This paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which can be automatically implemented and made available for rapid differentiation between normal pneumonia and COVID-19 starting from X-ray images. The model developed in this work is capable of performing three-class classification, achieving 97.48% accuracy in distinguishing chest X-rays affected by COVID-19 from other pneumonias (bacterial or viral) and from cases defined as normal, i.e., without any obvious pathology. The novelty of our study is represented not only by the quality of the results obtained in terms of accuracy but, above all, by the reduced complexity of the model in terms of parameters and a shorter inference time compared to other models currently found in the literature. The excellent trade-off between the accuracy and computational complexity of our model allows for easy implementation on numerous embedded hardware platforms, such as FPGAs, for the creation of new diagnostic tools to support medical practice.

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse.

Modern neural networks often rely on overparameterized architectures to ensure stability and accuracy, but in many real-world scenarios, large models are unnecessarily expensive to train and deploy. This is especially true in Internet of Things (IoT) and edge computing scenarios, where computational resources and available memory are severely limited. Reducing the size of neural networks without compromising their ability to solve the target task remains a practical challenge, especially when the goal is to simplify the architecture itself, not just the weight space. To address this problem, we introduce ImproveNet, a simple and general method that reduces the size of a neural network, without compromising its ability to solve the original task. The approach does not require any pre-trained model, specific architecture knowledge, or manual tuning. Starting with a standard-sized network and the standard training configuration, ImproveNet verifies the model's performance during training. Once the performance requirements are met, it reduces the network by resizing feature maps or removing internal layers, thus making it ready for AI-on-the-edge deployment and execution.