Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.

This work presents an electronic sensing approach composed of a pair of Physical–Chemical and Imaging modules to preserve an aquaponic system. These modules offer constant measurements of the physical–chemical characteristics within the fish tank and the grow bed, and an indication of the health of the growing plants through image processing techniques. This proposal is implemented in a low-cost computer, receiving measurements from five sensors, including a camera, and processing the signals using open-source libraries and software. Periodic measurements of the temperature, water level, light, and pH within the system are collected and shared to a cloud platform that allows their display in a dashboard, accessible through a web page. The health of the vegetables growing in the system is estimated by analyzing visible and infrared spectra, applying feature extraction, and computing vegetation indices. This work provides a low-cost solution for preserving sustainable urban farming systems, suitable for new farming communities.

In this work, we generate optical vortices from the supercontinuum output of an ultrafast laser interacting with a micro-structured fiber. Using a segmented spatial light modulator, multiple vortices are designed and dynamically generated and shifted in order to observe their superposition in the image plane. It is shown that single-color patterns of exquisite complexity can be generated across a wide frequency range. Multi-color interference patterns are experimentally generated and compared to the results of computer simulations. Multiple vortices of varying colors are also generated and independently controlled, demonstrating that no spatial interference occurs. Experimental results are compared with theoretical and numerical simulations, showing excellent agreement.

We present an approach for engineering the intensity trajectory and phase gradient of light beams with arbitrary shapes by estimating their parametric equations using Freeman chain code and by applying the fast Fourier transform. The analysis of the electric field distribution expected for a given curve allows the phase extraction over each local coordinate, generating a phase pattern to be displayed over a spatial light modulator. The intensity and phase gradient of eight different shapes is encoded during our experiments. The far field intensity profiles are captured and compared in shape to those designed, while the encoded phase is demonstrated by implementing a common path interference setup with a pair of beams from the spatial light modulator. The designed beams, initially drawn either by hand or generated with software, exhibit both the intensity and phase profiles encoded onto them.

A computational comparison between analytical and discrete image-based formulations for contour hologram synthesis is presented. The analytical approach relies on contour parameterization and Fourier-descriptor reconstruction to compute the phase accumulated along a closed contour, while the proposed discrete image-based approach performs contour extraction and phase accumulation directly on sampled spatial grids without explicit parametric modeling. Both methods were implemented under identical computational conditions and evaluated through numerical propagation in the Fraunhofer regime using representative contour geometries of increasing spatial complexity. Reconstruction quality was assessed using the Structural Similarity Index (SSIM), phase topology analysis, and hologram generation time measurements. Results show that both approaches successfully reconstruct the target contours while preserving the prescribed topological charge and phase winding behavior. High structural fidelity was obtained in all cases, with SSIM values above 0.93. The discrete image-based approach consistently produced slightly higher similarity values and achieved substantial reductions in computational cost, with speed-up factors ranging from approximately 120× to more than 1600×, depending on contour complexity. These results demonstrate that direct operation on sampled contour representations provides an efficient alternative to analytical contour parameterization while maintaining reconstruction quality and topological consistency.