Direct interface circuits (DICs) connect resistive, capacitive, and inductive sensors directly to microcontrollers or FPGAs, eliminating analog conditioning stages and offering compact, low-cost, and low-power instrumentation. This systematic review qualitatively synthesizes research up to March 2025 on DIC operation principles, performance metrics, and application domains. Following PRISMA guidelines, 90 studies from IEEE Xplore, ScienceDirect, MDPI, SpringerLink, Scopus, and Google Scholar were selected based on predefined inclusion criteria. Most studies focused on RC-based circuits (53%), followed by RL-based (5%) and charge transfer capacitive interfaces (5%). RC-DICs demonstrated accuracies below 0.01% using adaptive calibration; RL-DICs achieved resolutions of 10–12 bits with higher cycle requirements, while charge transfer interfaces presented systematic errors up to ±5% due to parasitic capacitances. Environmental monitoring, biomedical sensing, liquid-level control, and vehicular detection were frequent application fields. Due to methodological heterogeneity, findings were synthesized qualitatively without quantitative meta-analysis or formal bias assessments. Future research directions include enhanced noise immunity, simplified calibration, and robust parasitic effect compensation.

Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction model in order to help users locate in advance the availability of parking near the places they plan to visit. For this it is proposed a fog computing architecture that integrates a machine learning algorithm based on AdaBoost to predict parking places hours or days in advance. Additionally, a user interface was developed, which involves the collection of user inputs through a mobile application where the user is prompted to enter the destination location and the prediction time interval. Through extensive experimentation using real-world parking flow data, our proposed algorithm demonstrated an improved level of accuracy compared with alternative prediction methods. Moreover, a simulation was conducted to evaluate the system’s latency when using cloud computing versus our hybrid approach combining both fog and cloud computing. The results showed that employing the fog module in conjunction with cloud computing significantly reduced response delay in comparison with using cloud computing alone.

Heart rate (HR) is an essential indicator of health in the human body. It measures the number of times per minute that the heart contracts or beats. An irregular heartbeat can signify a severe health condition, so monitoring heart rate periodically can help prevent heart complications. This paper presents a novel wearable sensing approach for remote HR measurement by a compact resistance-to-microcontroller interface circuit. A heartbeat’s signal can be detected by a Force Sensing Resistor (FSR) attached to the body near large arteries (such as the carotid or radial), which expand their area each time the heart expels blood to the body. Depending on how the sensor interfaces with the subject, the FSR changes its electrical resistance every time a pulse is detected. By placing the FSR in a direct interface circuit, those resistance variations can be measured directly by a microcontroller without using either analog processing stages or an analog-to-digital converter. In this kind of interface, the self-heating of the sensor is avoided, since the FSR does not require any voltage or bias current. The proposed system has a sampling rate of 50 Sa/s, and an effective resolution of 10 bits (200 mΩ), enough for obtaining well-shaped cardiac signals and heart rate estimations in real time by the microcontroller. With this approach, the implementation of wearable systems in health monitoring applications is more feasible.

This paper presents a comprehensive review of Direct Interface Circuits (DICs), which provide a compact, cost-effective, and energy-efficient alternative for interfacing with sensors that exhibit electrical variations such as resistance, capacitance, and inductance. A distinctive characteristic of DICs is their ability to connect sensors directly to digital processors, including microcontrollers (MCUs) or Field-Programmable Gate Arrays (FPGAs), through RC, RL, or capacitive charge transfer configurations, without the need for additional signal conditioning components. The review outlines the fundamental operating principles and key elements of DICs, including time-to-digital conversion, digital processing, and techniques for assessing measurement accuracy, resolution, response time, and the effects of uncertainty and interference. By synthesizing findings from recent literature, this study provides an in-depth understanding of current advancements in DICs and offers a critical analysis of their state-of-the-art implementations. Finally, strategic recommendations are proposed to guide future research directions and application areas.

Este estudio tiene como objetivo analizar los parámetros que afectan la calidad de la energía eléctrica en una industria maquiladora en Ciudad Juárez, Chihuahua, México, identificando las normas vigentes y las consecuencias de problemas en la calidad de la energía. La metodología empleada incluye la recolección de datos mediante un analizador de red Fluke 1750 Power Recorder, seguido de un análisis detallado de factores como voltaje, corriente, frecuencia y distorsión armónica. Los resultados revelan que la calidad de la energía presenta variaciones significativas que pueden impactar la eficiencia operativa de la industria, así como la vida útil de los equipos. Se proponen acciones de mejora basadas en los hallazgos, destacando la importancia de realizar estudios periódicos para garantizar un suministro eléctrico estable. Este trabajo contribuye a la sustentabilidad al promover un uso más eficiente de la energía, reduciendo costos operativos, lo que a su vez minimiza el impacto ambiental de las operaciones industriales. La implementación de las recomendaciones derivadas del estudio no solo optimiza el rendimiento energético, sino que también fomenta prácticas responsables en el uso de recursos, alineándose con los objetivos de desarrollo sostenible.

Implementación de Algoritmos de Procesamiento Digital de Señales en Hardware Paralelo: Artículo de revisión