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Abstract The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Resumen El objetivo de este artículo es presentar el desarrollo de un sistema de medición en tiempo real de glucosa en medios acuosos. El sistema que se implementa incorpora dos lineas de investigación: i) diseño, síntesis e implementación de un sensor electroquímico no enzimático de Nanotubos de Carbono de Pared Múltiple con nanopartículas de Cobre (NTCPM-Cu) y ii) diseño e implementación de un algoritmo de aprendizaje automático basado en una Red Neuronal Perceptrón Multicapa (RN-PM), embebido en un ESP32 SoC (Sistema en Chip). Un dato de corriente que se extrae en tiempo real durante el proceso de oxidación-reducción a la que se somete un medio acuoso, alimenta el algoritmo embebido en el ESP32 para estimar el valor de glucosa. De los resultados experimentales se demuestra que el sensor nanoestructurado mejora la resolución en la respuesta amperométrica al identificar un lugar ideal para la toma de datos. Por su parte, la incorporación del algoritmo basado en una RN embebido en SoC otorga un nivel de 97.8 % de exactitud en la mediciones. Se concluye que incorporar algoritmos de aprendizaje automático embebidos en SoC de bajo costo en procesos experimentales complejos, mejora la manipulación de datos, incrementa la confiabilidad en resultados y adiciona portabilidad.

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Abstract In the post-pandemic era, it is critical to monitor and transmit biomedical signals, specifically ECG. This study aims to develop a platform that enables signal acquisition, adaptation, and transmission using different n-QAM modulation schemes. The system comprises an acquisition stage implemented in the 2.5 GHz band employing the Olimex module and electrodes equipped with an Ag/AgCl type sensor. To effectively manage appropriate bandwidths during implementation of the various n-QAM modulation schemes, an adaptive algorithm was developed and applied to the system. The power amplifier was operated in the linear region to enhance the crest factor and achieve an ACPR close to 30 dBc, demonstrating an appropriate demodulation of the electrocardiogram (ECG) signal, it is feasible to shift to modulation schemes above 64-QAM in order to detect high frequencies and perform a subsequent Fourier analysis. As a telemedicine proposal, the developed system offers flexibility in signal acquisition, data storage, and digitalization, in addition to a multivariable n-QAM scheme; the hardware implementation ensures n-QAM scheme compatibility. For the purpose of contributing to telemedicine via RF transmission, the system was executed on an AD9361 transceiver, which removes the requirement for a traditional signal vector generator and enables optimal control of the tones to be transmitted.

Resumen El monitoreo y transmisión de señales biomédicas, particularmente ECG, es fundamental en la era pospandemia, este trabajo de investigación se centra en el desarrollo de una plataforma para la adquisición, adaptación y transmisión de señales bajo diversos esquemas de modulación n-QAM. El sistema incluye una etapa de adquisición mediante el módulo Olimex y electrodos con un sensor tipo Ag/AgCl. Se desarrolló un algoritmo adaptativo a los diversos esquemas de modulación n-QAM para la gestión de anchos de banda apropiados durante una implementación en la banda de 2,5 GHz, al amplificador de potencia se operó en la región lineal para mejorar el factor de cresta y obtener un ACPR cercano a 30 dBc, se realizó una demodulación adecuada de la señal ECG y es posible migrar a esquemas de modulación superiores a 64-QAM si se requiere detectar altas frecuencias y un posterior análisis de Fourier. El sistema desarrollado como propuesta de Telemedicina brinda versatilidad para la adquisición de señales, digitalización, almacenamiento de datos y un esquema multivariable n-QAM, la implementación en hardware proporcionó una adecuada adaptabilidad para esquemas n-QAM. El sistema se implementó sobre un transceptor AD9361 que elimina el tradicional generador vectorial de señal y permite un control óptimo de los tonos a enviar, para aporte en el área de Telemedicina a través de transmisión RF.

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The design of a remotely operated vehicle (ROV) with a size of 18.41 cm × 29.50 cm × 33.50 cm, and a weight of 15.64 kg, is introduced herein. The main goal is to capture underwater video by remote control communication in real time via Ethernet protocol. The ROV moves under the six brushless motors governed through a smart PID controller (Proportional + Integral + Derivative) and by using pulse-wide modulation with short pulses of 1 µs to improve the stability of the position in relation to the translational, ascent or descent, and rotational movements on three axes to capture images of 800 × 640 pixels on a video graphic array standard. The motion control, 3D position, temperature sensing, and video capture are performed at the same time, exploiting the four cores of the Raspberry Pi 3, using the threading library for parallel computing. In such a way, experimental results show that the video capture stage can process up to 42 frames per second on a Raspberry Pi 3. The remote control of the ROV is executed under a graphical user interface developed in Python, which is suitable for different operating systems, such as GNU/Linux, Windows, Android, and OS X. The proposed ROV can reach up to 100 m underwater, thus solving the issue of divers who can only reach 30 m depth. In addition, the proposed ROV can be useful in underwater applications such as surveillance, operations, maintenance, and measurement.

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Profiling and parallel computing techniques in a cluster of six embedded systems with multiprocessors are introduced herein to implement a chaotic cryptosystem for digital color images. The proposed encryption method is based on stream encryption using a pseudo-random number generator with high-precision arithmetic and data processing in parallel with collective communication. The profiling and parallel computing techniques allow discovery of the optimal number of processors that are necessary to improve the efficiency of the cryptosystem. That is, the processing speed improves the time for generating chaotic sequences and execution of the encryption algorithm. In addition, the high numerical precision reduces the digital degradation in a chaotic system and increases the security levels of the cryptosystem. The security analysis confirms that the proposed cryptosystem is secure and robust against different attacks that have been widely reported in the literature. Accordingly, we highlight that the proposed encryption method is potentially feasible to be implemented in practical applications, such as modern telecommunication devices employing multiprocessors, e.g., smart phones, tablets, and in any embedded system with multi-core hardware.