Abstract

Abstract
Myocardial infarction, known as heart attack, is one of the leading causes of world death. It occurs when blood heart flow is interrupted by part of coronary artery occlusion, causing the ischemic episode to last longer, creating a change in the patient’s ECG. In this work, a method was developed for predicting patients with MI through Frank 3-lead ECG extracted from Physionet’s PTB ECG Diagnostic Database and using instantaneous frequency and spectral entropy to extract features. Two neural networks were applied: Long Short-Term Memory and Bi-Long Short-Term Memory, obtaining a better result with the first one, with an accuracy of 78%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Ventricular tachyarrhythmia (VTA), mainly ventricular tachycardia (VT) and ventricular fibrillation (VF) are the major causes of sudden cardiac death in the world. This work uses deep learning, more precisely, LSTM and biLSTM networks to predict VTA events. The Spontaneous Ventricular Tachyarrhythmia Database from PhysioNET was chosen, which contains 78 patients, 135 VTA signals, and 135 control rhythms. After the pre-processing of these signals and feature extraction, the classifiers were able to predict whether a patient was going to suffer a VTA event or not. A better result using a biLSTM was obtained, with a 5-fold-cross-validation, reaching an accuracy of 96.30%, 94.07% of precision, 98.45% of sensibility, and 96.17% of F1-Score. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The Small variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.

Abstract
The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity. Antenna defects, ranging from manufacturing imperfections to environmental wear, pose significant challenges to the reliability and performance of communication systems. This review paper navigates the landscape of antenna defect detection, emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection. This review paper serves as a valuable resource for researchers, engineers, and practitioners engaged in the design and maintenance of communication systems. The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures. In this study, a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented. The PRISMA principles will be followed throughout the review, and its goals are to provide a summary of recent research, identify relevant computer vision techniques, and evaluate how effective these techniques are in discovering defects during inspections. It contains articles from scholarly journals as well as papers presented at conferences up until June 2023. This research utilized search phrases that were relevant, and papers were chosen based on whether or not they met certain inclusion and exclusion criteria. In this study, several different computer vision approaches, such as feature extraction and defect classification, are broken down and analyzed. Additionally, their applicability and performance are discussed. The review highlights the significance of utilizing a wide variety of datasets and measurement criteria. The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation, such as real-time inspection systems and multispectral imaging. This review, on its whole, offers a complete study of computer vision approaches for quality control in antenna parts. It does so by providing helpful insights and drawing attention to areas that require additional exploration.