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Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

Facial Emotion Recognition (FER) is the automatic processing of human emotions by means of facial expression analysis [1]. The most common approach exploits 3D Face Descriptors (3D-FD) [2], which derive from depth maps [3] by using mathematical operators. In recent years, Convolutional Neural Networks (CNNs) have been successfully employed in a wide range of tasks including large-scale image classification systems and to overcome the hurdles in facial expression classification. Based on previous studies, the purpose of the present work is to analyze and compare the abstraction level of 3D face descriptors with abstraction in deep CNNs. Experimental results suggest that 3D face descriptors have an abstraction level comparable with the features extracted in the fourth layer of CNN, the layer of the network having the highest correlations with emotions.

In the field of machine learning, coresets are defined as subsets of the training set that can be used to obtain a good approximation of the behavior that a given algorithm would have on the whole training set. Advantages of using coresets instead of the training set include improving training speed and allowing for a better human understanding of the dataset. Not surprisingly, coreset discovery is an active research line, with several notable contributions in literature. Nevertheless, restricting the search for representative samples to the available data points might impair the final result. In this work, neural networks are used to create sets of virtual data points, named archetypes, with the objective to represent the information contained in a training set, in the same way a coreset does. Starting from a given training set, a hierarchical clustering neural network is trained and the weight vectors of the leaves are used as archetypes on which the classifiers are trained. Experimental results on several benchmarks show that the proposed approach is competitive with traditional coreset discovery techniques, delivering results with higher accuracy, and showing a greater ability to generalize to unseen test data.

Nowadays, green energy is considered as a viable solution to hinder \( CO_{2} \) emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover \( 40\% \) of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management.

In pattern recognition, neural networks can be used not only for the classification task, but also for feature selection and other intermediate steps. This paper addresses the 3D face recognition problem in order to select the most meaningful geometric descriptors. At this aim, the classification results are directly integrated in a biclustering process in order to select the best leaves of a neural hierarchical tree. This tree is created by a novel neural network GH-EXIN. This approach results in a new criterion for the feature selection. This technique is applied to a database of face expressions where both traditional and novel geometric descriptors are used. The results state the importance of the curvedness novel descriptors and only of a few Euclidean distances.