I+D Tecnológico

Estimation

Josep Ivan Atencio — 2026-04-14

The penetration of non-conventional renewable energies for electricity generation, especially solar power plants that use photovoltaic cells, has experienced considerable growth in Latin America and the Caribbean since the beginning of the 21^st century. It is gaining importance in electricity matrix of various countries in the region. The integration of these power plants poses several critical challenges that affect the grid stability and electricity dispatch management due to the intermittency of the main resource used by these plants which is the surface solar irradiance. To address these challenges, various power estimation and/or SSI estimation models based on neural networks such as recurrent neural networks and convolutional neural networks, statistical methods, and hybrid algorithms have been developed. The performance of these models depends intrinsically on the nature of the data input, which includes satellite images, meteorological variables, and power time series. This article presents a review of the current models presented in previous publication, establishing a comparative framework on the origin of the data in the development of solar estimation models.

Design and Validation of ASIC for UART Communication and MEMS Device Control

Diego Alberto Bouche — 2026-04-14

This work addresses the importance of precise digital signal control for the synchronization between a MEMS (Microelectromechanical Systems) device and a spectrometer within a laboratory prototype. The synchronization was implemented using a Field-Programmable Gate Array (FPGA), whose reconfigurability, high precision, small size, and relatively low cost offer significant advantages over alternatives such as microcontrollers or data acquisition systems (DAQ). The prototype configuration is managed from a computer, and communication with the FPGA is carried out through the UART (Universal Asynchronous Receiver/Transmitter) protocol via a USB connection. Since FPGAs do not include built-in libraries, the UART module had to be designed from scratch, and it was successfully implemented in this project. Although the FPGA provides the aforementioned advantages, an additional level of optimization could be achieved through the design of an Application-Specific Integrated Circuit (ASIC), which would further reduce the size of the synchronizing module and its power consumption, critical aspects for a portable system of this scale. This work presents only a preliminary ASIC analysis using open-source software tools, aiming to illustrate the potential benefits of such an improvement, although it is not the main objective at the current stage of the prototype.

Compressive optical architectures for data acquisition: transmissive and reflective cases

Maytee Zambrano — 2026-04-14

The growth of optics and electronics, together with more robust learning algorithms, has driven the implementation of compressive sensing in more economical architectures for data acquisition, such as single-pixel architectures. Taking advantage of these advances, a novel solution applicable to multiple sectors is presented. An example of this is compact hyperspectral sensors, which have low adoption in industry and research due to their high costs. This work is presented as a practical and theoretical tutorial on the design and implementation of single-pixel hyperspectral architectures with compressive sensing. The fundamental theory is detailed, and guidance is provided on the process of constructing two systems: one based on a reflective modulator and the other on a transmissive modulator. Step-by-step instructions are provided on how to integrate these elements to acquire hyperspectral data and reconstruct images. The efficiency of both assemblies was evaluated, obtaining Mean Absolute Error (MAE) values of less than 0.15 when compared to a commercial hyperspectral camera, demonstrating an acceptable average level of accuracy; a Spectral Angle Mapper (SAM) value of less than 0.3, metrics considered appropriate for practical applications in different contexts, such as the analysis of food, materials, or structures. It is confirmed that, following the optical design guidelines and compressive sensing tools presented in this work, it is feasible to develop functional hyperspectral sensors that are low cost and highly accessible.

Implementation of sparse image reconstruction strategies using compressive sensing: optimization algorithms and neural networks

Irianys Murgas — 2026-04-14

Compressive sensing (CS) theory allows for the recovery of signals at a sampling rate lower than that required by the Nyquist-Shannon theorem. This theory has led to the development of single-pixel imaging (SPI) systems, which enable the acquisition of 2D images with one-dimensional sensors using a set of light samples through coded apertures and the appropriate CS optimization algorithm. This work describes the procedure for reconstructing images from a CS-SPI system using conventional optimization algorithms as well as neural network-based algorithms. Their performance is demonstrated for different sampling matrices using the Python programming language and the test image, cameraman, which was adjusted to the inputs required by each algorithm. Five conventional algorithms are presented: OMP, CoSaMP, IRLS, Lasso, and BP, as well as five neural network-based algorithms: DR2, HSCNN-R, AMP-Net, Recon-Net, and IstaxRecon. The performance of these algorithms is presented to guide the selection of the method according to the needs of each CS-SPI application and the characteristics of each algorithm. The results showed that for conventional algorithms, LASSO demonstrated good performance in terms of execution time, and in the case of neural network-based algorithms, the HSCNN-R and IstaxRecon models showed high performance at different sampling rates.

Análisis de prácticas ambientales y propuesta de solución inteligente para la gestión sostenible de residuos

Nila Del Carmen Navarro — 2026-01-31

The objective of this research was to diagnose waste separation and recycling practices among students at the Universidad Iberoamericana de Panamá and propose an intelligent waste management system that promotes sustainable habits on campus. A quantitative and descriptive approach was used, applying a digital questionnaire with a Likert scale from 1 to 5. The survey was disseminated through university communication groups to ensure voluntary participation and anonymity, obtaining data from 129 students from various faculties during the month of October 2025. The results indicate that 72.6% of participants regularly separate waste in their homes, reflecting a moderate commitment to environmental culture. Likewise, the need to strengthen awareness of recycling and waste separation practices on campus was identified. Based on these findings, the implementation of an intelligent waste management system is proposed to facilitate proper waste sorting, improve recycling efficiency, and motivate students to adopt sustainable habits on an ongoing basis. It is concluded that the application of this strategy has the potential to consolidate a stronger environmental culture and optimize waste management at the university, becoming a replicable model for other educational institutions.