Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

This article presents a new solution to segment and quantify the microstructures from images of nodular, grey, and malleable cast irons, based on an artificial neural network. The neural network topology used is the multilayer perception, and the algorithm chosen for its training was the backpropagation. This solution was applied to 60 samples of cast iron images and results were very similar to the ones obtained by visual human tests. This was better than the information obtained from a commercial system that is very popular in this area. In fact, this solution segmented the images of microstructures materials more efficiently. Thus, we can conclude that it is a valid and adequate option for researchers, engineers, specialists, and professionals from materials science field to realise a microstructure analysis from images faster and automatically.

This paper compiles and analyzes some of the most current works related to upper limb prosthesis with emphasis on man-machine interfaces. A brief introduction of the basic subjects is given to explain what a prosthesis is, what types of prostheses exist, what they serve for, how they communicate with the user (control and feedback), and what technologies are involved. The method used in this review is also discussed, as well as the cataloging process and analysis of articles for the composition of this review. Each article is analyzed individually and its results are presented in a succinct way, in order to facilitate future research and serve as a source for professionals related to the area of prosthesis, such as doctors, engineers, researchers, and anyone interested in this subject. Finally, the needs and difficulties of the current prostheses, as well as the negative and positive points in the results are analyzed, and the progress achieved so far is discussed.

In this paper, mobile robotics and present tools used in localization, mapping, and navigation of a mobile robot are discussed. The main purpose of this paper is, given a map represented by the incidence matrix of a petri net (PN), to evaluate the use of radio-frequency identification (RFID) technology to recognize the position of a robot in this map as well as the use of the PN dynamics as the cognition system of this robot. Thus, cards with RFID technology were placed at each intersection of structured environment (labyrinth) ways. A robot equipped with an RFID reader at its bottom moves until it passes over these cards. When this happens, the vehicle performs actions, such as turning right or left according to the map defined in its algorithm. Once the above-mentioned actions are performed, it goes straight to the next card. To ensure that this happens, there is a black line connecting each card to its neighbor cards. The robot is equipped with three infrared sensors, so it can detect and follow these lines. The results show that the robot can get out of one RFID card and reach the next one since they are connected by a black line. Without these lines, due to the limitations in the structure of the robot, it loses its way and cannot return. Still, the robot is able to execute the necessary navigation movements, in the case of stopping, moving forward, and turning right and left. These movements are correctly coordinated by the PN dynamics. The robot knows which card it is on and goes to the next card according to the previously established map. Each path of the robot is mathematically modeled by the incidence matrix of a petri net. Therefore, it managed to reach the destination in each of the four proposed paths. The PN that represents path 1 has the same number of places and transitions and has curves only to the left. On path 2, a right turn was added. In path 3, besides the two-way curves, RP has more places than transitions. Lastly, in path 4, there is a crossing in the path, that is, the robot goes through the same card twice, making the right and different decisions in each case according to the respective maps. Experimental results show that this approach has feasibility and effectiveness.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain’s electrical activity through electrodes placed on the scalp’s surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was “subject-dependent”. In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.