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.

Advances in fiber technology have enabled the integration of plastic optical fibers (POFs) with light-emitting diodes (LEDs) for the development of simple, cost-effective, and sensitive sensing systems. The aim of this letter was to develop a low-cost device for measuring adulteration in liquids. In this letter, we present a low-cost optoelectronic system based on a U-shaped POF intrinsic sensor to measure the changes in RGB composition of light transmitted as an evanescent wave through liquid solutions (tequila–water mixtures). The system comprises a 3 W white LED as the light source, a 20-cm-long U-shaped POF, and an RGB photodiode (PD) array that detects changes in the RGB composition of light propagating through the POF. An uncoated 5-mm-long U-shaped POF is in contact with the analyte solution during the measurements. The system is tested using tequila–water mixtures of different concentrations. The RGB PD response is recorded digitally, which improves the efficiency and accuracy of the measurement while reducing the sensitivity of the sensor to external disturbances. The results indicate a system resolution of 0.3 wt% with respect to the tequila concentration, as well as red, green, and blue color sensitivities of 0.1, 0.6, and 0.4 8-bits/wt%, respectively. These findings can contribute to the development of an automated system that efficiently measures adulteration in liquids such as distilled spirits.

This paper proposes a completely decentralized algorithm for multi-robot coordination to explore and map terrain, under constraints on communication range. This algorithm is based on a market bidding process of unrevealed frontiers, where each robot calculates the bids independently. When it reaches its target position, the robot makes a decision by itself, which involves every one of the team members and their ranges of communication, under a decentralized scheme and without the necessity of a central module. Since the approach maintains a more stable communication network among the robots and, consequently, more similar individual maps, the result is an efficient and fault tolerant approach. The bidding function includes the distance of the considered robot towards the frontier, the distances to the others robots and their respective objectives and include the constraint on communication range. This function provides the robots with a complex behavior, so the robot tries to explore distant frontiers, while trying to keep itself within communication range of the other robots.

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.

Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing and feature engineering workflows that could benefit from more comprehensive documentation in research publications. To address this issue, this paper presents a machine learning framework for predicting heart attack risk online. Our systematic methodology integrates a unified pipeline featuring advanced data preprocessing, optimized feature selection, and an exhaustive hyperparameter search using cross-validated grid evaluation. We employ a metamodel ensemble strategy, testing and combining six traditional supervised models along with six stacking and voting ensemble models. The proposed system achieves accuracies ranging from 90.2% to 98.9% on three independent clinical datasets, outperforming current state-of-the-art methods. Additionally, it powers a deployable, lightweight web application for real-time decision support. By merging cutting-edge AI with clinical usability, this work offers a scalable solution for early intervention in cardiovascular care.