Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS.

Currently there is a huge amount of freely available hydrographic data, and it is increasingly important to have easy access to it and to be provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data go through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd–even algorithm; this allows analysis of a study area, regardless of size, shape or location. The aim is to offer two filtering methods and to discard only the real-time quality control data that present salinity drifts. This takes advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter that uses cluster analysis and 30 % in the case of the second, which discards profilers with salinity drifts, of the total real-time quality control data that are usually discarded by the users due to problems such as salinity drifts. This allows users to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, rather than waiting for the delayed-mode quality control that takes up to 12 months to be completed. This methodology has been tested for its replicability in five selected areas around the world and has obtained good results.

Las cámaras de seguridad y los sistemas de videovigilancia se han convertido en infraestructuras sumamente importantes para garantizar la seguridad de los ciudadanos. Sin embargo, el problema de inseguridad sigue en crecimiento debido en gran parte al fácil acceso a armas de fuego tipo pistola, la detección temprana de este tipo de armas es de suma importancia para ayudar a prevenir accidentes. El objetivo de este trabajo es implementar un sistema de visión estereoscópica que sea capaz de detectar en tiempo real este tipo de objetos con alta confianza ademas de lograr definir la distancia a la que se encuentra dicho objeto. Para dicha detección se implementó una arquitectura de redes neuronales convolucionales (CNN) con un algoritmo tipo YOLO, empleando aprendizaje transferido, junto con un algoritmo para la estimación estereoscópica de la distancia. El sistema presentado funciona con un valor de Intersección sobre unión (IoU) de 0.6 en el cual el se logró una precisión de 92.2 % además, el error promedio detectado en la estimación de distancia hasta un máximo de 3 metros es de 9.3 centimetros.

En este trabajo se presenta el control basado en datos mediante redes neuronales artificiales de un motor de CD. Los principales componentes del esquema de control propuesto son el modelo numérico de la planta que incluye como entrada el voltaje suministrado por la fuente y un modelo inverso, que calcula la señal de control, implementados mediante Redes Neuronales Artificiales (RNA). La novedad del presente método basado en datos con RNA es la incorporación del voltaje en el vector de estados del modelo, ya que interesa por su importancia práctica al modelar el motor de CD. Para validar el esquema propuesto, se presentan resultados experimentales sobre un motor de CD comercial ante perturbaciones variantes en el tiempo.

Currently there is a huge amount of freely available hydrographic data and it is increasingly important to have access to it efficiently and easily provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data goes through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd-even algorithm, this allows analysis of a study area, regardless of size, shape or location. Also, gives two filtering methods to discard only the real time quality control data that present salinity drifts, thus taking advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter and 30 % in the case of the second of the total real time quality control data that are usually discarded due to problems such as salinity drifts, this allows researchers to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, unlike waiting for the delayed mode quality control that takes up to 12 months to be completed.

Currently there is a huge amount of freely available hydrographic data and it is increasingly important to have access to it efficiently and easily provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data goes through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd-even algorithm, this allows analysis of a study area, regardless of size, shape or location. Also, gives two filtering methods to discard only the real time quality control data that present salinity drifts, thus taking advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter and 30 % in the case of the second of the total real time quality control data that are usually discarded due to problems such as salinity drifts, this allows researchers to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, unlike waiting for the delayed mode quality control that takes up to 12 months to be completed.