Optical fiber sensors (OFSs) have emerged as essential tools in the monitoring of physical, chemical, and bio-medical parameters in harsh situations due to their high sensitivity, electromagnetic interference (EMI) immunity, and long-term stability. However, the current literature contains scattered information in most reviews regarding individual sensing technologies or domains. This study provides a structured exploratory review in a novel inter-family analysis of both intrinsic and extrinsic configurations by analyzing more than 23,000 publications between 2019 and 2025 in five key domains: industry, medicine and biomedicine, environmental chemistry, civil/structural engineering, and aerospace. The analysis aims to critically discuss how functional principles/parameters and methods of interrogation affect the applicability of different OFS categories. The results reveal leading trends in the use of techniques like the use of fiber Bragg gratings (FBG) and distributed sensing in high-accuracy conditions or the rising role of extrinsic sensors in selective chemical situations and point out new approaches in areas like Artificial Intelligence (AI)- or Internet of Things (IoT)-integrated sensors. Further, this synthesis not only connects pieces of knowledge but also defines the technological barriers in terms of calibration cost and standardization: this provides strategic insight regarding future research and the scalability of industry deployment.

The use of sensors to detect or measure ripening changes in fruit is a growing area of interest to the scientific community. Colorimeters are commonly employed for color and shade identification; however, their usage to measure the color parameters of fruit rinds based on image analysis can be expensive. This article presents a non-invasive and low-cost optoelectronic system for detecting color changes in oranges to predict the ripening stage. The system utilises a 1 W white LED as a light source, an RGB photodiode array, and two plastic optical fibres bundled in parallel to form the head of an extrinsic sensor. A microcontroller is employed for model integration and data acquisition. The evolution of the skin color of the fruit was monitored until over-ripeness was evident. The sensor was designed to detect the color changes; the CIE L * a * b * color difference between the optoelectronic device results and those obtained by colorimetry was 2.6–4.5. To predict the ideal conditions for fruit handling and determine the maturity level, a multilevel perceptron ANN was trained, achieving an accuracy of 96.4%. In addition, an overall precision of 96.6% was achieved when classifying fruit into three maturity categories (under-ripe, ripe, and over-ripe), and the error was 3.4%. The combination of the optoelectronic device and ANN improves considerably this fruit color classification accuracy, can facilitate the determination of the optimal time for consumption, and optimize the postharvest process efficiency.

Distance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of $m\times m$ pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.

Brain–computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain–computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.

Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

La agricultura de precisión es una estrategia clave para enfrentar los efectos del cambio climático, la escasez de agua y la necesidad de aumentar la productividad agrícola de manera sostenible. En este contexto, el desarrollo de sistemas inteligentes ya no es un tema secundario, ya que integra sensores, plataformas IoT, sistemas embebidos y técnicas de automatización para el control preciso de variables climáticas y del suelo en invernaderos y estructuras cubiertas. En este trabajo se realiza una revisión de la literatura de estudios publicados en los últimos cinco años, utilizando el método TAK (título, resumen, palabras clave), abordando la aplicación de estas tecnologías en entornos de agricultura protegida. Se analizan sus usos en el monitoreo de humedad del suelo, control climático, gestión del riego e implementación de cubiertas plásticas. Además, se examinan las arquitecturas de comunicación utilizadas, la eficiencia energética, el costo y la escalabilidad de las soluciones propuestas. Finalmente, se identifican desafíos como la interoperabilidad, la accesibilidad tecnológica para pequeños productores y la necesidad de estandarización, delineando futuras líneas de investigación hacia una agricultura más inteligente, resiliente e inclusiva.