Abstract
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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.

Abstract
Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.

Abstract
In the modern world, making healthy food choices is increasingly important due to the rise in food-related illnesses. Existing tools, such as Nutri-Score and comprehensive food labels, often pose challenges for many consumers. This paper proposes an application that uses Optical Character Recognition (OCR) technologies to read and interpret food labels, thus upgrading current solutions that rely mainly on reading product barcodes. By using advanced optical character recognition and machine learning techniques, the system aims to accurately extract and analyze nutritional information directly from food packaging without relying on a database of pre-registered products. This innovative approach not only increases consumer awareness, but also supports personalized diet management for diseases such as diabetes and hypertension, while promoting healthier eating habits and better health outcomes. Two minimalist functional prototypes were developed as a result of this work: a desktop application and a mobile application.

Abstract
Growing obesity has been a worldwide issue for several years. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. Today, people have a very fast-paced life and sometimes neglect food choices. In order to simplify the interpretation of the Nutri-score labeling this paper proposes a method capable of automatically reading food labels with this format. This method is intended to support users when choosing the products to buy based on the letter identification of the label. For this purpose, a dataset was created, and a prototype mobile application was developed using a deep learning network to recognize the Nutri-score information. Although the final solution is still in progress, the reading module, which includes the proposed method, achieved an encouraging and promising accuracy (above 90%). The upcoming developments of the model include information to the user about the nutritional value of the analyzed product combining it's Nutri-score label and composition.

Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.