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.

In this article a hardware topology meant to compare the velocity performance of both an induction motor and a permanent magnet (PM) AC three-phase motor is presented. A variable reference is tracked by the sensorless vectorcontrolled adjustable speed drive (ASD) that permits, by means of the same type of control, performing the speed control loop of the two motors. The algorithms are programmed on a digital signal processor (DSP) in order to ensure efficient use of energy in the transistor bridge and proper tracking of the reference at low and high speeds. Regarding the torque performance, a laboratory test bed based on a torquemeter and two motors is constructed. The hardware implementation includes the power and the digital stages. A serial communication between the PC and the ASD is accomplished to put into operation in the user front-end because a high speed sample frequency is required for the analysis software that runs in the PC. This interface is used not only as comparing the speed response of the motors signals but also as looking the power quality analysis of each motor measurements based on software. Results are presented to demonstrate the effectiveness of the sensorless scheme.

El fitoplancton es un microorganismo marino que aporta mas del 50 % del oxígeno del planeta y constituye el eslabón primario en las cadenas tróficas marinas. Sin embargo, la actividad antropogénica ha generado el vertido de grandes volúmenes de contaminantes en los océanos, alterando la abundancia y distribución de estos organismos, en un contexto donde aún se desconoce la magnitud de su relación con el cambio climático. Con el objetivo de aportar herramientas para abordar esta problemática, en este trabajo se desarrollo un modelo computacional basado en redes neuronales artificiales para predecir la concentración de biomasa fitoplanctónica (clorofila-a) a partir de variables fisicoquímicas y físicas. El modelo fue entrenado con datos de la plataformaCopernicus Marine Service, correspondientes al Pacifico Tropical Oriental frente a Perú. Los resultados muestran un excelente desempeño, con una precisión del 98.5 %, un coeficiente de determinación (R2) de 0.9994, un error cuadrático medio de 0.0005 µmol/m2 y un error absoluto medio de 0.0100 µmol/m2. Estos resultados confirman la efectividad del modelo propuesto, cumpliendo con el objetivo planteado y ofreciendo una herramienta útil para el monitoreo ambiental marino.

A comprehensive comparative study is presented of three approaches for color morphological processing: the proposed bitmixing transformation, lexicographic ordering, and conventional marginal processing. The novel bitmixing method converts RGB channels into a single 24-bit scalar representation through bit-interleaving, preserving both color information and spatial ordering relationships. This enables the direct application of grayscale morphological operators while generally preserving structural, chromatic, and visual relationships in processed objects. Under appropriate conditions—such as when objects exhibit clear color-based boundaries and bilateral symmetry—the method tends to maintain these symmetries more consistently than marginal or lexicographic alternatives. Experimental evaluation shows that the bitmixing approach achieves a competitive balance between color preservation and computational efficiency. In specific scenarios involving color-defined regions and symmetric structures, it demonstrates modest advantages over marginal and lexicographic methods. These findings suggest that the proposed method can serve as a viable alternative in applications where color fidelity, structural coherence, and symmetry preservation are desirable, though its benefits are context-dependent.

A composition of the viscous opening and the lower leveling is introduced to extract brain in magnetic resonance imaging T1. The innovative transformation disconnects chained components and has better control on the reconstruction process of the marker inside of the original image. However, the sequential operator requires setting several parameters, making its application difficult. Due to this situation, a simplification is carried out on it to obtain a more practical method. The proposed morphological transformations were tested with the Internet Brain Segmentation Repository (IBSR) database, which is used as a benchmark among the community. The results are compared using the Jaccard and Dice indices with respect to (i) manual segmentations obtained from the IBSR, (ii) mean indices reported in the current literature, and (iii) segmentations obtained from the Brain Extraction Tool, since this is one of the most popular algorithms used for brain segmentation. The average indices of Jaccard and Dice indicate that the reduced transformation produces similar results to the other methods reported in the literature while the sequential operator presents a better performance.