

We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM) is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means) and an absolute typicality (typicality value, Possibilistic c-means). Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find.